Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition

Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatio-temporal graphs. Most GCN-based action recognition methods use deep feed-forward networks with high computational complexity to process all skeletons in an action. This leads to a high number of floating point operations (ranging from 16G to 100G FLOPs) to process a single sample, making their adoption in restricted computation application scenarios infeasible. In this paper, we propose a temporal attention module (TAM) for increasing the efficiency in skeleton-based action recognition by selecting the most informative skeletons of an action at the early layers of the network. We incorporate the TAM in a light-weight GCN topology to further reduce the overall number of computations. Experimental results on two benchmark datasets show that the proposed method outperforms with a large margin the baseline GCN-based method while having 2.9 times less number of computations. Moreover, it performs on par with the state-of-the-art with up to 9.6 times less number of computations.

I. INTRODUCTION
Human action recognition has been a very popular research topic in recent years. RGB videos and different types of modalities such as depth, optical flow and human body skeletons can be used for this task [1], [2], [3]. Compared to other data modalities, human body skeletons encode compact and high level information representing the human pose and joints' motion while being invariant to viewpoint variations, motion speed, human appearance, and body scale [4], and it is robust to context noise. Considering the success of existing pose estimation techniques [5], [6], [7] and the availability of depth cameras [8], obtaining the human body skeleton data is much easier than before. Thus, skeleton-based human action recognition has attracted an increasing research interest in recent years and many deep learning-based methods were proposed to model both the spatial and temporal evolution of skeletons in a sequence. Some methods use recurrent neural network (RNN) architectures, like the Long Short-Term Memory (LSTM) network [9], which are suitable to model temporal dynamics [1], [2], [10], [11], [12], [13] and utilize the skeleton data as vectors formed by the body joints coordinates. Methods employing Convolutional Neural Networks (CNN) [14], [15], [16], [17], [18], [19] reorganize the body joints' coordinates of each pose to a 2D map which is a suitable input format for CNNs. The high model complexity in all these methods make their training and inference processes very time consuming [16], [20]. Besides, these methods are not able to completely benefit from the non-euclidean structure of the skeleton data which represents the spatial body joints connections.
Graph Convolutional Networks (GCNs) have been very successful when applied to many pattern recognition tasks [21], [22], [23], [24], [25], [26], [27], [28], by generalizing the convolution operation from grid data into graph data structures. Recently, significant results have been obtained by employing GCNs for skeleton-based human action recognition [29], [30], [31], [32], [33], [34], [35]. In these methods, an action is represented as a sequence of body poses and each body pose is represented by a skeleton. The skeleton data is treated as a graph which models the spatial relationship between different body joints and the temporal dynamics in an action are expressed by a sequence of skeletons.
Most of the recently proposed GCN-based methods use deep feed forward networks to model the spatio-temporal features of body skeletons. Considering that these methods process all the body sekeletons in a sequence depicting the performed action, this approach is not efficient in terms of memory consumption and computation time. While memory efficiency can be increased by employing network compression, weight prunning, quantization and low-rank approximation approaches [36], [37], [38], [39], by processing a large number of body skeletons in each sequence, the number of floating point operations (FLOPs) is still large. Thus, to address both memory and computational efficiency in skeleton-based human action recognition, not only we need more compact and lightweight network architectures, but also the number of FLOPs should be minimized. Hence, processing fewer body skeletons for action recognition is a large step towards increasing the computational efficiency of both training and inference processes.
In this paper, we argue that all body skeletons in a temporal sequence are not equally important for recognizing actions. For each action class, there exist body poses which are the most informative for the action, and we can extract sufficient information for action recognition by focusing on the skeletons of these body poses only. Our goal is to increase computational efficiency while performing on par, or even better, compared to models which utilize all the body skeletons in a sequence for action recognition. In this regard, we propose a GCN-based model which is capable to select a subset of body skeletons for human action recognition. To select the most informative skeletons, we propose a trainable temporal attention module which measures the importance of each skeleton in a sequence and we employ this module in the GCN-based spatio-temporal model to increase its efficiency. The main contributions of our work are the following: • We propose a temporal attention module (TAM) to extract the most informative skeletons in an action sequence, leading to increased computational efficiency in both the training phase and inference. • We experimentally show that employing our proposed TAM with a light-weight GCN topology leads to state-ofthe-art performance levels in two widely adopted datasets for skeleton-based action recognition. • We show that a subset of skeletons is as informative as the full skeleton sequence, since our method performs on par with the state-of-the-art methods while increasing computational efficiency by a factor of up to ×9.6. This is a major advantage of our method compared to the stateof-the-art, as it is suitable for real time action recognition under restricted computation scenarios.
The remainder of the paper is organized as follows. Section II discusses the related works and section III introduces the baseline method ST-GCN [30]. Section IV describes the proposed method. The conducted experiments and results are presented in section V, and the concluding remarks are drawn in section VI.

II. RELATED WORK
Data driven skeleton-based human action recognition methods which use deep learning models are mainly categorized into RNN-based, CNN-based and GCN-based methods. RNNbased methods [1], [2], [10], [11], [12], [13] mostly employ LSTM networks [9] to model the temporal dynamics of skeletons' sequence. In these methods, the sequence of skeletons is introduced to the model as a sequence of vectors. Each vector is formed by the concatenated 3D coordinates of all body joints of skeleton. ST-LSTM [2] is one representative RNN-based method which captures the body joints' relationship in both temporal and spatial domain. CNN-based methods [40], [14], [15], [16], [17], [18], [19] convert the sequence of skeletons into pseudo-images by reorganizing the joints' coordinates into a 2D map and employ a state-of-the-art CNN model like ResNet [41], [42] to extract temporal and spatial features. Although the CNN-based methods are easier to train than RNN-based methods, they employ deep network architectures with large receptive fields to perceive the semantics of the input map. Besides, since all these RNN-based and CNNbased methods convert the skeletons into a regular grid or a sequence, they cannot utilize the complex, irregular and non-Euclidean structure of the skeleton data.
Recently, many GCN-based methods for skeleton-based human action recognition have been proposed which treat the human skeleton data as a graph structure representing the body joints (graph nodes) and their natural connections with bones (graph edges). Since the GCNs are able to capture the embedded features in irregular structured data, the GCNbased methods achieve higher performance in skeleton-based human action recognition compared to RNN-based and CNNbased methods. Spatio-temporal graph convolutional network (ST-GCN) [30] is the first GCN-based method proposed for action recognition. It receives the sequence of body skeletons directly as input and employs the GCNs' [21] aggregation rule to extract the spatial features of each skeleton in a sequence, while the temporal dynamics are modeled by using a temporal graph with fixed connections. Several methods have been proposed based on ST-GCN for skeleton based human action recognition which mostly focus on exploiting adaptive spatial graphs. 2s-AGCN [29] is one of the state-of-the-art methods proposed on top of ST-GCN [30]. This method adaptively learns the topology of the graph in different layers for each sequence of skeletons in an end-to-end manner. It updates the graph structure of the skeleton with a spatial attention and also a data dependent graph. Besides, it uses a two-stream framework to utilize both joints features and bones features in parallel. It introduces joints and bones data into two different models and, finally, the SoftMax scores of the two model are added to obtain the fused score and predict the action label. In DGNN [34] the skeleton data is represented as a directed acyclic graph to benefit from the relationship between joints and bones based on their kinematic dependency and it improved 2s-AGCN [29] by utilizing the motion data and also making the graph structure adaptive. GCN-NAS [31] follows a neural architecture search approach to explore an optimal GCN-based model in terms of graph structures. That is, it explores the search space to determine the best graph structure at each layer of the network, while it still employs the ST-GCN network topology. DPRL+GCNN [32] aims to select the most representative skeletons from the input sequence using deep reinforcement learning. It adjusts the chosen skeletons progressively by evaluating the models' performance in action recognition. Actional-Structural GCN (AS-GCN) [33] proposed an inference module based on encoder-decoder structure to capture richer action-specific correlations and also structural links between the joints in skeletons.

III. SPATIAL TEMPORAL GRAPH CONVOLUTIONAL NETWORK (ST-GCN)
In this section, we describe ST-GCN model [30] as our baseline. It is formulated over a sequence of skeleton graphs with multiple layers of spatio-temporal graph convolution operations. The model receives the joint coordinate vectors and the constructed spatio-temporal graph as input and applies GCN to capture the embedded patterns of the graph and extract high level features of the skeletons. Each convolution layer integrates the human body information along both the spatial and temporal dimensions. The extracted features are then passed to a fully connected layer which classifies the skeleton sequence to the action class using a standard SoftMax classifier. The entire model is trained with backpropagation to minimize the classification loss. The network architecture of ST-GCN [30] is constructed by 9 layers of spatio-temporal convolution units. In each layer the Resnet module is also applied and the output is followed by a batch normalization layer. In the following, the graph construction, and the spatial and temporal convolution units are described.

A. Graph Construction
Each skeleton is represented as a graph which forms the hierarchical representation of skeleton sequences and the spatial patterns and temporal dynamics of these graphs are captured automatically. The spatio-temporal graph is constructed using the 2D or 3D human body joint coordinates as nodes, and spatial and temporal edges. The spatial edges correspond to the natural connectivity of body joints in the skeleton, and the temporal edges connect the same body joints across consecutive skeletons. Fig. 1 (right) shows the spatio-temporal skeleton graph.
Formally, an undirected spatio-temporal graph on a sequence of skeletons is denoted as G = (V, E), where the set of nodes: indicates N body joints of a skeleton in a sequence of T time steps and E is the set of spatial (intra-skeleton) and temporal (inter-skeleton) connections. The graph structure is captured by the adjacency matrix A ∈ R N ×N which is a symmetric binary matrix defined as A ij = 1 if there is a connection between nodes ν ti and ν tj in time step t, otherwise A ij = 0. Given A as the spatial graph adjacency matrix which represents the joint connections in a single skeleton, the normalized adjacency matrixÂ with self connections is computed as: The body motions are grouped as concentric and eccentric, the spatially localized structure of a single skeleton is used to design the spatial partitioning process which divides the neighbor nodes of each body joint into three subsets: 1) the root node itself; 2) the nodes connected to the root which are closer to the skeletons' center of gravity than the root node; 3) the remaining roots' neighbors. Fig. 1 (left) shows the neighboring sets of each graph node which are presented with different colors [30], [29].

