Pervasive Lying Posture Tracking
Abstract
:1. Introduction
2. Background & Related Studies
2.1. Multi-Sensor Lying Posture Tracking
2.2. Single-Sensor Lying Posture Tracking
3. Methodologies
3.1. Data Preparation
3.2. Traditional Lying Posture Tracking
3.2.1. Feature Preparation
3.2.2. Ensemble Model Learning
3.3. Deep Lying Posture Tracking
4. Experimental Evaluation
4.1. Datasets & Preprocessing
4.1.1. Class-Act: Datasets from a Human Posture/Activity Classification
4.1.2. Daily and Sports Activities Dataset (Das)
4.1.3. Integrated Dataset
4.2. Comparison Metrics and Implementation Details
5. Results
5.1. Raw Data Inspection
5.2. Traditional Machine Learning
5.2.1. Feature Engineering
5.2.2. Lying Posture Detection
5.3. Deep Sequence Learning
5.4. Deep Learning vs. Traditional Machine Learning
5.5. Comparison with the State-Of-The-Art
- ET is the proposed feature-based classifier, which is an ensemble of decision trees trained on 48 time-domain features.
- AdaLSTM is the proposed deep learning model, which is an adaptive long short-term memory network with Adam optimizer and decaying learning rate.
- LDA, as proposed by Zhang et al., is a linear discriminate analysis (LDA) classifier trained on the mean value of the signal [11].
- SVM, as proposed by Jeng et al., is a multi-class linear kernel support vector machine classifier trained on the mean value of the tri-axial accelerometer signal [31].
- LSTM is a long short-term memory network with the same structure as the AdaLSTM, but with a fixed learning rate of 0.01.
5.5.1. Class-Act Dataset
5.5.2. Integrated Dataset
6. Discussion
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
LSTM | Long Short-Term Memory |
Bi-LSTM | Bidirectional Long Short-Term Memory |
AdaLSTM | Adaptive Long Short-Term Memory |
ML | Machine Learning |
DL | Deep Learning |
RNN | Recurrent Neural Network |
DAS | Daily and Sports |
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Feature | Description | Computation for Signal S | Number |
---|---|---|---|
AMP | Peak amplitude | 1–3 | |
MED | Median | median(S) | 4–6 |
MEAN | Mean value | 7–9 | |
MAX | Maximum value | 10–12 | |
MIN | Minimum value | 13–15 | |
VAR | Variance | 16–18 | |
STD | Standard deviation | 19–21 | |
RMS | Root mean square | 22–24 | |
P2P | Peak to peak | 25–27 | |
ZCR | Zero crossing rate | 28–30 | |
ENT | Entropy | 31–33 | |
SKN | Skewness | 34–36 | |
KRT | kurtosis | 37–39 | |
MAG | Mean Magnitude | 40 | |
ENG | Energy | 41 | |
RNG | Range | 42–44 | |
ANG | Angle | 45 | |
MAD | Mean absolute deviation | 46–48 |
Location | Accuracy | Balanced Accuracy | Score |
---|---|---|---|
Left Thigh | 94.5± 6.9 | 91.3 ± 10.3 | 90.7 ± 11.8 |
Right Thigh | 96.2 ± 8.1 | 94.4 ± 12.0 | 93.5 ± 14.4 |
Left Ankle | 94.9 ± 8.5 | 92.1 ± 12.8 | 91.4 ± 15.6 |
Right Ankle | 89.8 ± 13.5 | 82.9 ± 19.9 | 82.8 ± 22.7 |
Chest | 96.2 ± 9.1 | 93.6 ± 13.7 | 93.6 ± 16.2 |
