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Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. This problem will be more challenging if KWS models are further required for edge devices due to their limited memory. To alleviate such an issue, we propose a novel diversity-aware incremental learning method named Rainbow Keywords (RK). Specifically, the proposed RK approach introduces a diversity-aware sampler to select a diverse set from historical and incoming keywords by calculating classification uncertainty. As a result, the RK approach can incrementally learn new tasks without forgetting prior knowledge. Besides, the RK approach also proposes data augmentation and knowledge distillation loss function for efficient memory management on the edge device. Experimental results show that the proposed RK approach achieves 4.2% absolute improvement in terms of average accuracy over the best baseline on Google Speech Command dataset with less required memory. The scripts are available on GitHub.

Introduction
Spoken keyword spotting (KWS) [1] aims to identify the specific keywords in the audio input. It serves as a primary module in many real-world applications, such as Apple Siri and Google Home, which are widely utilized on the edge device [2,3]. Current deep-learning-based keyword spotting systems are usually trained with limited keywords in the compact model for lower computation and smaller footprint [4,5,6,7]. Therefore, the performance of the KWS model trained by the source-domain data may degrade significantly when confronted with unseen keywords of the target-domain at run-time [8,9].
To alleviate such a problem, prior work [10,11] utilize fewshot fine-tuning [12] to adapt KWS models with training data from the target-domain for new scenarios. However, performances on data from the source domain after adaptation could be poor, which is also known as the catastrophic forgetting problem [13]. Recent work [14] proposes a progressive continual learning [15,16,17] strategy for small-footprint keyword spotting to alleviate the catastrophic forgetting problem when adapting the model trained by source-domain data with the target-domain data. The limitations of such an approach are two-fold. First, the approach requires the task-ID as auxiliary information to learn the knowledge of different tasks, which is not always available in practice. Second, the storage volume occupied by the model will increase with the higher task numbers [18]. The storage volume will be unaffordable for light edge devices.
This paper proposes a novel diversity-aware incremental learning approach named Rainbow Keywords (RK) to address the issues mentioned above, requiring no task-ID information with fewer parameters. Specifically, the proposed RK approach introduces a diversity-aware sampler to select few but diverse examples from historical and incoming keywords by calculating classification uncertainty. As a result, the model will not forget the prior knowledge when learning new keywords even utilizing limited historical examples. Furthermore, we utilize a mixedlabeled data augmentation to additionally improve the diversity of selected examples for higher performances. Besides, we propose a knowledge distillation loss function to guarantee that the prior knowledge could remain from the limited selected examples. We conduct our experiments on Google Speech Command dataset following the setup of prior work [19,20]. Experimental results show that the proposed RK approach achieves 4.2% absolute improvement in terms of Average Accuracy over the best baseline with less required memory.

RK Architecture
With online keyword spotting systems, we assume that the model should identify all keywords in a series of tasks without catastrophic forgetting. For each task τt, we have input pairs (xt, yt), where xt denote audio utterances and yt are keyword labels. We aim to minimize a cross-entropy loss [21,22] of all keywords N t up to the current task τt formulated as : where o denotes the output logits of the model in the task τt.

Rainbow Keywords Network
We now introduce the proposed Rainbow Keywords (RK) approach, which consists of three parts: a diversity-aware sampler, a data augmentation strategy, and a knowledge distillation loss function. The detailed workflow of each module is as followed.
As illustrated in Figure 1, we first receive several incoming audio utterances of the task τt as inputs D  Figure 1: Block diagram of the proposed Rainbow Keywords approach. Specifically, D t S denotes incoming audio stream data of the task τt. D t E and D t−1 E denote the examples of the task τt and τt−1, respectively. We x,x and K present each sample in Dc, the five perturbations of x and the five perturbation strategies. "Compute u(x)" is to compute u(x) by Eq.3.

Diversity-Aware Sampler
The diversity-aware sampler aims to select diverse examples to manage memory efficiently. Such diverse examples are defined by the relative location of each example in feature space, which are estimated by the uncertainty of the sample through the inference by the classification model [23]. The required three steps are shown in the right red box of Figure 1: Split by keywords: The first step gathers the historical examples D t−1 E and incoming data D t S , and groups them into subsets as Dc, c = 1...N t by unique keywords, where N t denotes the total numbers of unique keywords in D t−1 E ∪ D t S set. Estimate uncertainty: The second step estimates the uncertainty of each sample x in Dc by Monte-Carlo (MC) method [24], which is defined in Equation 2.
where x,x, y denote each audio utterance of keyword D N t , the five perturbations of x, and the label of x. Therefore, the uncertainty of the audio utterance x is formulated as u(x): where K presents five perturbation strategies, including Clipping Distortion [25], TimeMask [25], Shift [26], PitchShift [26] and FrequencyMask [25]. The larger u(x) indicates that the less confidence of model to predict the perturbations.
Select by uncertainty: The third step selects L examples from Dc descending by uncertainty u(x) with the step size of Only these examples are available for training.

Data Augmentation
As the examples in D t E are few due to memory limitation, we apply the data augmentation to further increase the diversity [27] of D t E . Specifically, we randomly mix two audio utterances to increase the amounts of training data without extra storage.

Knowledge Distillation Loss
Recent studies [28,29,30] show that Knowledge Distillation (KD) is effective for transferring knowledge between teacherstudent models. Inspired by such theory, we consider the model of task τt−1 as the teacher model and the model of task τt as the student model. We propose a knowledge distillation loss to preserve the prior knowledge from the teacher for the student model to avoid catastrophic forgetting, which is formulated as: where o t−1 (x) and o t (x) denote the output logits of the teacher model and student model, respectively. N t is all keywords up to the task τt. σ is the knowledge distillation softmax function parameterized by the temperature T . The temperature T is the experiential hyper-parameters of knowledge distillation set as 2.0. As a result, we aim to minimize the total loss of all keywords N t up to the current task τt formulated as: Where LCE is the cross-entropy loss defined in Eq.1, and LKD is the knowledge distillation loss defined above. λ is the experiential hyper-parameters defined as 1 − N t−1 N t .

Dataset
We conduct experiments on the Google Speech Command dataset v1 (GSC) [31], which includes 64,727 one-second audio clips with 30 English keywords categories. We utilize 80% of data for training and 20% of data for testing. All of the audio clips in GSC are sampled at 16kHz in our experiment.