B. Spatial Graph Convolution
Given the input spatial features of i th joint, f in (ν i ), the spatial graph convolution is defined as [30]: where N i is the set of intra-frame neighbor vertices of ν i which are directly connected to it. As mentioned in III-A, ST-GCN uses a partitioning method to divide the neighbor vertices of each node into three subsets. According to this partitioning process, l i in (2) maps each neighbor vertex into one of these three subsets and w is the weight matrix which is unique for each neighbor set and maps the target nodes' features into a new subspace. Note that the number of neighbor subsets and weight matrices are fixed while the number of neighbor nodes in each subset is varied for each node. Z ij is a normalization factor which balances the contribution of each set of neighbor nodes in producing f out (ν i ) which contains the output features of the target node ν i . In terms of implementation, the C-dimensional input feature vector for the node ν i is denoted as x i = f in (ν i ) and X ∈ R Cin×T ×N represents the input feature tensor for a sequence of skeletons, where C in denotes the number of input channels, T is the number of skeletons and N is the number of body joints in each skeleton. The model receives the feature tensor X as input and updates the nodes' feature vectors by applying the spatial convolution to produce X ∈ R Cout×T ×N with C out channels as output. By employing the layer-wise propagation rule of GCNs proposed in [21], the spatial convolution is formally defined as [30]: where ⊗ is the element-wise product of two matrices. According to the partitioning process described in section III-A, each node has 3 subsets of neighbors. Therefore, the ajdacency matrixÃ is defined as the summation of 3 different adjacency matrices which are indexed by p as follows: A 0 = I N represents the nodes' self connections and the normalized adjacency matrix for each subset is defined as: where D (ii)p = N j A (ij)p + ε, with ε = 0.001 used to avoid empty rows in degree matrix. M p ∈ R N ×N is a learnable attention map which highlights the elements of each adjacency matrix and it is initialized as an all-one matrix. W p ∈ R Cout×Cin denotes the weight matrix which transforms the node features of each partition. In practice, a standard 2D convolution is used which performs C out × 1 × 1 convolutions, then the resulting tensor is multiplied with the masked normalized adjacency matrix on the last dimension N .

C. Temporal Graph Convolution
To capture the temporal dynamics in a skeleton sequence and consider the motions taking place in an action, we need to propagate information related to the nodes' features in other skeletons of the sequence. To this end, a temporal convolution is applied on the output tensor of the spatial convolution step. In temporal dimension, each node is only connected to its corresponding node in its previous and next skeletons, so the number of neighbors for each node is fixed to 2. Therefore, a 2D convolution with a predefined temporal kernel size K t is is applied to the output of (3), X , to aggregate the features of each body joint in different time steps.

IV. TEMPORAL ATTENTION-AUGMENTED GRAPH CONVOLUTIONAL NETWORK (TA-GCN)
In this section we describe an end-to-end temporal attentionaugmented GCN model for efficient skeleton-based human action recognition. In order to extract discriminative features in temporal dimension of data, we propose TAM which highlights the most informative skeletons in a sequence. Based on this process, we apply skeleton selection to highly reduce the overall computational cost both during training and inference.
Similar to most of the recently proposed works [30], [29], the proposed model is composed of multiple layers of spatiotemporal units. In each unit, the embedded spatial features in each skeleton are first extracted by employing a standard 2D convolution as described in Eq. (3). Motivated by [29], the learnable spatial attention map M p is added (and not elementwise multiplied) to the graph adjacency matrix A p . Adding the attention map to the graph adjacency matrix has the advantage that, not only the strength of the existing graph edges can be highlighted, but relationships between graph nodes which are not connected in the adjacency matrix based on the physical connectivity of the human body skeleton joints can also be captured [29]. Such connections can be important in effectively describing actions, e.g. encoding the relationships between the joints of the human arms and the head is particularly important for describing actions like 'hand waving'. Thus, the spatial attention module is employed to selectively focus on the most informative joints in each skeleton and the spatial convolution in Eq. (3) takes the following form: The TAM takes a tensor H (l) ∈ R C l ×T ×N as input, which can be the input data, i.e. H (0) = X, or the output of the l th hidden layer of the network. First, two average pooling operations in both feature and spatial dimensions is performed to produce the h (l) ∈ R 1×T ×1 , which denotes the average feature value of each skeleton in the sequence. Then, this tensor is introduced to a fully connected layer which transforms features in temporal dimension T to produce the attention tensor a ∈ R 1×T ×1 = {a 0 , a 1 , ..., a T } as follows: where Θ ∈ R T ×T denotes the learnable transformation matrix, and the resulted attention tensor a indicates the importance of each skeleton in the sequence.
To highlight the most informative skeletons, we create the attention tensor Λ ∈ R C l ×T ×N , a duplicated version of attention map a with C l × N copies, which is subsequently dot multiplied to H (l) as follows: where ⊗ denotes element-wise multiplication. To select a subset of T skeletons fromĤ (l) , the values in the attention map a are sorted in descending order and the skeletons corresponding to the T highest attention values are selected to be introduced into next layers of the network. To improve the computational efficiency of model in both training and inference phases, it would be preferable to use the TAM in the early layers of the network so that the next layers will process less number of skeletons. The proposed model is composed of 6 GCN layers and 1 TAM. The first two GCN layers, map data into 64 dimensional feature space using spatial convolution followed by batch normalization layer and element-wise ReLU activation function. The mapped data is then introduced into the TAM which selects a subset of skeletons. In order to find the most discriminative skeletons using the TAM, the temporal convolution operation which smoothens the features in temporal domain is not used in the first two GCN layers. The 3 rd and 4 th layers change data dimensions from 64 to 128 and the last two GCN layers increase the number of channels to 256. In the last 4 GCN layers, both spatial and temporal convolutions are applied and each operation is followed by batch normalization and element-wise ReLU activation function. All the GCN layers except the first two layers utilize the ResNet [42] module to benefit from the input skeleton data too. The strides of the temporal convolution layers in 3 rd and the 5 th GCN layers are set to 2 as pooling layer. At the end, the refined spatio-temporal features are introduced into a global average pooling layer which produces an output feature vector of size 256 × 1 for each sequence of skeletons and it is introduced into a fully connected layer which is equipped by a SoftMax classifier to classify the action. The model is trained with backpropagation in an end-to-end manner to minimize the classification error while it is learning to select the most discriminative skeletons.
Motivated by the method in [29], which utilizes both joints and bones features to enhance the classification performance, we also explore the skeleton bones' length and direction as the second-order information. Each bone is represented as a 3D vector bounded with two joints. The source joint is the one that is closer to the skeletons' center of gravity than the target joint. Therefore, each bone pointing from its source joint to the target joint holds both the length and direction information between two joints.
After extracting the bone features from the skeleton data, the two feature tensors are concatenated on their first dimension and the merged joint-bone tensor of size 2C in × T × N is introduced to the model as input. The overall architecture of the proposed method is shown in Fig. 2.

V. EXPERIMENTS
In this section, we describe experiments evaluating the performance of the proposed TA-GCN model in skeleton based human action recognition. We conducted experiments on two widely adopted datasets for evaluating the performance of skeleton-based action recognition methods. These datasets are: 1) The NTU-RGB+D [10]: is the largest multi-modality indoor-captured action recognition dataset. The dataset includes RGB videos, infrared videos, 3D skeletons and depth sequences. The 3D skeleton data is captured by the Microsoft Kinect-v2 camera and is used in our experiments. This dataset consists of 56, 880 video clips from 60 different human action classes which are captured from three different views. Each skeleton is represented by 25 joints which are featured by 3D coordinate values. In our experiments, we follow exactly the same data splits and benchmark evaluations as in [10]. In Cross-View (CV) benchmark, the training set contains 37, 920 samples captured from cameras two and three and the test set contains 18, 960 samples captured from the first camera. In Cross-Subject (CS) evaluation, 40, 320 videos which represent 20 different action classes are used for training and the remaining 16, 560 videos are used for testing. The number of frames for each sample is 300 and for the samples which have less than 300 frames, the frame sequence is repeated until it reaches 300 frames. In practice, the input data is a tensor of size (3 × 300 × 25).
2) The Kinetics-Skeleton [43]: is a very large action recognition dataset that contains 300, 000 video clips of 400 different human actions collected from YouTube. The 2D joints' coordinates (x, y) on every frame and their confidence score c are estimated using the public OpenPose toolbox [7]. Each skeleton in this dataset contains 18 body joints and each body joint is represented by a 3D vector (x, y, c). The number of frames for each sample is fixed to 300 in a similar way as explained for NTU-RGB+D dataset and the input data would be a tensor of size (3×300×18). We use the Kinetics skeleton data which is provided by [30]. The training and validation sets contain 240, 000 and 20, 000 skeleton sequences, respectively.

