<p>after reading some literature in the area I'd recommend the following:</p>
<ul>
<li>Try using Convolution Neural Networks (CNNs) <a href="https://wiki.tum.de/download/attachments/242090417/IMU_Sensor_Fusion_With_ML.pdf?version=1&amp;modificationDate=1562329774913&amp;api=v2" rel="nofollow noreferrer">this</a> paper outlines some really good points on why should you use CNNs.</li>
<li>Try a Combination of the different layers in the same model. Start with some Convolution Layers, then some LSTMs and then a couple of Dense Layers followed by Dropout</li>
</ul>

CC BY-SA 4.0
2020-08-27T00:14:38.397Z
2020-08-27T00:14:38.397Z
Saurav Maheshkar
<p>Currently, I'm working on 6-axis IMU(Inertial Measurment Unit) dataset. <a href="https://www.kaggle.com/dasmehdixtr/carla-driver-behaviour-dataset" rel="nofollow noreferrer">This dataset</a> contain 6 axis IMU data of 7 different drivers. The Imu sensor attached on vehicle. The drivers drives same path. So, the dataset include 6 feature columns and a label column.</p>
<p>I tried multiple neural network models.The sensor data is a sequential data so I tried LSTM(Long Short Term Memory) &amp; classical fully-connected layers.
Some of my architecture(in keras framework):</p>
<hr />
<pre><code>Layer (type)                 Output Shape              Param #   

lstm_4 (LSTM)                (None, 1, 128)            69120     
_________________________________________________________________
lstm_5 (LSTM)                (None, 1, 64)             49408     
_________________________________________________________________
lstm_6 (LSTM)                (None, 1, 32)             12416     
_________________________________________________________________
dense_8 (Dense)              (None, 1, 64)             2112      
_________________________________________________________________
dropout_2 (Dropout)          (None, 1, 64)             0         
_________________________________________________________________
dense_9 (Dense)              (None, 1, 7)              455       


2nd Architecture:

=================================================================
dense_10 (Dense)             (None, 32)                224       
_________________________________________________________________
dense_11 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_12 (Dense)             (None, 128)               8320      
_________________________________________________________________
dense_13 (Dense)             (None, 256)               33024     
_________________________________________________________________
dropout_3 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_14 (Dense)             (None, 512)               131584    
_________________________________________________________________
dense_15 (Dense)             (None, 256)               131328    
_________________________________________________________________
dense_16 (Dense)             (None, 128)               32896     
_________________________________________________________________
dense_17 (Dense)             (None, 64)                8256      
_________________________________________________________________
dropout_4 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_18 (Dense)             (None, 128)               8320      
_________________________________________________________________
dense_19 (Dense)             (None, 7)                 903     
</code></pre>
<p>The best accuracy in my models was %70 which is not good. How style of layers should I use to handle this data? Or, which type of model would increase accuracy?</p>

<deep-learning><recurrent-neural-networks><long-short-term-memory><deep-neural-networks>
What type of model should I fit to increase accuracy?
dasmehdix
# What type of model should I fit to increase accuracy?

(Asked by: username_0 on 2020-08-26)

Currently, I'm working on 6-axis IMU(Inertial Measurment Unit) dataset. This dataset contain 6 axis IMU data of 7 different drivers. The Imu sensor attached on vehicle. The drivers drives same path. So, the dataset include 6 feature columns and a label column.
I tried multiple neural network models.The sensor data is a sequential data so I tried LSTM(Long Short Term Memory) & classical fully-connected layers.
Some of my architecture(in keras framework):

Layer (type)                 Output Shape              Param #   

lstm_4 (LSTM)                (None, 1, 128)            69120     
_________________________________________________________________
lstm_5 (LSTM)                (None, 1, 64)             49408     
_________________________________________________________________
lstm_6 (LSTM)                (None, 1, 32)             12416     
_________________________________________________________________
dense_8 (Dense)              (None, 1, 64)             2112      
_________________________________________________________________
dropout_2 (Dropout)          (None, 1, 64)             0         
_________________________________________________________________
dense_9 (Dense)              (None, 1, 7)              455       


2nd Architecture:

=================================================================
dense_10 (Dense)             (None, 32)                224       
_________________________________________________________________
dense_11 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_12 (Dense)             (None, 128)               8320      
_________________________________________________________________
dense_13 (Dense)             (None, 256)               33024     
_________________________________________________________________
dropout_3 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_14 (Dense)             (None, 512)               131584    
_________________________________________________________________
dense_15 (Dense)             (None, 256)               131328    
_________________________________________________________________
dense_16 (Dense)             (None, 128)               32896     
_________________________________________________________________
dense_17 (Dense)             (None, 64)                8256      
_________________________________________________________________
dropout_4 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_18 (Dense)             (None, 128)               8320      
_________________________________________________________________
dense_19 (Dense)             (None, 7)                 903     

The best accuracy in my models was %70 which is not good. How style of layers should I use to handle this data? Or, which type of model would increase accuracy?

**Tags:** deep-learning, recurrent-neural-networks, long-short-term-memory, deep-neural-networks


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## 1 answers

### Answer by username_1 on 2020-08-27. Score: 2 | Accepted answer

after reading some literature in the area I'd recommend the following:

* Try using Convolution Neural Networks (CNNs) [this](https://wiki.tum.de/download/attachments/242090417/IMU_Sensor_Fusion_With_ML.pdf?version=1&modificationDate=1562329774913&api=v2) paper outlines some really good points on why should you use CNNs.
* Try a Combination of the different layers in the same model. Start with some Convolution Layers, then some LSTMs and then a couple of Dense Layers followed by Dropout


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