Network configuration
We employ the TC-ResNet-8 [32] as our testbed to evaluate the proposed rainbow keywords approach. It includes a 1-D convolution layer followed by three residual blocks, which consist of 1-D convolution, batch normalization and ReLU active function. Each layer has {16,24,32,48} channels. We first pre-train the TC-ResNet-8 model on the GSC dataset with 32,000 audio clips, including 15 unique keywords. To evaluate the learning ability of the proposed RK approach, we split the rest data as 5 tasks. Each task includes 3 new unique keywords, which is unseen in previous tasks. To simulate the condition of edge devices, we set the max amount of examples L due to the limited memory in edge devices [19,20].
During the training stage, we utilize the Mel-frequency cepstrum coefficients (MFCC = 40) as inputs. The network is optimized by the Adam [33] algorithm with the learning rate 0.1. The batch size is set to 128 and the number of epochs is 50.

Reference baselines
We built eight baselines for comparisons.
• Fine-tune training: adapts the TC-ResNet-8 model for each new task without class incremental learning (CIL) strategies. We consider it as the lower-bound baseline.
• NR [34]: is a CIL approach which randomly selects training samples from previous tasks for future training.
• iCaRL [30]: is a CIL approach which selects the samples close to the mean of its own class. Then iCaRL utilizes examples for future training.
• EWC [35]: is a CIL approach which incorporates a quadratic penalty to regularize parameters of model that were important to past tasks. The importance of parameters is approximated by the Fisher Information Matrix.
• RWalk [36]: is a CIL approach which improves the EWC. Both Fisher Information Matrix approximation [35] and online path integral [37] are fused to calculate the importance for each parameter. RWalk also selects historical examples and utilizes them for future training. • BiC [29]: is a recent CIL approach with more attentions. BiC introduces an additional layer to correct task bias of the network. BiC also uses the same sampling method as iCaRL to select historical examples.
• PCL-KWS [14]: is a CL approach for Spoken Keyword Spotting. Specifically, the PCL-KWS includes several task-specific sub-networks to memorize the knowledge of the previous keywords. Then, a keyword-aware network scaling mechanism is introduced to reduce the network parameters. The PCL-KWS requires the task-ID to select conspronding sub-networks.
• Joint training: trains the TC-ResNet-8 model with the whole dataset, regardless of any constrains. We consider it as the upper-bound baseline.

Metrics
We report performances in terms of the accuracy and efficiency metrics. The accuracy metrics include Average Accuracy (ACC), and Backward Transfer (BWT) [38]. Specifically, the 'Average Accuracy' reports an accuracy averaged on all learned tasks after the entire training ends. The "BWT" evaluates accuracy changes on all previous tasks after learning a new task, indicating the forgetting degree. The efficiency metrics include Parameters and Memory [39]. The 'Parameter' measures the total parameters of the model in the strategy. The 'Memory' indicates the memory requirement of total training data in each task.

Effect of the knowledge distillation loss
We first analyse and summarize the performances with knowledge distillation loss. As shown in Table 1, we observe that the proposed knowledge distillation loss function achieves 54.5% relative improvements in terms of BWT.

Effect of various task numbers on RK approach
We further analyse and summarize the performances of the proposed RK approach with increasing task numbers as shown in Figure 2. The x-axis represents the task numbers (= 20, 10, 5, 4) and the y-axis is the evaluation metric of ACC. For example, when the task number is set to 20, we pre-train the TC-ResNet-8 with 21,000 audio clips including 10 unique keywords and the rest data is split into 20 tasks including 1 unique unseen keyword. The corresponding ACC is the accuracy of the testing set after each task is finished training. The memory size L is 1500 for the proposed RK approach here. We observe that the proposed RK approach achieves the best ACC performances with increasing task numbers. Even though the difficulty of preserving prior knowledge is increased with the increasing task numbers, the proposed RK approach still can obtain over 85.0% ACC, which is much better than other baselines.  Table 2: Average Accuracy (ACC) and Backward Transfer (BWT) in a comparative study of the proposed data augmentation. "NoAugment" means no data augmentation is applied in the experiment. L denotes the memory size for RK.

Effect of various memory size on RK approach
We then report the effect of various memory size (L=300 or 500) on RK approach, as shown in Figure 3. We constrain all methods under the same memory size as that of the RK approach. We observe that even all approaches perform much better with increasing memory size (i.e., more available training data), the proposed RK approach still outperforms other methods. Furthermore, even with limited memory size (L=300), the proposed RK approach can obtain over 65.0% ACC performances, much better than other methods.

Effect of the data augmentation
We also summarize the performances of the effects with two data augmentation methods on the proposed RK approach, as This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-100E-2018-006).
The computational work for this article was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg). shown in Table 2. We observe that all data augmentation methods improve the performances in terms of ACC. The best performances are achieved by the 'Mixup' data augmentation. We adopt the 'Mixup' data augmentation hereafter. Table 3 summarizes the comparison between the proposed RK and other competitive methods in terms of Average Accuracy (ACC), Backward Transfer (BWT), Parameters and Memory. The task number is set to 5. We observe that the proposed RK achieves the best performance with memory size L of 3000.

Benchmark against other competitive methods
Comparing with the best baselines: RWalk, the proposed RK-3000 achieves 4.2% absolute improvements in terms of Average Accuracy with fewer memory, which is closer to the upperbound performances. Furthermore, even with only 16.2M training data, our approach RK-500 has comparable performance to other baseline methods, which is effective on edge devices.

Conclusions
In this paper, we propose a novel diversity-aware class incremental learning method named Rainbow Keywords (RK) approach to avoid catastrophic forgetting with less memory. Experimental results show that the proposed RK approach achieves 4.2% absolute improvement in terms of average accuracy over the best baseline. Ablation study also indicates that the proposed data augmentation and knowledge distillation loss are quite effective on edge devices.


Development of a Novel Coaching Platform to Improve Tackle Technique in Youth Rugby Players: A Proof of Concept

Rugby union is a field sport that is played at amateur and professional levels by male and female players globally. One of the most prevalent injury risks associated with the sport involves tackle collisions with opposition players. This suggests that a targeted injury reduction strategy could focus on the tackle area in the game. In amateur rugby union, injuries to the head, face and shoulder are the most common injury sites in youth rugby playing populations. A suboptimal tackle technique may contribute to an increased injury risk in these populations. One proposed mitigation strategy to reduce tackle-related injuries in youth populations may be to increase tackle proficiency by coaching an effective tackle technique. The present study aimed to demonstrate a proof of concept for a tackle technique coaching platform using inertial measurement units (IMUs) and a bespoke mobile application developed for a mobile device (i.e., a mobile phone). The test battery provided a proof of concept for the primary objective of modelling the motion of a player in a tackle event. The prototype (bespoke mobile application) modelled the IMU in a 3D space and demonstrated the orientation during a tackle event. The participants simulated ten tackle events that were ten degrees above and ten degrees below the zero degree of approach, and these (unsafe tackles) were indicated by a red light on the mobile display unit. The parameters of ten degrees above and below the zero angle of approach were measured using an inclinometer mobile application. These tackle event simulations provided a real-time stream of data that displayed the angle of tackles on a mobile device. The novel coaching platform could therefore constitute part of an injury reduction strategy for amateur or novice coaches to instruct safer tackle practice in youth rugby playing populations.