A. Experimental setting
All experiments were conducted on PyTorch deep learning framework [44] with 4 GRX 1080-ti GPUs and batch size of 32 and 128 for NTU-RGB+D and Kinetics datasets, respectively. The SGD optimizer is employed to optimize the model with Cross-entropy loss function through back-propagation with weight decay set to 0.0001. We followed exactly the same setting explained by authors for the baseline method [30] and the state-of-the-art [29]. In more details, the learning rate is not fixed through all epochs. For NTU-RGB+D dataset, it starts with 0.1 and it is divided by 10 at epochs 30, 40 while the total number of training epochs is fixed to 50. For Kinetics dataset, it starts with 0.1 and it is divided by 10 at epochs 45, 55 and the total number of training epochs is fixed to 65. For the Kinetics-Skeleton dataset we don't perform data-augmantation method which is used in [30]. In more details, we utilize all the 300 skeletons in a sequence.  Fig. 3 shows the obtained performance in terms of classification accuracy on Kinetics-Skeletons dataset and NTU-RGB+D dataset with both CV and CS benchmarks. As can be seen in Fig. 3, by increasing the number of skeletons, the models' accuracy is increased gradually. For NTU-RGB+D dataset, the best performance achieved by our method is equal to 94.2% and 87.97% for CV and CS benchmarks, respectively. This performance is achieved by selecting 150 most informative skeletons and increasing the number of skeletons doesn't improve the performance necessarily. This confirms our hypothesis that the model does not need to process all available skeletons to perform action classification. Since the Kinetics-Skeleton dataset is more challenging than NTU-RGB+D, both top1 and top5 accuracies are reported. The best top1 and top5 accuracies are 34.95% ad 57.28%, respectively, which are achieved by selecting 250 skeletons. Overall, we can observe that by applying the proposed TAM for skeleton selection we can achieve competitive performance even for a small number of selected skeletons. For example, the performance in NTU-RGB-D (CV) for T = 50 is equal to 92.44%, compared to 93.83% corresponding to using all T = 300 skeletons. This is an advantage for application scenarios with computational restrictions.

C. Comparison with the state-of-the-art methods
We compare the performance of the proposed method with the of state-of-the-art methods in Tables. I and II on the NTU-RGB+D and Kinetics-Skeletons datasets, respectively. Table I is divided in three blocks grouping the methods in three categories, i.e. RNN-based, CNN-based and GCN-based methods, respectively. As can be seen, CNN-based methods perform better than RNN-based methods in general, while the state-of-the-art performance is achieved by GCN-based methods.  I  COMPARISONS OF THE CLASSIFICATION ACCURACY WITH  STATE-OF-THE-ART METHODS ON THE TEST SET OF NTU-RGB+D   DATASET   Method CS(%) CV(%) #Streams Skel.sel. HBRNN [1] 59.1 64.0 5 Deep LSTM [10] 60.7 67.3 1 ST-LSTM [2] 69.2 77.7 1 STA-LSTM [11] 73.4 81.2 1 VA-LSTM [12] 79.2 87.7 1 ARRN-LSTM [13] 80.7 88.8 2 Two-Stream 3DCNN [14] 66.8 72.6 2 TCN [15] 74.3 83.1 1 Clips+CNN+MTLN [16] 79.6 84.8 1 Synthesized CNN [17] 80.0 87.2 1 3scale ResNet152 [18] 85 The results for the NTU-RGB+D dataset in Table I show that the proposed method, TA-GCN, outperforms all the CNNbased and RNN-based methods with a large margin. Besides, TA-GCN outperforms ST-GCN, which is the baseline in GCNbased methods, and DPRL+GCNN by a large margin in both CV and CS benchmarks. Compared to AS-GCN, the proposed method achieves higher accuracy in CS benchmark and a similar performance in CV benchmark. Here we should also note that the only competing GCN-based method that performs skeleton selection, i.e. DPRL+GCNN [32], performs poorly compared to all variants of the proposed method. The TA-GCNs' competitive methods are 2s-AGCN and GCN-NAS and the best performing method is DGNN. For Kinetics-Skeleton dataset (Table II), the proposed method outperforms the RNN-based methods, ST-GCN and AS-GCN methods and it has competitive performance with 2s-AGCN. The best performing methods on this dataset are GCN-NAS and DGNN. When the proposed model is trained using two different data streams joints and bones, (2s-TA-GCN), it achieves similar top1 accuracy with 2s-AGCN and its top5 accuracy exceeds  2s-AGCN slightly. The 4s-TA-GCN which is trained using four data streams outperforms DGNN (top5) and achieves competitive performance with GCN-NAS while it has 4.8 times less computational complexity. Table. III shows the computational complexity comparison in terms of FLOPs and model parameters (Params) between the GCN-based competing methods on NTU-RGB+D (CV) dataset. Since the source code of the DPRL-GCNN method is not provided by the authors, it is not mentioned in the Table. III. Fig. 4, illustrates the computational complexity (FLOPs) of our method, when it selects different number of skeletons, and also the state-of-theart methods which process all the skeletons. It can be seen that TA-GCN has less computational complexity compared to all the competing methods, even when it process all the skeletons in the sequence (i.e. for T = 300). Tables I and II indicate the number of network streams used by each GCN-based method and whether each method performs skeleton selection (or otherwise employs all T = 300 skeletons). As can be seen, methods employing more than one streams generally outperform those with one stream. Although these methods achieve higher classification accuracy compared to single-stream methods, they have high computational complexity. Similar to our proposed method, the ST-GCN and DPRL-GCNN methods train only one network stream. They utilize only the joint data to train the model and we fuse both joint and bone information in the first layer (input) which prevents increasing number of model parameters and FLOPs. The results show that not only does our method outperform the baseline ST-GCN in terms of classification accuracy, but also it has 2.9 times less FLOPs and 1.3 times less number of parameters. Therefore, TA-GCN can be a strong and efficient baseline for GCN-based human action recognition which achieves better performance than ST-GCN. The 2s-AGCN, GCN-NAS methods are built on top of ST-GCN method and train two networks using joint and bone data separately which doubles the number of model parameters and FLOPs. DGNN which has the best classification accuracy on both datasets trains 4 different networks with joint, bone, jointmotion and bone-motion data. DGNN and GCN-NAS have 12.6 and 19.3 times more FLOPs than the proposed method, respectively, and from 3.6 to 8.9 times more parameters.

Columns 3 and 4 in
To conduct a fair comparison, we also trained TA-GCN method with 4 different data streams separately. In Table. I, II, 2s-TA-GCN shows the ensembled softmax scores of the trained models with joint and bone data and 4s-TA-GCN shows the classification accuracy of the model when the softmax scores of all the 4 streams, joint, bone, jointmotion and bone-motion, are ensembled. The results indicate that training multiple models and ensembling the softmax scores can increase the accuracy by at least 1%. 2s-TA-GCN has a similar accuracy with 2s-AGCN while it has 3.3 times less FLOPs and 1.5 times less parameters. 4s-TA-GCN outperforms the competing methods 2s-AGCN and GCN-NAS with 1.65, 4.82 times less FLOPs, respectively. As can be seen in Table. III, GCN-NAS has the maximum number of FLOPs and it has competitive performance with 2s-TA-GCN which has with 9.6 times less computational complexity. In comparison with state-of-the-art, our proposed TA-GCN achieves good performance with much less computational complexity which makes TA-GCN applicable for many practical tasks with limited computational capacity.

VI. CONCLUSION
In this paper, we proposed a temporal attention-augmented GCN to improve the computational efficiency in skeletonbased action recognition. Our method trains the attention mechanism to select the most informative skeletons for each action in an end-to-end manner. On two widely used benchmark datasets, the proposed method has competitive performance with the state-of-the-art, while being up to 10 times less computationally complex, and it outperforms the baseline in terms of both classification accuracy and computational complexity with a large margin. Therefore, it can be an efficient and strong baseline for skeleton-based action recognition.

ACKNOWLEDGMENT
This work received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors' views only. The European Commission is not responsible for any use that may be made of the information it contains.


Electrotactile feedback for hand interactions: A systematic review, meta-analysis, and future directions

Haptic feedback is critical in a broad range of human-machine/computer-interaction applications. However, the high cost and low portability/wearability of haptic devices remains an unresolved issue, severely limiting the adoption of this otherwise promising technology. Electrotactile interfaces have the advantage of being more portable and wearable due to its reduced actuators’ size, as well as benefiting from lower power consumption and manufacturing cost. The usages of electrotactile feedback have been explored in human-computer interaction and human-machine-interaction for facilitating hand-based interactions in applications such as prosthetics, virtual reality, robotic teleoperation, surface haptics, portable devices, and rehabilitation. This paper presents a systematic review and meta-analysis of electrotactile feedback systems for hand-based interactions in the last decade. We categorize the different electrotactile systems according to their type of stimulation and implementation/application. We also present and discuss a quantitative congregation of the findings, so as to offer a high-level overview into the state-of-art and suggest future directions. Electrotactile feedback was successful in rendering and/or augmenting most tactile sensations, eliciting perceptual processes, and improving performance in many scenarios, especially in those where the wearability/portability of the system is important. However, knowledge gaps, technical drawbacks, and methodological limitations were detected, which should be addressed in future studies.

