A conference paper Presented in 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), 26-28 September, Dhaka, Bangladesh

1. Human Activity Recognition Using Multichannel
Convolutional Neural Network
[Conference Presentation]
Contributing Authors
Niloy Sikder
M.Sc. Student
CSE Discipline
Khulna University, Khulna
niloysikder333@gmail.com
Md. Sanaullah Chowdhury
B.Sc. Student
ECE Discipline
Khulna University, Khulna
sanaullahashfat@gmail.com
Dr. Abdullah Al Nahid
Associate Professor
ECE Discipline
Khulna University, Khulna
nahid.ece.ku@gmail.com
Abu Shamim Mohammad Arif
Professor
CSE Discipline
Khulna University, Khulna
shamimarif@yahoo.com
Presenter
Niloy Sikder

2. Sep 27, 2019 ICAEE 2019 1
Human Action Signal
Fig. 1: Sensors integrated in a smartphone[1]
 A time-domain signal that is produced when an activity is performed
 Typically recorded using an Accelerometer and a Gyroscope.
 Accelerometer records the relative acceleration in the three-dimensions
 Gyroscope records the relative angular momentum in the three-dimensions

3. 2
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Examples of HAR Signals
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X
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X
Fig. 2: A sample HAR signal of walking

4. 3
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Examples of HAR Signals (cont.)
Fig. 3: A sample HAR signal of climbing up stairs
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Z
X
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X

5. 4
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Examples of HAR Signals (cont.)
Fig. 4: A sample HAR signal of climbing down stairs
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Z
X
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X

6. 5
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Examples of HAR Signals (cont.)
Fig. 5: A sample HAR signal of standing
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Z
X
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X

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Examples of HAR Signals (cont.)
Fig. 6: A sample HAR signal of sitting
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X

8. 7
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Examples of HAR Signals (cont.)
Fig. 7: A sample HAR signal of laying
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X

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Why Teach the Machine?
 To develop software or devices to detect and record these movements
 The associated database will show the amount of time spent in each activity
 The moves can be replicated with an artificial device
Why Keep a Database?
 Performing some of the activities (like laying and sitting) for a longer
period of time is harmful
 Keeping track can help us to adjust our behavior or habits and reduce the
risk of developing various conditions

10. 9
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Proposed HAR Classification Model
Fig. 8: The proposed HAR classification model
Raw Activity
Signal
Frequency
Features
Power
Features
Feature Extraction Classification
CNN
1st
Channel
CNN
2nd
Channel
Classification
Result
Concatenation

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A Cat Waveform
Fig. 9: Outline of a cat
Fig. 10: Frequency analysis of the cat waveform

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Extracting Features from HAR Signals
Fig. 12: tSNE of the UCI HAR dataset based on time-
domain signals
Fig. 11: A Typical HAR signal

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Extracting Features from HAR Signals (cont.)
Fig. 14: tSNE of the UCI HAR dataset based on
frequency-domain signals
Fig. 13: Frequency-domain representation a typical HAR signal

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Fig. 16: tSNE of the UCI HAR dataset based on power
information
Fig. 15: Power features of a typical HAR signal
Extracting Features from HAR Signals (cont.)

15. 14
Channel_1 input
Class
Label
Frequency Information
Body_acc Body_gyro Total_acc
Body_acc Body_gyro Total_acc
Raw HAR Data
Power Information
Body_acc Body_gyro Total_acc
Channel_2 input
X-axis
Y-axis
Z-axis
Processing of a HAR signal
Fig. 17: Processing of a HAR signal
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16. 15
Multichannel CNN Model for HAR Classification
Fig. 18: A Multichannel CNN Model for HAR Classification
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HAR
Frequency
Features
HAR
Power
Features
Kernel: 2×2, ‘Relu’
Featuremap: 16
Kernel: 2×2, ‘Relu’
Featuremap: 16 Kernel: 2×2,
Max-pooling
Dense
Layer
Concatenation
Classification
Output
Input
1
st
Channel
2
nd
Channel

17. Fig. 19: HAR classification accuracy at each epoch
Experimental Results
Fig. 20: F1-Score at each epoch
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18. 17
Experimental Results (cont.)
Fig. 22: AUC-ROC
Fig. 21: Confusion matrix of the last epoch of the classification
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19. 18
Comparison with State-of-the-art Methods
Method Our Anguita
2012
Anguita
2013
Almaslukh
2017
Bhattacharjee
2018
Bota 2019
Accu
racy
(%)
Wlk 97.38 95.61 99.19 97.8 97.78 84.84
WUp 94.90 69.85 95.75 97.7 92.56 80.83
WDn 95.48 83.22 97.62 98.1 98.75 80.80
Sit 87.17 92.96 87.98 98.9 96.77 79.46
Stn 96.24 96.43 97.37 92.5 87.08 80.69
Lay 99.81 100 100 100 99.81 80.86
Total 95.25 89.35 96.37 97.5 95.08 81.24
Precision (%) 95.32 89.93 96.58 97.3 94.99 81.66
Recall (%) 95.16 89.68 96.32 97.5 95.46 81.25
F1-score (%) 95.24 89.8 96.45 97.4 95.22 81.45
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20. Sep 27, 2019 ICAEE 2019 19
Scopes for Future Studies
 Test the affectivity of the method on other HAR datasets
 Deal with the lower accuracy score of the “Sitting” class

21. Thank You
Any Questions?