Introduction
Rugby union is a field sport that is played at amateur and professional levels by male and female players globally. The most recent data from World Rugby report that the number of registered players stands at approximately 9.6 million across 123 countries [1]. One of the most prevalent injury risks associated with the sport involves tackle collisions with opposition players. Injury data from rugby surveillance projects in Ireland involving amateur rugby players (Irish Rugby Injury Surveillance (IRIS), 2018-2019) cited that the most common injuries occur in the physical contact areas (i.e., tackling, rucks) of the game [2]. In this report, it was recorded that tackling an opponent accounted for the majority of match and training injuries in amateur rugby (59% of the overall injury burden). IRIS also found that the most frequently reported match injury for both male and female players was concussion (11% and 19%, respectively). In the United Kingdom, injuries to the head, face and shoulder were the most common injury sites in amateur youth rugby playing populations, with over 50% of the injuries occurring in the tackle or collision areas of the game [3]. Data provided by research in school-level rugby union in Australia, with a similar age profile (age 13 ± 4.5 y), suggested that injuries to the head and face accounted for 33.7% (n = 112) of all injuries [4]. From the overall total of 332 injuries, there were 61 cases of suspected concussion injuries. Additional research found that amateur players who had an incorrect head position relative to the ball carrier resulted in a higher incidence of concussions, neck injuries and facial fractures when compared with players that performed tackles with the correct head position (the injury incidence with incorrect head positioning was 69.4/1000 tackles, compared with the injury incidence with correct head positioning of 2.7/1000 tackles) [5].
When compared with professional rugby, the injury incidence involving tackling in adult amateur rugby was lower than that in the professional game but had a higher incidence rate when compared with youth rugby [6]. This suggests that future injury reduction strategies must be aimed towards the tackle area in the game of amateur rugby union [6]. In youth rugby, a suboptimal tackle technique may contribute to an increased injury risk, but this risk can be reduced by implementing safer techniques during training sessions [7]. At youth rugby levels, it has been reported that many coaches have relied on informal or anecdotal resources (e.g., previous playing experience or observation of matches) to develop their knowledge of tackle training [8].
One proposed mitigation strategy to reduce tackle-related injuries in youth populations may be to enhance coaching skills that increase tackle proficiency and technical capacity in youth rugby players [8,9]. Additional research supports current rugby coaching practice, whereby a player should have their head positioned upwards and be facing the ball carrier to improve tackle technique proficiency [10]. This training technique results in a 'head up' position which allows the tackler to perform a shoulder tackle on the mid torso of the opposing player. This suggested tackle technique can assist the head placement of the tackler once the initial contact is instigated and can result in a more proficient tackle technique [10].
Evidence suggests that tackle mechanism-related research may act as a mitigating factor in reducing tackle-related injuries [11]. Further research has proposed that the tackler trunk had a direct effect on head kinematics when utilising tackles aimed at the mid and/or lower trunk of the player being tackled [12]. With respect to youth rugby, it has been advised that the 'head up and forward before contact' coaching cue may result in safer contact techniques [13]. This study found that players who were deemed to be concussed displayed a 'down' or 'away' head position on contact which may lead to an in-creased risk of head or neck injuries [13]. This coaching instruction is a technique that could be incorporated by coaches to instruct players to maintain the 'up and forward' head position into tackle contact and not only in anticipation of contact with an opposing player. Injury risk may be reduced when coaches are able to observe and measure tackle proficiency by quantifying the quality of the tackle technique.
For example, research has demonstrated the use of observable technical criteria which outline a list of clear distinct phases of tackling (i.e., pre-contact, contact and post-contact). This study analysed the tackle technique of each player which was scored one point or zero depending on the action performed during pre-contact, contact and post-contact [14]. For example, during the pre-contact phase, if the tackler was in a 'head up and forward' angle of approach, this was deemed as a good action and awarded one point. The sum of the points allocated was used to determine the technical proficiency score of the player during tackling or contact events. Further research has found that tacklers that had an incorrect head position or that had intentionally used a poor technique tended to result in more injuries as the players' head and neck were in an incorrect position [5].
By extension, these safer tackle techniques (i.e., 'head up and forward' and zero angle of approach) could be applied to other contact areas (e.g., rucks, scrums) to mitigate against injury in contact areas of the game. In amateur youth rugby, coaches that deliver tackle training may have knowledge gaps specifically related to their own competencies in coaching an effective tackle technique [9]. If coaches were to implement a diagnostic prescriptive model to address areas of improvement and monitor 'ideal' movement in all contact areas, this may reduce injury risk as coaches could instruct the optimal movement technique during contact training [15]. By utilising contemporary wearable technologies such as inertial measurement units (IMUs), the movement of players can be captured. The use of IMUs is well established in the literature [16]. There is support for the use of IMUs as tracking devices for human movement in sport and translating specific movement actions which can be detected and extracted using IMUs [17]. A recent study suggested that the use of microprocessor technology (such as that contained in IMUs) is a valid method to detect human motion in contact events in rugby union [18]. At present, IMUs are not linked to visualisation platforms for coaching tackle technique.
The present study aimed to demonstrate a proof of concept for a novel tackle technique training platform using IMUs and a bespoke mobile application developed for a mobile device (e.g., a mobile phone or tablet). It was hypothesised that developing a mobile coaching platform which utilises live IMU data could potentially lead to improved coaching strategies for amateur and novice coaches regarding tackle technique. This coaching platform could, in turn, reduce the incidence of upper head, neck and facial injuries in youth rugby by better informed coaching practice. The platform could therefore potentially constitute part of an injury reduction strategy for amateur or novice coaches that are responsible for instructing safe tackle practice in youth rugby playing populations.

Materials and Methods
A novel prototype mobile application was developed by the research team for use on mobile phones. The prototype application was initially developed for laptop usage and further engineered for use on a mobile phone. The development of this mobile functionality (e.g., mobile phone/tablet) was explicitly for use in an applied setting by amateur coaches for training sessions with amateur youth rugby union players.