INTRODUCTION
T HE afferent nerves provide the central nervous system with various sensory information, which is essential for perceiving the spatiotemporal continuum (i.e., the world) that surround us. The glabrous skin of the human hand is innervated by 12 diverse types of afferent fibers, which are responsible for perceiving pain, thermal, kinesthetic, and tactile (e.g., form, texture, skin's motion, motion of exogenous objects, pressure) sensations [1]. This haptic information is considered crucial for facilitating an effective humanmachine/computer interaction [2], [3], [4], [5], [6], [7]. Indeed, haptic information is needed in human-machine/computer-interaction systems where the user may operate virtual or robotic hands, or simply interact with virtual environments or physical objects in distance, hence, in a wide range of application scenarios,to enrich interaction fidelity of user's awareness, haptic information is required [2], [3], [4], [7].
Knowing that afferent nerves can be stimulated by electric, mechanical, and thermal stimuli, a number of haptic interfaces have been developed to elicit such types of haptic feedback [4], [5], [6]. Although mechanical haptic interfaces are the most common types [5], the vibrotactile are the ones that have been predominantly developed, evolved, and investigated [3], [4], [6]. While contemporary tactile devices appear to be effective, their cost, portability, and wearability remain issues that should be further addressed [3], [6], [8].
To this end, electrotactile interfaces have the advantage of being more portable and wearable due to their actuators' size, as well as benefiting from lower power consumption and manufacturing cost, especially when using a high number of actuators/stimulators (e.g., when stimulating multiple areas) [4], [9]. Also, its advantageous wearability/portability and lower cost could be further improved by using micromachining means for the fabrication of high-density stimulators [4]. Electrotactile feedback is provided by a system comprised of electrodes and stimulators (actuators). As displayed in Fig. 1, the electrical current travels through the subdermal area between the anode(s) and cathode(s) and stimulates the nerves endings (i.e., skin's receptors). The area of the skin where the electrode contacts the skin is stimulated, however the sensation may be spread further when the contact point is near nerve bundles [10]. The way electrotactile systems functions is therefore completely different than mechanical and thermal tactile interfaces, as it electrotactile feedback is not mediated by any skin receptor.

Stimulation Area
This paper presents a systematic review and meta-analysis of electrotactile technology for the hands between 2010 and 2021. The systematic review facilitates a comprehensive presentation of the state-of-art, while the meta-analysis enables a quantitative congregation of the findings, which in turn allow us to offer insights into the state-of-art and suggest future directions.
Specifically, Section 2 introduces the methodology and inclusion criteria of the review, and then offers a discussion of the amassed papers clustered by the type of stimulation (i.e., epidermal and subdermal) and the type of implementation (i.e., portable devices, augmented haptics, guidance & notification systems, biomedical devices, and teleoperation & VR). Section 3 presents the methods for the meta-analysis, where the frequency of the findings, the sample sizes, and the locations of stimulation are analyzed and discussed. Section 4 offers a comprehensive discussion, where perspectives on the current state-of-art and future directions are discussed in terms of reliability of findings, technical improvements, wearability/portability, and implementations (i.e., prostheses, teleoperation, and VR). Finally, Section 5 offers a concise recapitulation of all the above.

Methodology
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed [93]. A step-wise method was hence used for amassing and then filtering the research items based on their relevance to the topic (see Fig. 2). Five digital databases were used: (1) IEEE Xplore Digital Library; (2) ACM Digital Library; (3) ScienceDirect; (4) Web of Science; and (5) Scopus to ensure a comprehensive collection of electrotactile feedback implementations for hand interactions. Correspondingly, suitable keywords and Boolean logic were used to facilitate an extensive inclusion, yet specificity to the topic (see Fig. 2). A chronological limit was set since the technology was predominantly advanced during the last decade. Finally, parsing criteria were used to ensure relevance to the topic (e.g., stimulation on upper limbs; discussion of user study's results). The amassed studies are displayed in Table 1 clustered by their findings. This section will attempt to provide the reader with an overview of the electrotactile feedback implementations in diverse disciplines for various hand interactions.We categorize the selected works with respect to the two main types of stimulation (i.e., epidermal and subdermal) to facilitate a discussion over the pros and cons of each approach. Also, the amassed studies are differentiated and clustered based on the type of implementation. This categorization allows a discussion specific to the requirements of each type of implementation.
Moreover, Sato and Tachi, (2010) effectively rendered force vectors on the fingertip [81]. In their subsequent study, Sato et al. by rendering diverse combinations of force vectors on the fingertip, they managed to render texture properties (i.e., smoothness and roughness) [58]. These results were also replicated in other studies (i.e., [57], [59]), which further support the potency of electrotactile feedback to render textural information, and facilitate recognition and discrimination between diverse materials [59]. Finally, in another follow-up study, Okabe et al., (2011) by rendering the force vectors, elicited an illusion of multi-directional motion to the users [43]. The users perceived a motion of sliding over a surface by following diverse trajectories.
Nevertheless, these encouraging results were not limited to the stimulation of the fingers. Another area of interest on the hand, for facilitating human-machine/computer interactions, is the palm [2], [3], [4], [6], [94]. However, the skin of palm folds and stretches in various ways especially in its central area [96], which may affect the impedance of electrical stimulation. Also, the peripheral regions of the palm have been found to be more receptive to haptic feedback [96]. Since, the impedance of electrotactile feedback is an important factor for its effectiveness and pleasantness [5], the electrical stimulation hence should be carefully delivered on the peripheral regions of the palm. In accordance with these methodological suggestions, the users in the study of Boldt et al., (2014) were able to discriminate the spatial (i.e., location) and temporal (i.e., frequency) aspects of the electrical stimulation [11]. Also, recently, Alotaibi et al., (2020) conducted a user study (N = 21), where the participants perceived tactile sensations on their palm [34]. The same study also revealed that the amplitude and pulse width of the electrical stimulation on the palm play a significant role in the perceive sensations [34].

Subdermal Stimulation
As discussed above, skin's conductivity is important for the perceptual effectiveness and pleasantness of the electrotactile feedback. However, the skin's conductivity may fluctuate over time due to physiological changes such as perspiration, which may affect the electrodermal activity [97]. In response to this drawback, the subdermal stimulation has been counter-proposed for providing the user with a stable electrotactile feedback, which may also be more energy-cost-effective [47]. Micro-needle electrodes have been successfully used for providing the user with tactile sensations by stimulating the index fingertip [47] and the forearm [26]. The microneedle electrodes penetrate the glabrous skin and stimulate the deep receptors (e.g., pacini corpuscles) of the finger (see Fig. 3) [80]. Interestingly, this subdermal stimulation was efficient in rendering textural aspects like roughness [80]. However, regarding the stimulation of forearm, the subdermal stimulation was found to be significantly less stable (i.e., greater psychophysical variations) than epidermal stimulation [33]. Also, epidermal stimulation appears to have better spatial resolution [40]. However, subdermal stimulation appears to be advantageous in terms of practicality (e.g., compactness and placement) which is important in applications, such as prosthetics, where permanent placement may be preferred [40]. Nevertheless, in human-machine/computer applications, where wearability or portability is more crucial, a non-invasive solution like epidermal stimulation would be more effective and practical.

Electrotactile Feedback for Portable Devices
Another advantage of electrotactile feedback is that it can be easily applied to portable devices. One of the early attempts of this decade is the Palm Touch Panel by Fukushima and Kajimoto, (2011) [42]. This handheld device was at the size of a smartphone, where the front display was the interactive one (i.e., a touchscreen), and the back was the stimulating part, which delivers the electrotactile feedback to the palm. The Palm Touch Panel was able to render the sense of touching (i.e., selecting/interacting) by stimulating the corresponding area of the palmar skin [42]. Using a comparable stimulator attached on the back of a smartphone, Khurelbaatar et al. stimulated the index fingertip of the user's hand, which was holding the smartphone, while the interaction with the touchscreen was done by the other hand's index [69]. In their study, the users successfully recognized diverse shapes, which indicated the spatial accuracy of the electrotactile feedback [69].
Furthermore, on electrotactile touchpads, users who were receiving the electrotactile feedback on the interactive index fingertip reported a high acceptability, as well as an easiness to differentiate the intensity of stimulation [30]. Using a comparable haptic display, the finger tracking was evaluated as highly accurate [67]. Finally, Yem and Kajimoto, (2017) study postulated that the users were perceiving the amount of pressure applied on the haptic interface, by delivering a stimulation proportional to the applied pressure [38].
However, the unique advantage of electrotactile feedback is that can be delivered through transparent displays, which are placed in front and/or the back of touchscreen [56]. In Altinsoy and Merchel, (2011) the users received an electrotactile feedback on both the hand holding the device (i.e., stimulating the palm) and the index fingertip which was interacting with the touchscreen [56]. Importantly, the users received textural information, which was assisted them with recognizing and differentiating effectively between sandpaper and grooved wood [56]. Kajimoto, (2012) examined the Skeletouch, a transparent haptic display, which was used over a smartphone's touchscreen for efficiently rendering the sense of touch by stimulating the index fingertip [20]. Equally, Wang et al., (2018) used E-Pad, a transparent electrotactile display over a tablet (see (a) in Fig. 4) for rendering motion (i.e., index movement on the touchscreen) and provide guidance (i.e., following the path) [21]. The participants were substantially better in following the path when they were receiving electrotactile feedback than when they were receiving vibrotactile feedback [21].

Augmented Haptics
As seen above, the electrotactile feedback can be delivered over other surfaces (i.e., tablets and smartphones). This also suggests electrotactile feedback's potentiality for augmenting haptic information. Indeed, Yoshimoto et al., (2013) by stimulating the middle phalanx of the index, were able to provide the users with a sense of touch on the fingertip, while the fingertip was at midair point over the target-object [50]. In addition, the users were able to perceive the pressing force on the augmented surface of the target-object [50]. Comparably, other studies were able to render textural information by stimulating the index fingertip [60] or middle phalanx [61]. Interestingly, the augmented haptic information, by stimulating the middle phalanxes of index and thumb, was implemented for guiding the user's hand movements and significantly improving the performance on a carving task [87].
However, the above studies have used "traditional" electrodes, while one of the benefits of electrotactile feedback is that can be offered through thin, flexible, and/or transparent electrodes. Withan et al.,(2018) implemented thin, flexible, and transparent electrodes, which were also connected to a relatively small stimulator (see (b) in Fig. 4) [23]. This electrotactile device not only presented a desirable wearability and portability, but it was also capable of delivering augmented tactile sensations of various surfaces (e.g., a toy car, pencil, paper, and human skin) and while performing diverse tasks in physical (e.g., writing) and virtual reality (i.e., interacting with virtual objects) [23]. Lastly, in a follow-up study, Groeger et al., (2019) used similar 3-D printed electrodes connected to a portable controller (stimulator), which they then were attached on the surface various objects (e.g., pen or smartphone's case) instead of the human skin (e.g., on the fingertip) [22]. This study demonstrated the acceptability of the electrotactile feedback, and a effective augmentation of tactile sensations [22]. Although these multi-purpose electrodes can be easily 3-D printed [22], an important drawback is that their durability is limited to up to 8 hours in a office environment [23].

Guidance & Notification
As discussed above, the study of Yoshimoto et al., (2016) showed that electrotactile feedback may be efficiently used for providing guidance to the user for performing tasks (i.e., carving), which require fine movements [87]. In the same direction, the study of Pamungkas and Turnip, (2019) examined how the electrotactile feedback may assist the user with virtual typing (i.e, mid-air interactions with a virtual keyboard) [92]. By stimulating the lower phalanxes of all five fingers, their study showed that electrotactile feedback facilitates a substantially faster learning curve for virtual typing [92]. The guidance in both studies is provided by notifying the users that their movements deviate from the optimal ones.
Hence, the electrotactile feedback may also be used as a medium for delivering notifications. Indeed, electrotactile feedback has been found to be effective for facilitating communication between the user and Internet of Things (IoT) devices [76]. Notably, Stanke et al., (2020) compared the efficiency of electrotactile and vibrotactile feedback in providing notifications to IoT users by stimulating the left hand's wrist and ring proximal phalanx [74]. Their results indicated that, compared to vibrotactile feedback, electrotactile feedback has a substantially better spatial resolution and facilitates a significatntly improved recognition of notification patterns [74]. Contemporary wearable devices like the Apple Watch, which are worn on the wrist, provide predominantly a vibrotactile feedback to the user [6]. Thus, the aforementioned results indicate that electrotactile feedback may be an alternative with improved performance, while it also alleviates financial burden due to its cost-effective design and fabrication.

Biomedical Applications: Assistive Devices
Electrotactile Feedback has been also implemented for biomedical applications. As discussed above (see 2.2., 2.4-2.6) electrotactile feedback has been effectively delivered on the index fingertip. Likewise, researchers have administered it on the index fingertip in order to provide the users with a sensory substitution (e.g., hapticvisual or haptic-audio), which may improve the everyday lives of individuals with respective sensory impairments.
For substituting visual with haptic information, Liu et al., (2016) developed the Finger Eye, a wearable text reading assistive system for the blind and visually impaired individuals [88]. In their pilot study, they successfully converted visual data (i.e, text displayed on a screen or paper), which were captured by a Pan-Tilt-Zoom (PTZ) camera, into haptic information correspondingly to the braille reading system. Hence, using text recognition, Liu et al., (2016) actually translated conventional text into braille [88]. However, a disadvantage was that the camera was bulky (i.e., normal size of a PTZ camera) which prevents the wearability and portability of such text reading assistive system. Nevertheless, Rahimi et al. (2019b) developed a significantly more portable and wearable similar device (see (g) in Fig. 4) [89]. Also, Rahimi et al, (2019a) and (2019b) attempted to provide an automated recurrent calibration process [31], [89]. Although more research is needed towards this direction, this was a first step to achieve an automated regulation of stimulation power, which will address one of the main drawbacks of electrotactile feedback.
Furthermore, Huang et al. (2017aHuang et al. ( , 2017b) substituted audio with haptic information [90], [91]. Through a tactile transducer, they administered electrotactile stimulation on the index fingertip to ameliorate speech recognition of cochlear implant users (i.e., with hearing impairment) [90], [91]. They converted speech sound amplitude and frequency into electrical signal with equivalent properties. However, the frequency of the electrical signal did not overpass the 200Hz to ensure a temporal discrimination of the amplified speech. Indeed, cochlear implant users showed a significantly improved speech recognition in noise [90], [91]. The significantly improved speech recognition was observed both in Mandarin [90] and English speakers [91].

Biomedical Applications: Prostheses
Somatosensory information plays a crucial role in both motor performance as well as embodied cognition, which makes it vital for prosthetic hands/arms users [16], [98]. Notably, the sense of embodiment has been suggested to increase the acceptance of a prosthetic hand/arm [16], [98]. Electrotactile, vibrotactile, or combined feedback of them have found to increase both motor performance of prosthetic hand/arm users, as well as the sense of embodiment, although more research is required to further explore those advantages [16], [98]. Regarding electrotactile feedback, the majority of the implementations are related to prostheses of upper limbs, which will be discussed here.
The rationale is that the electrical stimulation is provided to the forearm or the upper arm, by electrodes placed in the circumference of the limb (aka non-somatotopic feedback), where the stimulation of each electrode attempts to represent the tactile sensation of a hand's part (e.g, a finger and/or the palm.). Indeed, the users can differentiate each stimulation and associate it correctly with the corresponding finger [68], without requiring a somatotopic feedback (i.e., the electrodes to be placed near or at the amputation area) [70]. Also, in the hybrid systems (i.e., vibrotactile and electrotactile), the electrotactile feedback can be easily differentiated from the vibrotactile feedback [17]. Notably, the electrotactile feedbakc has been well accepted by the users [32]. Given that the somatosensory information is provided by the electrotactile and the motor abilities by an electromyography (EMG) means (i.e., measuring the muscle movement of the forearm, informs on applying the corresponding hand movement and force), a synchronization between electrotactile feedback and EMG is paramount. Strbac et al., (2019) study achieved an effective synchronization between electrotactile feedback (see (f) in Fig. 4 for the electrotactile device) and EMG, while the users were able to perceive tactile sensations [39]. However, a drawback of electrotactile feedback is that it necessitates recurrent calibration for providing a personalized and pleasant feedback. Isakovic et al., (2019) offered an optimized calibration process, though follow-up research is required [32].
Furthermore, the sense of touch has been repeatedly rendered with success by delivering electrotactile feedback [36], [51], [52], [53]. However, like the vast majority of the relevant studies, the electrotactile feedback was administered on the forearm. Nonetheless, the study of Wang et al., (2013) replicated the successful touch rendering by stimulating the upper arm [46]. Also, considering that spatial aspects are crucial for hand interactions, the location of electrotactile feedback has be found that can be easily differentiated [70], [72]. Beyond the position, the orientation and
direction of the stimulation were successfully identified by the user [53]. Importantly, the study of Geng and Jensen, (2014) revealed a spatiotemporal identification of the artificial finger's position and the pulses' width, where the users recognized and paired the current position of the active finger, and/with the duration of stimulation [51]. These results thus support the necessary perceptual mechanisms for hand interactions.
However, additional perceptual processes are required for hand interactions, which pertain to shape, size, textural, and motion features. In this direction, the earliest attempt in this decade was made by the study of Damian et al., (2012), where an electrotactile feedback on the forearm allowed the users to perceived the motion of the grabbed object, as well as the slippery and the grip force [62]. Similarly, Franceschi et al., (2017) showed that the users can recognize the shape, trajectory, and direction of a moving target-object [65]. Also, the participants in Choi et al., (2017) study received a stimulation on the upper arm, which enabled them to perceive the pressure that the prosthesis applied on the surface [75]. Equally, the participants of Huang et al., (2012) by receiving a stimulaton on forearm, were able to perceive the applied force [44]. Finally, Chai et al., (2019) showed that the electrotactile feedback on the forearm facilitates a discrimination of the size and texture (i.e., hardness and softness) of the target object, and it also assists with differentiation of the applied grasping force [73]. Consequently, these results indicate the potency of electrotactile feedback to improve grasping and handling performance of prostheses' users. Nevertheless, these benefits should be studied directly, especially in patients (i.e., amputees), since the aforementioned studies majorly recruited abled (i.e., healthy) individuals.
Several studies have indeed examined the benefits of electotactile feedback (predominantly applied on the forearm) on hand interactions (e.g., grasping) in amputees. The study of Strbac et al., (2016) found that the set up of the electrotactile system on amputees was effortless, and the amputees were able to perceive kinesthetic information such as aperture, grasping force, and wrist rotation [14]. Also, Isakovic et al., (2016) demonstrated that electrotactile feedback assisted the amputees to have a significantly improved force perception, as well as a substantially improved grasping accuracy and precision [82]. Substantial improvements in force perception and grasping performance of amputees were also found in the study of Witteveen et al., (2012), where the users were controlling a virtual hand [45]; and in the study of Strbac et al., (2017), where the users were controlling a robotic hand (Michelangelo hand) [83]. Notably, the amputees in the study of Xu et al., (2016), beyond a significantly improved grasping and force control, showed also significant improvements in grasping stability and movement speed [64]. These results were also replicated in the study of Cheng et al., (2019), where by using a wearable electrotactile system (see (e) in Fig. 4) for stimulating the upper arm, they also observed that the individuals with amputations showed significant improvements in differentiating materials with diverse level of stiffness [79]. Of note, in contrast with the above mentioned studies, which implemented a closed-loop between EMG and electrotactile feedback, the study of Achanccaray and Hayashibe, (2020) implemented the electrotactile feedback in combination with a brain-computer-interface (BCI), where the latter was controlling the prosthetic's movements and actions [66]. Comparably to the closed-loop systems, their study revealed that the conjunction of BCI and electrotactile feedback facilitates a substantially improved motor imagery, and significant improvements in flexion and extension, which are the essential grasping movements [66].