Technical Description-Inertial Measurement Units (IMUs)
The Shimmer3 Wireless Sensor Unit (Shimmer Research Ltd., Dublin, Ireland) is a wearable device that contains two accelerometers, a gyroscope and a magnetometer. This combination of sensors is an IMU with nine degrees of freedom (DFs). The proposal would use the IMUs to capture the orientation of a player's body in a tackle simulation training exercise by using kinematic data which notionally focused on the orientation of a player's upper body (i.e., scapulae and shoulders).

Technical Description-Development of the Prototype Mobile Application
This bespoke mobile application is a lightweight application connecting the IMU data streams directly to a 3D model in the prototype mobile application. The axial output from the IMU is converted to Euler angles that are applied to the model in real time within the mobile application. Initially developed for a laptop using the Unity 3D Game Engine, it was further engineered and ported to a mobile device to increase access to data during training sessions. Unity3D was used in the development of the Android application as it has an integrated physics engine which was used when manipulating the nine DFs provided by the IMU. Converting from Euler angles to quaternions was necessary to manipulate the data returned from the IMU.
The system is composed of three core applications and a database (see Figure 1). There are two cross-platform applications and a representational state transfer application programming interface (REST API) server application backed by the database instance. The applications may be deployed on Android or Windows 10 (Microsoft Corporation, Redmond, WA, USA) devices, and the server application was deployed to Azure Web Services (Microsoft Corporation, USA) for this project.

Ethics
Ethics for this study was granted by the Taught Programme Research Ethics C mittee (TPREC) of the Atlantic Technological University (reference nu GMIT_TPREC_250120). All participants provided written and/or verbal informed con prior to data collection.

Data Collection
Four male participants (age 18.1 ± 0.6 y/height 179.5 ± 3.3 cm) were fitted with a vest (VX Sport Systems), and the IMUs were inserted into the GPS pocket located betw the scapulae of the GPS vest. This enabled a constant position on the participant to m ure the roll, pitch and yaw (i.e., rotation around the XYZ axes). The participants calib the IMUs by assuming a standing position and completing rotational, hinge and movement patterns.
Prior to the tackle simulation tests, all participants undertook a warmup tha cluded technical instruction (the participants were not rugby players), a physical m sation and a familiarisation with each test condition. The participants were instruct approach a foam-padded surface which simulated the contact area during tackle ev to become familiarised with the test area. Once the GPS vests and IMUs were worn b participants, they were instructed to approach the padded surface with their hips hi and their heads positioned upward parallel to the ground plane. When the particip touched the padded area with their shoulder, tape (zinc oxide) was applied to the pa area to measure where their shoulder met the padded area; this indicated each pa pant's zero angle of approach to the foam-padded surface when testing commenced Each participant engaged in ten static simulated tackle events (five attempts o left shoulder and five attempts on the right shoulder). These static tackle simulations executed in a parallel direction (i.e., simulating a zero angle of approach to the gro plane of the test area). The simulated tackles that were performed in this manner duced a green light on the prototype mobile application which indicated a safe tackle Figure 2. The participants simulated ten tackle events that were approximately ten grees above and ten degrees below these parallel tolerances, and these (unsafe tac

Ethics
Ethics for this study was granted by the Taught Programme Research Ethics Committee (TPREC) of the Atlantic Technological University (reference number GMIT_TPREC_250120). All participants provided written and/or verbal informed consent prior to data collection.

Data Collection
Four male participants (age 18.1 ± 0.6 y/height 179.5 ± 3.3 cm) were fitted with a GPS vest (VX Sport Systems), and the IMUs were inserted into the GPS pocket located between the scapulae of the GPS vest. This enabled a constant position on the participant to measure the roll, pitch and yaw (i.e., rotation around the XYZ axes). The participants calibrated the IMUs by assuming a standing position and completing rotational, hinge and gait movement patterns.
Prior to the tackle simulation tests, all participants undertook a warmup that included technical instruction (the participants were not rugby players), a physical mobilisation and a familiarisation with each test condition. The participants were instructed to approach a foam-padded surface which simulated the contact area during tackle events to become familiarised with the test area. Once the GPS vests and IMUs were worn by the participants, they were instructed to approach the padded surface with their hips hinged and their heads positioned upward parallel to the ground plane. When the participants touched the padded area with their shoulder, tape (zinc oxide) was applied to the padded area to measure where their shoulder met the padded area; this indicated each participant's zero angle of approach to the foam-padded surface when testing commenced.
Each participant engaged in ten static simulated tackle events (five attempts on the left shoulder and five attempts on the right shoulder). These static tackle simulations were executed in a parallel direction (i.e., simulating a zero angle of approach to the ground plane of the test area). The simulated tackles that were performed in this manner produced a green light on the prototype mobile application which indicated a safe tackle (see Figure 2. The participants simulated ten tackle events that were approximately ten degrees above and ten degrees below these parallel tolerances, and these (unsafe tackles) were indicated by a red light on the mobile display unit (see Figure 2). The parameters of ten degrees above and below the zero angle of approach were measured and marked on the foam-padded surface using an inclinometer mobile application (Clinometer, France).

Results
The test battery provided a proof of concept for the primary objective of modelling the motion of a participant in a tackle event using an IMU and a novel prototype mobile application. The prototype application modelled the IMU unit in a 3D space and demonstrated the orientation during a tackle event to the researchers. These tackle event simulations provided a real-time stream of data that displayed the angle of tackle for the researchers to observe on the mobile phone device. The data collected were saved and used to replay the tackle technique to each participant which assisted the researchers in modifying and coaching the participants' tackle posture characteristics. The data returned were used to calculate the roll, pitch and yaw of the IMU on the participant and update the 3D model on the mobile phone device. The pitch, roll and yaw are the rotations about the X, Y and Z axes, respectively. These axes are shown in Figure 3 for the IMU which are aligned on the participant with the Y axis along the spine and the X axis across the shoulders. Table 1 shows a sample of the data returned from the IMU during the testing phase of the project. Four readings are provided with 38 data points for each. The IMUs return data from the accelerometers, the gyroscope and the magnetometer. These data are used by the processor within the IMU to produce the axis angles and the quaternion values. For the purposes of this project, the data from the low-noise accelerometer. gyroscope, magnetometer, axis angle and quaternion were saved and imported for use in displaying the orientation of the Unity 3D model. Each data point is timestamped, and this is used to order the data points during a replay of a tackle event.
Based on these data, the axis angle and the quaternion values are calculated by the IMU processor during the data streaming process. These values are filtered for use by the Unity application and stored in a JavaScript Object Notation (JSON) format for ease of