Teleoperation & VR
Observing the advantages the electrotactile feedback brings to the control of prosthetic hands, one would reasonably expect these advantages would also apply to the teleoperation of robotic hands and other mechatronics. However, a significant difference between prosthetic and robotic-teleoperation is that the feedback is nonsomatotopic (i.e., the feedback is applied on a anatomical part which is in-distance from the moving/interactive anatomical part) while in the latter is somatotopic (i.e., the feedback is appied on the corresponding anatomical part which initiates the actions). Hence, in teleoperation, the user expects to receive the haptic feedback on the same area of the hand with the corresponding area of the robotic hand which makes the contact with the target object. Nevertheless, despite the essential differences, the studies which have implemented electrotactile feedbakc for teleoperation purposes have produced findings comparable to the findings on prostheses.
Pamungkas and Ward (2013) developed a glove which applies electrotactile feedback on the dorsal area of hand (see (c) in Fig. 4), which was implemented for teleoperating a robotic hand [49]. In their pilot study, they rendered the sense of touch, which allowed the operator to successfully place a peg in a hole [49]. In a subsequent pilot study, they tele-operated a robotic drill through immersive VR, where the operator performed a drilling with dexterity and accuracy [19]. In Pamungkas and Ward (2016) pilot study, they used the same glove to convey the sensation of contacts in immersive VR for diverse use cases such as playing with a virtual bouncing ball, setting up a campfire, feeling different surface textures, and shooting a gun [99]. While these are promising results, pilot system-testing studies (e.g., N = 1-3), like the aforementioned studies, are inadequate for providing reliable results and conclusions.
On the other hand, Sagardia et al., (2015) developed a VR teleoperation system integrated with and an electrotactile delivery system composed of a small tactor with eight electrodes for each finger (see (d) in Fig. 4), in an attempt to offer a simulation/training for on-orbit servicing (OOS) missions [18]. The motor movement was controlled by a bimanual haptic device HUG (i.e., lightweight robot arms) and the electrotactile feedback was delivered to each fingertip. Their system was tested in three VR assembly tasks for remote OOS missions, which included pressing a button, switching a lever switch, and pulling a module from its slot. The VR teleoperation training showed comparable results to the physical training, while the users reported that the VR training was highly realistic [18]. Importantly, their results were replicated and their system further scrutinized in a user study with an adequate sample size (N = 19) [85]. In this follow-up study of Hummel et al. (2016) the significant difference was that the motor movements in VR were not facilitated by bimanual robotic hands, instead, the hands of the users were tracked, hence their hand-movements were directly mirrored in VR [85]. Their user study found that the electrotactile feedback substantially improved the grasping performance of the users, while it significantly decreased their workload [85]. These results hence appear to replicate the benefits of electrotactile feedback that were seen in prostheses (e.g., improved grasping), and indicate that similar advantages can be seen in VR for computerand/or machine-human interactions.

META-ANALYSIS
In order to better analyze and categorize the above works, we carried out a meta-analysis of their main characteristics, extracting and converting them into variables. We considered: (i) Findings: (a) The frequency of the findings pertaining to the conclusions of each study are considered. The findings are analyzed by a natural language processing (NLP) approach to indicate the most frequent findings/conclusions. (b) The field of application is also considered, including biomedical engineering, computer science, robotics, mechanical engineering, and psychology. (c) The rendered information, sense or illusion, such as tactile, kinesthetic, thermal, and guidance. (ii) Sample Size: (a) The sample size is divided into clusters (i.e., N ≤ 10, N ≥ 10 , N ≥ 15, and N ≥ 20) to evaluate the frequency of each sample size cluster. (b) The type of study is also defined by the sample size.
The studies are dichotomized to pilot (N ≤ 10) and user studies (N > 10). (iii) Stimulation Locations and Devices: (a) The stimulation's locations, including the forearm, the upper arm, the wrist, the dorsal hand, the palm, the phalanxes, and the fingertips. (b) The type of electrotactile device, categorized into free electrodes (i.e., only the electrodes are on the skin), multipad (multiple electrodes on a pad), armband (i.e., electrodes and stimulator are on the arm), microneedle electrodes, finger cap, wristband (same as armband but on the wrist), touchpad, and glove. (c) The connected device, which includes the other devices or systems that the haptic interfaces are connected with (e.g., prosthetic hand,tablets, smartphones, tablets, cochlear implants, VR, brain computer interfaces, and internet of things).

Findings' Frequency
As the Fig. 5 displays, the most frequent findings pertains to the successful rendering of tactile sensations (e.g., touching a surface), closely followed by the high acceptability of electrotactile feedback by the users. In line with the subsequent most frequent findings, as a result of an efficient touch/contact rendering is the facilitation of an equally successful recognition and discrimination of objects based on their shape and size. However, the recognition and discrimination require the elicitation of perceptual processes pertaining to spatial, temporal, textural (e.g., roughness), and motion features. Indeed, as it can be seen in Fig. 5, these perceptual processes were also frequently facilitated in the studied that implemented electrotactile feedback. This is also in line with the frequency of eliciting haptic information pertinent to tactile and kinesthetic features (see Fig. 6), where the 2/3 offered tactile information, and the 1/3 of the studies offered both tactile and kinesthetic information. However, the observed frequencies may have been modulated by the dominance of biomedical engineering applications (see Fig. 6), which have predominatly used electrotactile feedback for providing prostheses' users with somatosensory information. Also, this coincides with the particularly high frequency of findings pertaining to performance improvements on force control and grasping (see Fig. 5). Nevertheless, the improvements in grasping and force control would not be facilitated without having also benefits on movements' stability, accuracy, and speed, as well as an efficient perception of object's motion, slipping, and stiffness, which can be seen in Fig. 5 as frequent findings. These frequent findings indisputably indicate the benefits that electrotactile feedback brings in human-machine/computer-interactions.
Furthermore, the remaining frequent findings can be seen that they relate to the types of implementations, which were discussed in Section 2. Thus, as the Fig. 5 displays, the electrotactile feedback can be implemented for providing guidance to the user (e.g, in teleoperaction or in using portable devices), augmenting haptic sensations, notifying IoT users, improving speech recognition, assisting in virtual typing, and converting conventional text into braille text. However, a downside is that automating and/or optimizing the calibration of electrotactile feedback was scarcely attempted. Furthermore, although that the findings were replicated across diverse disciplines and implementations (see Fig. 6, the transferability and applicability of the benefits should be replicated and validated in the field or implementation of interest. Finally, a disadvantage that was observed across the studies, is the sample size, where the 2/3 of the studies were pilots (i.e., N ≤ 10), while only 1/3 of the studies were user studies with N > 10 (see Fig. 6).

Sample Size
Since the sample size defines the generalizability and reliability of the results, the sample size should thus be considered for interpreting responsibly the amassed findings pertinent to the electrotactile feedback. Beyond the dichotomized approach in Fig. 6, which showed that 66% were pilot studies (i.e., N ≤ 10), and 34% were user studies (i.e., N > 10), the sample size will also be discussed based on four clusters (see Fig. 7). As it can be seen only the 18% of the studies had a sample size N ≥ 15, and only 6% had a sample size N ≥ 20. This indicates that user studies with a substantial sample size, which examined the utility and potentials of electrotactile feedback, were scarcely conducted in the last decade. Unfortunately, the 72% had a sample size N ≤ 10. The 41% of the studies had a sample size N ≥ 10, while and 59% had a sample size N < 10 (see Fig. 7). While a small sample size can be justified in studies which recruited patients, the rest user studies should have had larger samples. Notably, only 13% (i.e., 10/78) recruited patients, while the rest 87% recruited healthy individuals (predominantly students). The high percentage of pilot studies and small user studies raises considerable concerns regarding the generalizability and reliability of the findings discussed above. However, it should be considered that the findings appeared to be replicated across studies and disciplines. Nevertheless, the devices were predominantly different, where most of them were not examined in more than one study (i.e., conducting a couple or several follow-up studies), and they were not designed to address comparable user requirements (i.e., substantially different target population). Thus, although the observed advantages of electrotactile feedback in the last decade, the sample size should be a criterion that should be taken into consideration in future studies in order their findings to produce reliable and generalizable results.

Stimulation Locations & Devices
As discussed above, the majority of the implementations of electrotactile feedback were in the field of prosthetics. This can also be seen in Fig. 8, where 31 studies delivered the stimulation on the forearm and 3 on the upper arm. It should be noted that there are 32 studies which examined electrotaticle feedback for providing prosthetics' users with somatosensory information. Hence, 28 studies stimulated only the forearm, only 1 study the upper arm, and 2 studies stimulated both the forearm and the upper arm. The latter studies were investigating the feasibility to gain comparable benefits by stimulating the upper arm. As it can be seen in Fig. 9 these studies (i.e., Biomedical Engineering) were using haptic devices such as free electrodes, multipads (i.e., multiple electrodes in a compact design), and armbands (i.e., the stimulator and the electrodes together in a compact device). Also, 24% of them targeted a prosthetic hand and 5% of them a virtual prosthetic hand. Furthermore, as it was discussed in Section 2, the biomedical applications also included a text-reading assistive system (i.e., text to Braille conversion) using a finger cap haptic device, and a speech recognition system using a tactile transducer (see also Fig. 9). Thus, the biomedical devices were examined in 37 studies (32 in prosthetics, 3 in Braille, and 2 in Speech recognition) in total. The text-reading assistive and the speech recognition system, had integrated an electrotactile feedback, which was delivered to the index fingertip. Thus, from the 29 studies which administered electrotactile feedback on the index fingertip Fig. 8, 5 of them were for these biomedical applications. As the Fig. 8 illustrates, the 2 nd most frequent location for receiving the electrotactile feedback, was the index fingertip. This is reasonable outcome since the fingertips, especially the index fingertip, are crucial for perceiving tactile and kinesthetic information, which are vital in hand interactions [1], [6], [8], [94], [95]. In the amassed studies, beyond the aforementioned biomedical applications, the index fingertip was stimulated in human-computer-interaction studies where the user was interacting with portable devices (e.g., tablets and smartphones), virtual environments (i.e., immersive VR), and for teleoperting robotic hands (see Fig. 9). Unfortunately, the deliverance of electrotactile feedback on the rest of locations has scarcely been examined. The studies of Pamungkas et al. (e.g., [19], [49]) were the ones which delivered electrotactile feedback on the dorsal hand (see Fig. 8). Also, the proximal phalanxes were all of them stimulated in a single study, where electrotactile feedback was used for virtual typing (see [92] and Fig. 8). The wrist was stimulated in a single study, where electrotactile was used for notifying IoT users (see [74]). The middle phalanxes were stimulated in studies, where electrotactile feedback was implemented for augmenting haptic sensations (e.g., [50], [61]). The electrotactile feedback on the palm was mainly examined in psychophysic studies (e.g., [11], [34]). Equally, the rest of the fingertips were stimulated only in psychophysical studies (e.g., [27]) or in teleoperation studies (e.g., [18], [85]). In summary, as the Fig. 8 clearly indicates, the forearm and the index fingertip were the locations, where the electrotactile feedback was predominantly administered and examined in the studies of the last decade. In contrast, the effects of delivering electrotactile feedback on the rest of the arm's and hand's anatomical locations appear to be under-investigated in this decade.

DISCUSSION & FUTURE DIRECTIONS
The systematic review and meta-analysis of the studies which implemented electrotactile feedback for hand interactions in the last decade, offered a comprehensive overview of the state-of-art and utility of electrotactile feedback in human-machine/computerinteractions. The electrotactile feedback appeared to elicit tactile sensations and perceptual information, which are essential in hand interactions. Also, the implementation of electrotactile feedback was found to facilitate improvements in performance on various tasks, such as grasping, speech recognition, and virtual typing among others. Furthermore, it was found to be efficient in converting conventional text into Braille, and providing the users with notifications (see IoT), augmented haptic sensations, and guidance. Notably, the advantages of electrotactile feedback were observed and replicated across diverse disciplines and haptic devices, by stimulating different anatomical parts of the arm and the hand. However, the systematic review and meta-analysis have also surfaced and indicated shortcoming and gaps that should be addressed in future studies.

Generalizability & Reliability of Findings
While the amassed findings indicated important benefits of electrotactile feedback for hand interactions, a significant shortcoming was that the studies predominantly had small samples sizes (e.g., N ≤ 10). Considering that the implementations target either large populations (e.g., users of portable devices, wearable devices like smartwatches, and immersive VR), or clinical populations (e.g., amputees, legally blind, and individuals with hearing impairments), the sample size is even more important for reliably generalizing the observed findings.
There is a replicability and reproducibility crisis that affects all the disciplines, which examine effects on humans subjects [100]. This crisis, unfortunately, postulates that the several findings may be false or misleading [100]. The main reason of this crisis are due to the small sample sizes of studies and the implementation of inappropriate statistical analyses [100]. The sample size affects the statistical power of the analysis. Consequently, studies with small sample sizes produce analyses with low statistical power [101]. Having under-powered statistical analyses, affects considerably the observed effect sizes, where they may substantially inflate them, or in some cases to deflate them [101]. Hence, under-powered statistical analyses prevents of accurately estimating the value (i.e., effect size) of the examined item.
In human-machine/computer-interaction studies, the effect sizes are of utter importance for examining the utility of the medium or technique [102]. Given that, the effect sizes constitute of primary concern, even more than p-values, the recruitment of an adequately large sample size is a precondition for appropriately examining the effect sizes [102]. In human-machine/computer-interaction, the sample size of "large" user studies vary form N = 15 -25, while the most common sample size is N = 12 [103]. However, in most disciplines, these sample sizes considered as moderate to small. In such sample sizes, the implementation of parsimonious statistical metrics is paramount for observing representative effect sizes [104]. Specifically, instead of Cohen s d (for t-tests and comparisons) and the η 2 (for ANOVA and regression analyses), the Hedges g and the ω 2 should respectively preferred in studies with moderate or small sample size [104].
In summary, considering all the above in conjunction with the identified shortcomings in the studies which implemented electrotactile feedback, future studies should strive to examine the effects and benefits in studies with larger sample sizes. We suggest a minimum of N = 10 for preliminary results, and N ≥ 15 for comprehensive user studies, especially for the studies which explore biomedical applications. Importantly, we suggest the examination of effect sizes to be conducted by using parsimonious metrics such as the the Hedges g (for t-tests and comparisons) and the ω 2 (for ANOVA and regression analyses).

Technical Improvements
As discussed in Section 2, a substantial disadvantage of electrotactile feedback is that may cause skin irritation, burns, electric shock, and discomfort [5]. To prevent such undesirable effects, a calibration may be conducted to adjust the intensity and duration of the electrotactile feedback to the personal sensational and discomfort thresholds of each individual/user [16]. However, physiological changes like perspiration may fluctuate skin's conductivity, which consequently may cause a lack of sensation or a discomfort respectively to the fluctuations [97]. While it was considered that the sensation and discomfort thresholds predominantly increase across the duration of exposure to electrotactile feedback, the study of Vizcay et al., (2021) showed that after familiarization with the electrotactile feedback the sensation and discomfort threshold does not appear to significantly change [105]. However, the absence of significant changes was observed after the 2 nd calibration, which indicates that this is product of increased tolerance and/or stabilization of skin's conductivity [105]. Another factor that should be considered is the location of the stimulation (i.e., diverse skin's thickness) which requires diverse sensation and discomfort threshold proportionally [16]. Therefore, the calibration should be done for each location of stimulation, and it also be recurrent for readjusting the sensation and discomfort thresholds proportionally to the perceptual changes (e.g., tolerance) and changes of skin's conductivity.
In the systematic review, only three studies were identified, which attempted to automate and optimize the calibration process. The studies of Rahimi et al, (2019a) and (2019b) attempted to provide an automated recurrent calibration process for the users (legally blind) of a Braille text-reading assistive system [31], [89]. Furthermore, Isakovic et al., (2019) attempted an optimizaztion of the calibration process in order to provide the users with a personalized electrotactile feedback [32]. Although that these are studies in the desired direction, further studies are required for achieving an optimal and automated recurrent calibration. Given its importance in user's experience and performance, this should be prioritized in future studies.
Moreover, the material of the electrodes is equally important for delivering a pleasant and efficient electrotactile feedback [5]. A recent review suggested that the material of contemporary electrodes meets these criteria [15]. However, as it was also seen in Withana et al., (2018) study, while these electrodes are light, flexible, efficient in eliciting tactile sensations, their main disadvantage is their limited durability [23]. Similarly, in the field of prosthetics, the studies of Isakovic et al., (2016) and Strbac, et al., (2016) implemented flexible electrodes integrated with a multichannel stimulator [14], [82]. However, the electrodes in these studies were also disposable with limited durability. On a positive note, the recent studies of Stephens et al., (2018b,2020) showed that concentric electrodes coated with a conductive graphene ink are equally flexible and efficient in delivering tactile sensations, and, importantly, are reusable with an estimated durability over a year [106], [107]. However, more research is required for elucidating on these promising findings of concentric graphene electrodes.

Wearable & Portable Devices
As discussed above, contemporary haptic devices should also be wearable and/or portable. The wearability of haptic devices not only improve their overall efficiency, but they also enable their frequent use in everyday life [6], [8]. The electrotactile feedback was predominantly integrated in devices with low wearability (e.g., see (d) in Fig. 4) Fig. 4) [23], [39], [71]. Despite that the two devices are destined for prosthetics and the other one for augmenting haptic sensations, they provide evidence in support of the wearability of electrotactile interfaces. Furthermore, the recent study of Stanke et al., (2020), by using a wristband, provided IoT users with notifications [74]. Given that contemporary wearable devices are frequently worn on the wrist (e.g., Apple Watch) [6], the study of Stanke et al., (2020) provides evidence that the electrotactile feedback can be feasibly integrated with such everyday devices.
Furthermorere, the electrotactile feedback was efficiently delivered through transparent haptic touchpads on portable devices such as smartphones and tablets. The Skeletouch of Kajimoto, (2012) on a smartphone's touchscreen [20] and the E-Pad (see (a) in Fig. 4) of Wang et al., (2018) on tablet's touchscreen [21] are the two most representative examples. Considering that the tablets and smartphones have become indispensable tools in everyday life, which are used to alleviate the daily cognitive workload and improve performance and productivity on everyday tasks and activities [108], [109], the use of haptic sensation may improve the performance and usability of these devices. Interestingly, the study of Wang et al., (2018) showed that electrotactile feedback may provide the user with guidance and improve spatial performance [21]. Nevertheless, none of the electrotactile feedback systems was integrated with the portable device in a compact design. Future iterations should attempt to derive with a compact and practical design of electrotactile systems integrated with portable devices. Finally, as the majority of studies predominantly stimulated the index fingertip. Future studies should also attempt to examine the effects of multi-finger stimulation in corresponding tasks which require interactions using multiple fingers.