Results
The test battery provided a proof of concept for the primary objective of modelling the motion of a participant in a tackle event using an IMU and a novel prototype mobile application. The prototype application modelled the IMU unit in a 3D space and demonstrated the orientation during a tackle event to the researchers. These tackle event simulations provided a real-time stream of data that displayed the angle of tackle for the researchers to observe on the mobile phone device. The data collected were saved and used to replay the tackle technique to each participant which assisted the researchers in modifying and coaching the participants' tackle posture characteristics. The data returned were used to calculate the roll, pitch and yaw of the IMU on the participant and update the 3D model on the mobile phone device. The pitch, roll and yaw are the rotations about the X, Y and Z axes, respectively. These axes are shown in Figure 3 for the IMU which are aligned on the participant with the Y axis along the spine and the X axis across the shoulders. Table 1 shows a sample of the data returned from the IMU during the testing phase of the project. Four readings are provided with 38 data points for each. The IMUs return data from the accelerometers, the gyroscope and the magnetometer. These data are used by the processor within the IMU to produce the axis angles and the quaternion values. For the purposes of this project, the data from the low-noise accelerometer. gyroscope, magnetometer, axis angle and quaternion were saved and imported for use in displaying the orientation of the Unity 3D model. Each data point is timestamped, and this is used to order the data points during a replay of a tackle event.
Based on these data, the axis angle and the quaternion values are calculated by the IMU processor during the data streaming process. These values are filtered for use by the Unity application and stored in a JavaScript Object Notation (JSON) format for ease of processing within the application. These data are illustrated in Figure 4 for a single reading.          The quaternions are used to calculate the roll, pitch and yaw to be reflected in the Unity 3D model. The calculations for these are conducted with the method shown in the code sample in Figure 5 and are based on standard math formulas for this purpose. The quaternions are used to calculate the roll, pitch and yaw to be reflected in the Unity 3D model. The calculations for these are conducted with the method shown in the code sample in Figure 5 and are based on standard math formulas for this purpose. Once these values are calculated, the 3D Unity application updates the orientation of the model and demonstrates the modelling of a player's motion, using the IMU in a tackle event in Figure 2. Tackle angles that were deemed safe (i.e., within ten degrees of a zero angle of approach) displayed a 'green' light on the mobile display, and tackles that were deemed unsafe displayed a 'red' light (i.e., outside the safety parameters of ten degrees above or below the zero angle of approach) on the mobile phone display (see Figure 2). Once these values are calculated, the 3D Unity application updates the orientation of the model and demonstrates the modelling of a player's motion, using the IMU in a tackle event in Figure 2. Tackle angles that were deemed safe (i.e., within ten degrees of a zero angle of approach) displayed a 'green' light on the mobile display, and tackles that were deemed unsafe displayed a 'red' light (i.e., outside the safety parameters of ten degrees above or below the zero angle of approach) on the mobile phone display (see Figure 2).

Discussion
The objective of this study was to demonstrate a proof of concept for a novel tackle technique training platform using IMUs and a bespoke mobile application developed for a mobile device (e.g., a mobile phone or tablet). This novel mobile coaching platform could be used to capture the motion and orientation of a participant's body during tackle events in rugby. The prototype coaching platform utilised a novel mobile phone application and IMUs as a sensor-based recording system. This study successfully provided a proof of concept for the coaching platform to demonstrate safe or unsafe angles of approach during tackle events in tackle technique training for amateur coaches. Participants demonstrated tackle angles that provided kinematic data of the upper body in static simulated tackle technique training. As this is a prototype tackle technique coaching platform, further development remains to refine the visual warning system for amateur coaches. The current prototype mobile phone application can provide visual feedback to the coach using a traffic light system (green or red) based on the angles of rotation around the XYZ axes. In this study, suggested tolerance parameters (i.e., ±10 • the zero angle of approach) of the tackle were based on existing kinematic rugby tackle data [12,15]. The system displays a green or red indicator light along the axes depending on the set tolerance parameters utilised in the study. The model on the mobile user interface (UI) changes colour as the angle of rotation approaches the tolerance parameters (ten degrees above or ten degrees below the parallel plane) which indicates either safe (green) or unsafe (red) angles of approach in a tackle event. The suggested parameters (±10 • ) were primarily selected as a guide to novice coaches, and assist in a 'head up and forward' approach to tackle safety. By adopting this approach to coaching safer tackle technique, it may assist in improving tackle posture and enable novice youth players to target the upper legs and lower trunk area of the ball carrier in live tackle scenarios.
As previously mentioned, coaching appropriate safer tackle technique may reduce injury risk in youth players or in adult amateur players [19]. The data provided by this platform could constitute part of an injury reduction strategy for amateur or novice coaches. Recent research has suggested that when coaches emphasise a proficient tackle technique in training, it has a positive relationship with the perception of importance for players in tackle training and overall injury reduction [20]. The data provided by this novel coaching platform could improve the knowledge of tackle technique and the development of better implementation techniques for the coach. Where coaches are capable of instructing, guiding and offering feedback to players on tackle technique, the players' tackle proficiency and self-efficacy progress in conjunction with better technique [20].
A future development of this platform for amateur coaches would be to validate the novel coaching platform against video analysis of tackle technique as video analysis is a recognised method for analysing biomechanical motion in rugby training or during active competition [21]. Evidence suggests that combining video analysis and wearable technology such as IMUs is a more robust method to investigate human motion in a sports setting [22][23][24][25]. This application of using video analysis for tackle technique may alter lower extremity kinematics for inexperienced youth rugby players which could reduce head accelerations during tackle events [26]. One advantage of using video analysis in sport is the ability to infinitely pause, replay and focus on specific segments of training or competitive conditions. This capability to pause and replay could aid amateur coaches in identifying tackle technique deficits, with a greater awareness of injury risk in rugby settings [27].
The present study offers a potentially novel platform to assist amateur coaches in identifying injury risk and improve the safety of tackle technique for youth rugby union players without the use of additional video analysis. The novel platform developed for this study has the capability of instant feedback or review of individual tackle technique during a training session. The platform has the added functionality to store and review tackle training sessions for a number of players which could be reviewed by the player and coach at a later date. The use of this novel coaching platform could facilitate vision training for tackle performance and may assist in developing the anticipation skills required for tackling and contact skills in rugby union. Vision training has been demonstrated to be a proposed concussion reduction strategy as it may assist in improving the ability to 'scan' the visual field for opponents and anticipate physical impacts [26]. The use of mobile applications and technology for sports coaching is well established. Technology and mobile applications can provide the means of gathering and analysing data which can then be translated into coaching practice for amateur coaches.

Limitations
There are limitations to the current research, even though it has demonstrated that data gathered from wearable sensors (IMUs) can increase the potential to correct tackle technique using a mobile coaching application. The focus of this initial stage of research was to demonstrate that IMUs and a mobile application could model an optimum safe tackle technique in training exercises by suggesting an optimum approach to the development of a safe tackle technique. In turn, this type of technology could assist a novice coach in developing a safer tackle posture for tacklers (i.e., 'head up and forward' posture). The current research could be further enhanced to identify specific causes of injury that occur with a poor tackle posture during tackle events in training and ultimately in live match play. As the game of rugby is a dynamic field-based sport, it is challenging to predict how tackles will be executed in real time. Consequently, the modelling in this proof of concept is constrained by limitations in the existing mobile application to adjust for non-optimum tackle angles. In many live tackle situations, the tackler may be unable to execute the optimum tackle angle. During these instances, the tackler may not be able to target the upper legs or lower trunk area of the ball carrier. This may mean that a tackler executes a tackle below the knees or above the waistline of the ball carrier. In these scenarios, these tackle angles would be outside the current parameters of ±10 • and deemed unsafe using the current mobile application. Future iterations of the mobile application may need a facility where the optimum tackle angle could be adjusted, as this may replicate a more 'real' reflection of tackling in live matches.
A further limitation of the current research is in respect to the ±10 • on tackle approach. The current research selected ±10 • based on existing kinematic rugby tackle data which suggested a safe tackle approach would target the upper leg and lower trunk. Using an initial zero angle of approach to the tackle and allowing ±10 • tolerance would mimic a would-be tacklers angle of approach to an oncoming ball carrier. The rationale for using ±10 • as an angle of approach as that it may assist in the 'head up and forward' motion for players to recall, and novice coaches to cue in coaching tackle safety. Equally, it could be suggested that ±15 • or other angle variations could be used as a safe tolerance; however, this was beyond the scope of the current paper. Using different tolerances (e.g., ±5 • , ±15 • , ±20 • ) may yield different results and assist in furthering the development of the current mobile application.
Future research development of this proof of concept will be to compare the current platform with video analysis as a means of reliability and validity testing. The current study demonstrated that it is possible to model static tackling posture characteristics in novice rugby players. The second phase will involve modelling live tackling events that measure proficient angles of approach to tackle technique and measurement of the forces during tackle impacts. This development will assist in the overall quality of coaching for tackle technique training in amateur rugby players and amateur coaches.
A final limitation of the present study was that all participants were male, which meant that the research team was unable to conclude if this prototype coaching platform may be suitable for female rugby players. This should be a notable consideration due to the differences in body mass and muscular distribution between male and female players in the amateur game of rugby union. This may lead to the development of alternate means of tackle technique coaching platforms and tackle proficiency strategies for females due to these parameters.

Conclusions
The objective of this study was to demonstrate a proof of concept for a novel tackle technique training platform using IMUs and a bespoke mobile application to capture the orientation of a youth rugby player's body during a tackle event. This study successfully demonstrated the development of a novel cross-platform software application for use in combination with IMUs. Wearable technology combined with the development of bespoke mobile applications has the potential to capture motion and potentially improve tackle technique proficiency to reduce injury risk in youth rugby. With advances in wearable technology, and associated reductions in cost, the development of novel mobile applications for improving tackle technique will enhance the potential for such applications to be widely utilised in amateur and community sport settings. In addition, this use of wearable technology and the developed bespoke mobile application could be utilised to monitor improper body position during activities of daily living, i.e., sitting posture or examining different physical strengths (asymmetries) of the left or right part of the body due to physiological or anatomical changes.  Informed Consent Statement: Informed consent was provided by all participants who took part in the current investigation.


Development of STEM-Based LKPD with Guided Inquiry Design to Improve Student’s Science Process Skills

This study aims to develop a STEM-based Student Worksheet (LKPD) through Guided Inquiry to Improve Students' Science Process Skills. This research and development method refers to the Borg & Gall model up to the 8th stage, namely a trial use that has been adapted to research needs, namely developing LKPD and seeing an increase in students’ science process skills after using LKPD in learning activities, with research subjects in class XI IPA 5 and XI. IPA 3, each totaling 24 students. The results obtained are: 1) the validation of the LKPD that has been developed is "very valid" with the percentage of material experts at 82.36% and the percentage of media experts being 82.44%; 2) the feasibility of the developed LKPD is very feasible with a percentage of 95.43%; 3) The LKPD developed is declared practical because the response questionnaires from students are categorized as practical 4) the results of students' responses to the use of "very good" trials with a percentage of 87.33%; 5) The effectiveness of LKPD is seen from the achievement of students' science process skills with an N-Gain value of 0.78 or with a percentage of 78% overall very good and increasing in every meeting. It was concluded that the development of STEM-based LKPD through Guided Inquiry was valid and feasible and could be used in the Physics learning process to improve Students' Science Process Skills on Thermodynamic Materials.