Prostheses
As discussed in Sections 2 & 3, approximately half of the implementations of electrotactile feedback were in prosthetic hands' users. They produced robust evidence regarding the advantages of electrotactile feedback for providing the users with somatosensory information, especially in closed-loop systems with EMG. Electrotactile feedback was found to efficiently render tactile sensations, and eliciting perceptual processes pertaining to spatial (e.g., shape and size), textural (e.g., softness), and kinesthetic (e.g., object's or hand's motion) properties. Importantly, this enriched somatosensory information resulted in improvements in performance (e.g., force control, grasping and object's manipulation accuracy), as well as in an improved acceptance and embodiment of the prostheses.
However, only 3 out of 32 studies (i.e., 9%) examined electrotactile stimulation on the upper arm. Examining the administration of electrotactile feedback on upper arm would facilitate the replication of the findings (i.e., comparable to forearm stimulation) and postulate the applicability and utility of electrotactile feedback in prostheses (e.g., in closed loop systems) for individuals which were amputated at a point on the forearm or elbow. The studies which have done so (e.g., Choi et., (2019) [75] and Cheng et al., (2019) [79]) produced findings postulating that electrotactile feedback on the upper arm presents advantages comparable to forearm's stimulation. Nevertheless, future studies should strive to investigate further the administration of electrotactile feedback on upper arm.
Furthermore, only 7 out of 32 studies (i.e., 22%) examined these advantages in patients (i.e., amputees). Considering that the final aim is the amelioration of acceptance and performance of prostheses, the evaluation of the systems integrating electrotactile feedback in end-users (i.e., amputees) is of utter importance. Hence, prospective studies should attempt to examine the proposed systems in clinical populations. However, the proposed systems should be integrated with products (i.e., prosthetic hands or arms) that aim to be released in the market, in order to elucidate on the benefits of their system. In this systematic review, only one study was identified that conducted such study. Strbac et al., (2017) integrated their closed-loop system with Michelangelo hand (a prosthetic hand which is already in the market) for evaluating its performance [83]. Future studies should strive to evaluate the proposed systems by integrating them with prosthetics which are either already or intending to be released in the market.
Finally, as mentioned above, the electrotactile was used with EMG in closed-loop systems. Alternatively, the study of Achanccaray and Hayashibe, (2020) integrated electrotactile feedback with BCI, where an improved motor imagery, and significant improvements in flexion and extension were observed [66]. Given that flexion and extension are the main movements in grasping, the results of this study indicate that electrotactile with BCI systems may show benefits comparable to closed-loop systems. Future studies may attempt to examine further the BCI with electrotactile combination, which may be found to be an efficient alternative to closed-loop systems.

Teleoperation & Virtual Reality
As discussed above, prosthetic hands' users were provided with somatosensory substitution by implementing electrotactile interfaces. This somatosensory substitution facilitated an embodied perception of the prostheses, which also assisted the users with improving their performance on tasks requiring grasping and handling items. Similarly, the telepresence, which takes place in teleoperation systems, requires a sense of embodiment for the operated tool (e.g., robotic hand) which facilitates an improved control and performance. Indeed, comparable performance benefits were observed on teleoperation tasks requiring handling items (e.g., [18], [85]). Also, the majority of the teleoperation systems involved immersive VR (e.g., [18], [49], [54], [85]). This was well expected since the telepresence in teleoperation systems is comparable to the sense of presence in immersive VR.
In immersive VR, the sense of presence depends on the strength of the placement (i.e., realistic visuals), plausibility (i.e., realistic interactions), and embodiment illusions [110], [111], [112]. However, the placement illusion is fragile, especially when the virtual environment does not realistically react to the user's actions [111], [112]. This is resolved by the plausibility illusion, which is the deception of the user that the environment reacts to user's actions. Notably, the strength of the plausibility illusion is analogous to the sensorimotor contingency, which is the integration of the senses such as vision, hearing, touch, kinesthesia, smell, and taste [110], [113]. The haptic feedback pertains both to the sense of touch and kinesthesia (i.e., proprioception) [110], [114]. The haptics therefore additionally reinforce the plausibility illusion by providing an expected haptic feedback to the user [110], [114].
Also, the haptic feedback is central in embodiment illusion [114], [115], [116]. There is multidirectional interaction between haptic sensations and embodiment illusion, where the haptic feedback appears to reinforce the embodiment illusion [116], while a strong embodiment illusion (i.e., virtual body ownership) appears to intensify the perceived strength of the haptic sensation [115].
However, the wearability/portability of the haptic device is vital in immersive VR applications [6], [8]. As discussed above, the electrotactile seems to facilitate a compact and wearable design, which can be implemented in immersive VR applications for providing the users with haptic information [15]. Nevertheless, in this systematic review, only 6 studies used immersive VR, where most of the times was used for teleoperation purposes (e.g., Sagardia et al., (2015) [18]) and frequently had very small sample sizes (e.g., Pamungkas et al., (2013 [49], [54]). Of course, immersive VR is intrinsically related with teleoperation, since it offers an improved telepresence to the operator [117]. Thus, it is not come with surprise the frequency of implementing immersive VR for teleoperation purposes. Given that there are common requirements in prosthetics (i.e., embodiment), teleoperation (i.e., telepresence and embodiment), and immersive VR (i.e., sense of presence and embodiment), it would be expected that electrotactile feedback would facilitate improvements in user experience (i.e., presence) and performance. Thus, it was unexpected that a scarcity of electrotactile feedback implementations in teleoperation and immersive VR, especially in the latter, will be observed in the last decade.
The studies which implemented electrotactile feedback for improving immersive VR experiences, using VR head-mounted displays, were the small pilot study of Pamungkas and Ward, (2016) [99], and the pilot study of Withana et al.,(2018) [23]. Thus, it cannot be robustly supported that the improvements in embodiment and performance, which were observed in prosthetics' users, will be replicated in immersive VR. However, regarding performance, the user study of Hummel et al.,(2016), using a VR CAVE system, showed that electrotactile facilitates a better grasping [85]. Also, the recent user study of Vizcay et al., (2021), using a VR head-mounted display, equally reported improvements in contact accuracy and precision [105]. Although that these studies provided evidence in an adequate sample size (N = 19 and 21 respectively), further research is required for elucidating on the performance gains in immersive VR by administering electrotactile feedback. Importantly, no study has investigated the impact of electrotactile feedback on presence and embodiment in immersive VR. Considering that the sense of presence and embodiment are the cornerstones of immersive VR, future studies should hence endeavour to scrutinize the contribution of electrotactile feedback in them.

SYNOPSIS & CONCLUSIONS
In this paper we have conducted a systematic review of the electrotactile feedback implementations for various hand interactions in human-computer/machine-interaction systems. The concentrated studies were discussed per type of stimulation or implementation. Furthermore, a meta-analysis of the studies was additionally performed, which highlighted the replicability of the findings across diverse electrotactile feedback systems, as well as the methodological drawbacks and knowledge gaps. Finally, a comprehensive discussion of all the above was attempted to offer perspectives on the current state-of-art and future directions.
The amassed studies indicated that electrotactile feedback was predominantly used for biomedical applications, especially for providing prosthetic hands' users with somatosensory information. The early attempts were made using free electrodes attached either (principally) on the forearm or on the upper arm. More recent attempts involved a more ergonomic armband which includes both the electrodes (e.g., in a multipad form) and the stimulator. Other biomedical applications pertained to cochlear implant users and readers of Braille texts. Furthermore, electrotactile feedback was implemented to facilitate a successful robotic teleoperation and human-computer interaction in VR. Finally, haptic interfaces were used for providing users of portable devices (e.g., smartphones and tablets) with electrotactile sensations, as well as for augmenting haptic information.
Although the electrotactile feedback was predominantly used (approximately half of the applications) in biomedical engineering, comparable findings were found to every type of application for human-machine/computer-interaction regardless the discipline and the location of stimulation. This interdisciplinary replication of the amassed findings allows their presentation in a unifying manner. Hence, one could conclude that electrotactile feedback was found to be well accepted by diverse users. Also, it was successful in rendering and/or augmenting several tactile sensations pertinent to touch and textures. Electrotactile feedback elicited multiple perceptual processes such as spatial, temporal, kinesthetic (e.g., motion, force, and slipping), and textural (e.g.,roughness) perception. The haptic information provided by electrotactile feedback assisted the users to have a greater force control and grasping performance, which further facilitated an effective teleoperation of robotic hands and tools. Furthermore, electrotactile feeling was also efficient in providing guidance and improving speech reception, as well as facilitating Braille text reading. Finally, it was found efficient in notifying the user by eliciting a state of alertness, as well as for guiding the user in performing fine-movement tasks such as carving and virtual mid-air typing.
Although that a recurrent limitation pertaining to small sample sizes using diverse actuators/stimulators was observed and discussed, the findings were replicated regardless the location of the stimulation and the implemented device. Nevertheless, future studies should strive to examine the usability of electrotactile feedback in larger sample sizes and conduct appropriate statistical analyses respectively. A priority should be the automation and optimization of electrotactile feedback's recurrent calibration, as well as an improved durability of electrodes. Of course, the wearability and/or portability of electrotactile feedback should be another future aim by integrating it in devices such as smartphones, tablets, and smart watches. Also, electrotactile feedback systems may be integrated with commercial prosthetic hands/arms, for providing sensory substitution by stimulating the upper-arm or forearm, where the long-term effects on the users' performance, acceptability, and embodiment may be scrutinized. Finally, the same benefits on performance and embodiment may also be investigated in immersive VR and teleoperation systems, where these factors are crucial for the systems' effectiveness and user's experience.