Introduction
The 2013 curriculum emphasizes the scientific method of learning, which is defined as helping students recognize and comprehend various materials through a scientific method. The information does not depend on the teacher or teacher alone; it can come from anywhere. Physics is taught as a tool for everyday problem-solving and for developing scientific thinking and behavior. To solve and respond critically and scientifically to natural phenomena and events [1], students are required to comprehend and interpret the information they have gained. The laws, theories, postulates, facts, concepts, and procedures that are consistent with the scientific method can be found in the physics field [2].
The Guided Inquiry learning model [3] is the learning model that can help students improve their science process skills by the characteristics of physics learning and the curriculum of 2013. It is possible to say that guided inquiry learning can improve students' way of thinking in understanding concepts in physics learning [4]. This learning model can develop students' scientific critical thinking by placing students as learners in solving a problem and obtaining investigative knowledge so that they can understand scientific concepts.
The guided inquiry learning model can engage students' active participation in the physics learning process in class and develop students' science process skills at high, medium, and low academic abilities [5].
According to the curriculum of 2013, STEM learning is one of the learnings that emphasizes the learning process so that students can use a scientific approach to comprehend various materials [6]. STEM education is the integration of science, technology, engineering, and mathematics (STEM) education, which has been suggested to aid in the achievement of 21 st -century skills [7]. Science process skills are skills that can be linked to STEM because students can learn them through the science section using STEM science process skills. Students need to have strong science process skills to be able to use the scientific method to find ideas or facts [8].
STEM is an integration between the four disciplines of science, technology, engineering, and mathematics using an interdisciplinary approach and is applied based on real-world contexts and problem-based learning [9]. STEM is an integrated learning approach that connects real-world applications with classroom learning that includes natural science (science), technology, engineering results, and mathematics [10]. STEM-based learning can improve students' science process skills in applying their knowledge to make designs as a form of problem-solving related to the environment and technology. Science process skills are a work process a person's brain is an important instrument for learning, considering, and analyzing what is good or bad [11].
Teacher teaching materials can incorporate the STEM approach in the form of LKPD. One way to facilitate teaching and learning activities so that students and teachers can interact effectively is through the Student Worksheet (LKPD). Students and educators alike may be able to overcome obstacles through the creation of STEM-based LKPD. The Lesson Plan (RPP) can be implemented using LKPD in general as a complement or support tool [12].To help students develop their process skills, teachers should prepare worksheets for their students (LKPD). These worksheets can be used to present learning through experiments or other methods. Student activity that teaches them to put their newly acquired knowledge into practice is the Student Activity Sheet. The Student Activity Sheet plays a crucial role in assessing how well students retain and apply the information presented. The four fields of science, technology, engineering, and mathematics comprise the developed LKPD. It's the potential to enhance students' science process skills, as well as their ability to think critically and creatively in the technology, engineering, and mathematics section [13]. Thermodynamics is one of the materials that are difficult to comprehend but serves as the foundation for numerous scientific fields. The laws of thermodynamics are fundamental to everyday life and education. Teaching effective thermodynamics is essential because it is a fundamental science that must be thoroughly comprehended. Understanding the difficulties students face with the concept of thermodynamics and how the concept can be presented through the use of LKPD as educational materials is a good place to start [14].
Despite the availability of laboratory facilities in schools, physics instruction rarely makes use of practicum methods. This is even though there are numerous difficulties students face when learning physics, such as memorizing solid physics material and counting, as well as learning physics in a classroom setting that is not contextual and is not balanced with the selection of teaching materials during classroom learning activities [15]. The teacher also rarely makes connections between the material being studied and everyday concepts during the learning process. The aforementioned facts are comparable to the findings of interviews and observations conducted with physics subject teachers at one of the public schools in Lhokseumawe, SMAN Modal Bangsa Arun.
This finding shows that students do not yet understand science process skills. Additionally, the instructor stated that students had never utilized physics concepts-based STEM worksheets. According to previous research [16], the STEM integrated approach, which is based on guided inquiry on temperature and heat, was successfully utilized as teaching material. The critical thinking abilities of students can be enhanced by LKPD developed using the STEM approach. Worksheets based on STEM can be used effectively as a supporting learning resource to improve students' creative thinking skills [17].
This issue motivates researchers to develop STEM-based LKPD through Guided Inquiry to Enhance Students' Science Process Skills to carry out research. The following are the primary goals of this study to develop effective STEM-based worksheets that can enhance students' science process skills through the use of guided inquiry. This study only looks at thermodynamics material from class XI. The fact that this study examines not only the development of a product but also the improvement and efficacy, which is tested on a large scale after the product has been developed, sets it apart from previous studies. In addition, the developed LKPD is based on STEM and uses a guided inquiry model, which makes it easy for students to understand and is expected to improve students' science process skills, which is important in the 21 st century learning process.

Method
This research using research & development (R&D) Borg and Gall model, consist of: potential problems, collecting data, designing the product, validating the design, revising the design, testing the product, revising the product, testing the use, and mass production [18]. However, the researcher in this study only restricted the research phase to the use trial phase, which was tailored to the objectives and requirements of the study.
This research on the development of LKPD was carried out at SMAN Modal Bangsa Arun in North Aceh, during the even semester of the 2021/2022 academic year. Each of the 24 students in the experimental and control classes of XI IPA 5 and XI IPA 3 received STEM-based LKPD objects through Guided Inquiry on Thermodynamics material as the research subjects. Validation and student response questionnaires were the methods of data collection used.
The results of this study include both quantitative and qualitative data. Quantitative data in the form of evaluation results and scores of responses to assessment questionnaires from experts in design, material, and physics.While the developed STEM-based worksheet responses and suggestions from material experts, design experts, physics teachers, and students comprise the qualitative data, The descriptive data analysis that was carried out in this study consisted of two types of data analysis: data analysis for the results of the validation questionnaire and data analysis for the results of the teacher questionnaire and student responses that were designed and summarized in the form of a Likert scale table for material experts, design experts, and physics teachers. The data analysis technique was divided into these two types of data analysis. Calculating the level of validity/ feasibility using percentage agreement with the equation 1.
(1) with, P = percentage of eligibility, f = total scores of data collection result, and N = ideal score. For expert validation is then presented according to the criteria 76% ≤ P ≤ 100% (valid), and 0% ≤ P ≤ 39% (invalid) [17]. Furthermore, to find out the response of students to the LKPD developed from the LKPD, using the persentage agreement equation 1 with the same criteria.
The results of the calculation of student response questionnaires were analyzed using the assessment criteria in Table 1.

Result and Discussion
A group of experts, including three instructors of physics education, have looked over the LKPD's results. Based on what the expert team talked about, it's hoped that the student worksheets can be used to learn thermodynamics and physics.
The outcomes of developing STEM-based worksheets has design to the thermodynamic materials and class XI students' characteristics. This STEM-based LKPD has a science section on simple thermodynamic material experiments that has been adapted to the guided inquiry syntax. The objectives of learning, problem formulation, hypothesis formulation, identifying variables, tools, and materials, as well as experimental steps, results and discussion, data analysis, and conclusions are all included in this section.
Students are required to examine how thermodynamics is applied to everyday technology in the technology section of technology, engineering, and mathematics. Students are instructed to draw or design using tools, materials, and work steps tailored to the thermodynamic material as part of engineering. Students are required to analyze the given questions in the mathematics section.
The Borg and Gall model is referred to until the eighth stage in the findings of the research on the development of STEM-Based Student Worksheets (LKPD) through Guided Inquiry. The following is a description of the outcomes that were obtained from each step of using Guided Inquiry to create STEM-based Student Worksheets (LKPD): a. Potential Issues: Teachers and students at SMAN Modal Bangsa Arun were interviewed and observed to determine the level of potential issues in this study. b. After the issue has been identified, data collection is carried out. This data serves as the basis for product planning, which is intended to address the issue. The data that was gathered consisted of information regarding teacher requirements for existing and desired teaching materials, as well as class XI physics content, particularly content on thermodynamics. c. The STEM-based LKPD (Science, Technology, Engineering, and Mathematics) design of thermodynamic materials for class XI SMA/MA consists of an introduction, a content section, and a conclusion. The content feasibility, linguistic, presentation, and graphic aspects of LKPD are then used to measure it. d. Product Design Validation, product design validation is carried out by media and material expert validators following the completion of the initial product design. On the questionnaire that the researcher has provided, validators are asked to fill it out and make suggestions regarding the quality of the materials and media used in the products that have been developed. e. Product Revision: The product that was developed based on the expert team's suggestions for improvement will be revised after it has been validated by material and media experts.
f. Product Trial: The next step is a small-scale product trial. Ten students from class XII IPA 5 at SMAN Modal Bangsa Arun, who have previously studied Thermodynamics, participate in the test at this point. g. Product Revision: At this point, the STEM-based LKPD is revised taking into account the stage. The use trial, which is carried out on a large scale, is the subsequent stage. At this point, 24 students from the experimental class XI IPA 5 at SMAN Modal Bangsa Arun participated in the STEM-based LKPD through Guided Inquiry.
The STEM-based LKPD validation test according to material experts is based on aspects of content quality assessment, students' science process skills, presentation quality, and language quality. The results of the calculation of LKPD validation according to material experts can be seen in the Table 2.  Table 2 shows the average results of expert's judgement of 4.15 with a percentage of 82.36% included in the valid category, which means it is feasible to use. The percentage of the total validation results by material experts on STEM-based worksheets that have been developed can be expressed by the following Figure 1.

Figure 1. Expert Judgement Result
The results of the STEM-based LKPD validation according to media experts are based on aspects of LKPD size assessment, cover design, content presented by Table 3 Table 3 shows the average results of the media expert's assessment, which is 4.12 with a percentage of 82.44% in the valid category, which means it is feasible to use. The percentage of total expert judgement to validation results on STEM-based worksheets that have been developed can be expressed by the Figure 2.  Table 2 and Table 3 which contain the results of the validity of the LKPD show that the three aspects of the LKPD validation sheet show valid categories. It can be said that the LKPD developed is feasible to be tested, according to [18] which states that an LKPD is said to be feasible if it meets three conditions, namely didactic requirements, construction requirements, and technical requirements.
Didactic requirements are requirements related to the fulfillment of the principles of effective learning in an LKPD. Construction requirements are requirements related to language, while technical requirements are requirements related to writing based on established rules. The results of the didactic criteria aspect obtained a valid category, where this criterion was according to the didactic requirements, namely the requirements related to effective learning principles in an LKPD, this was according to what was said by [3]. Meanwhile, in the construction aspect, it obtained a valid category, which was also according to what were the requirements of the construction requirements where the language used in this STEM-based LKPD was according to the Indonesian language rules. This is according to what was stated by [8], namely that LKS must be arranged in an attractive format and design so that it is easy to read and that students can understand the activities in the LKPD.
The construction requirements in the LKPD include providing sufficient space to give students the flexibility to write answers or draw on the LKPD. The results of the validation on the LKPD developed as a whole are categorized as valid and have very high reliability. The results of the validation on the LKPD which are categorized as valid state that the developed LKPD has met the standards of a good LKPD component, and it can be said that the developed LKPD can produce data on the achievement of students' science process skills correctly [19]. Assessment by the teacher serves to assess the appearance of the LKPD from various aspects including content quality, students' science process skills, presentation quality, linguistic quality, and LKPD appearance. The following are the results of the feasibility test from the teacher experts judgement on the LKPD presented by Table 4.  Table 4 shows the average result of the feasibility test by the teacher, which is 4.76 with a percentage of 95.42% in the very good category, which means that it is suitable for use in schools in the learning process. The percentage of total teacher expert validation results on STEM-based LKPDs that have been developed can be expressed by Figure 3. The results of student responses that were tested on class XII IPA 5 students, totaling 10 students, were seen based on the results of the student response questionnaires which can be seen on Table 5 Table 5 shows the average results of student responses, namely 4.03 with a percentage of 87.33% in the very good category, which means that it is suitable for use in schools in physics learning activities. The percentage of the total aspect of the test results on the use of the product can be seen on Figure 4. Based on the test of calculating the N-Gain score in the experimental class, it shows that the average value of the N-Gain score for the experimental class using STEM-based LKPD through Guided Inquiry on Thermodynamics material is 0.78 or in a percentage of 78% it with high category. While the test for calculating the N-Gain score in the control class shows that the average value of the N-Gain score for the experimental class using non-STEM-based LKPD through Guided Inquiry on Thermodynamics material is 0.62 or in a percentage of 62% with medium category. The results of the increase in N-Gain seen in the Thermodynamics material were obtained from the results of the pretest and posttest in each class, namely class XI IPA 5 as the experimental class and class XI IPA 3 as the control class. In the experimental class, the highest pretest score was 45 and the lowest score was 15, while in the control class the highest pretest score was 40 and the lowest score was 15. The pretest scores in the experimental class and control class has a different averages, for control class has a same average; higher score 28.75 while the average value of the pretest in the experimental class is 29.37.
Learning activities carried out using STEM-based LKPD through Guided Inquiry were developed to require students to carry out scientific activities through observation, making hypotheses, and conducting practicals as well as linking the concepts obtained with everyday life. Improving Science Process Skills in learning can be seen in the ability of students to propose hypotheses, apply concepts that have just been learned in new situations, can determine tools and materials, determine what is 83 observed, work steps, conduct experiments, and can determine how to process data from observations, tables, and diagrams, as well as compiling and submitting systematic and clear reports.

Conclusion
The development of STEM-based LKPD through Guided Inquiry that has been developed obtained the validation results of material experts by 2.36% and the percentage of media experts by 82.44%. The results of the feasibility test obtained a percentage of 95.43%, and the results of student responses in the use trial with a percentage of 87.33% so that it can be categorized as valid and feasible as a learning medium. The use of LKPD in the learning process as one of the stages of research and aims to see an increase in students' Students' Science Process Skills shows an N-Gain value of 0.78 with a percentage of 78% entering the high category. So it can be concluded that the STEM-based LKPD through Guided Inquiry produced is categorized as feasible as a learning medium and can be used in learning and can be used in physics learning to improve students' Students' Science Process Skills on thermodynamic material.