Left Arm | 78.6 ± 11.7 | 62.9 ± 15.1 | 60.9 ± 16.6 |
Right Arm | 89.5 ± 12.1 | 84.0 ± 18.3 | 81.6 ± 21.7 |
Left Wrist | 78.6 ± 12.5 | 67.1 ± 19.1 | 64.1 ± 21.7 |
Right Wrist | 80.7 ± 14.1 | 79.7 ± 21.3 | 67.9 ± 23.8 |
Location | Accuracy | Balanced Accuracy | Score |
---|---|---|---|
Left Thigh | 98.9 ± 8.2 | 98.4 ± 5.2 | 98.2 ± 6.2 |
Right Thigh | 95.9 ± 7.3 | 93.4 ± 11.8 | 91.5 ± 15.6 |
Left Ankle | 97.9 ± 4.2 | 96.8 ± 6.3 | 96.9 ± 6.4 |
Right Ankle | 94.5 ± 6.3 | 92.4 ± 9.4 | 91.7 ± 10.7 |
Chest | 98.3 ± 7.1 | 97.4 ± 7.1 | 97.3 ± 7.3 |
Left Arm | 77.6 ± 11.7 | 68.8 ± 14.1 | 66.3 ± 16.5 |
Right Arm | 86.8 ± 9.2 | 79.0 ± 14.5 | 75.7±17.3 |
Left Wrist | 64.8 ± 22.9 | 64.9 ± 24.8 | 62.9 ± 23.2 |
Right Wrist | 66.8 ± 26.7 | 67.6 ± 26.2 | 66.9 ± 28.9 |
Location | ET | LDA | SVM | LSTM | AdaLSTM | |
---|---|---|---|---|---|---|
Mean Value (%) | Left thigh | 90.7 | 95.4 | 95.4 | 92.5 | 98.2 |
Right thigh | 93.5 | 96.1 | 93.2 | 84.8 | 91.5 | |
Left ankle | 92.1 | 88.3 | 94.8 | 90.2 | 96.9 | |
Right ankle | 82.9 | 90.0 | 89.5 | 83.7 | 91.7 | |
Chest | 97.0 | 94.8 | 90.1 | 88.3 | 97.3 | |
Left arm | 60.9 | 58.1 | 53.3 | 53.7 | 66.3 | |
Right arm | 81.6 | 82.2 | 76.1 | 75.5 | 75.7 | |
Left wrist | 64.1 | 55.0 | 50.7 | 51.6 | 64.0 | |
Right wrist | 67.9 | 65.5 | 59.2 | 54.1 | 69.4 | |
Coefficient of variation | Left thigh | 0.13 | 0.15 | 0.13 | 0.22 | 0.06 |
Right thigh | 0.15 | 0.16 | 0.17 | 0.28 | 0.17 | |
Left ankle | 0.17 | 0.18 | 0.16 | 0.24 | 0.06 | |
Right ankle | 0.27 | 0.18 | 0.12 | 0.27 | 0.11 | |
Chest | 0.17 | 0.16 | 0.26 | 0.15 | 0.07 | |
Left arm | 0.27 | 0.33 | 0.29 | 0.36 | 0.24 | |
Right arm | 0.26 | 0.22 | 0.24 | 0.23 | 0.22 | |
Left wrist | 0.33 | 0.36 | 0.36 | 0.50 | 0.25 | |
Right wrist | 0.35 | 0.32 | 0.29 | 0.36 | 0.31 |
Location | ET | LDA | SVM | LSTM | AdaLSTM | |
---|---|---|---|---|---|---|
Mean Value(%) | Left thigh | 90.6 | 94.6 | 91.4 | 92.9 | 93.7 |
Right thigh | 97.3 | 96.9 | 91.4 | 93.2 | 94.0 | |
Chest | 95.4 | 95.4 | 96.7 | 90.7 | 95.0 | |
Left wrist | 65.9 | 42.1 | 58.4 | 54.1 | 63.3 | |
Right wrist | 78.6 | 66.7 | 74.3 | 42.1 | 69.2 | |
Coefficient of variation | Left thigh | 0.19 | 0.14 | 0.25 | 0.09 | 0.21 |
Right thigh | 0.13 | 0.15 | 0.20 | 0.16 | 0.17 | |
Chest | 0.13 | 0.13 | 0.24 | 0.25 | 0.12 | |
Left wrist | 0.39 | 0.50 | 0.50 | 0.53 | 0.42 | |
Right wrist | 0.34 | 0.38 | 0.32 | 0.40 | 0.39 |
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Alinia, P.; Samadani, A.; Milosevic, M.; Ghasemzadeh, H.; Parvaneh, S. Pervasive Lying Posture Tracking. Sensors 2020, 20, 5953. https://doi.org/10.3390/s20205953
Alinia P, Samadani A, Milosevic M, Ghasemzadeh H, Parvaneh S. Pervasive Lying Posture Tracking. Sensors. 2020; 20(20):5953. https://doi.org/10.3390/s20205953
Chicago/Turabian StyleAlinia, Parastoo, Ali Samadani, Mladen Milosevic, Hassan Ghasemzadeh, and Saman Parvaneh. 2020. "Pervasive Lying Posture Tracking" Sensors 20, no. 20: 5953. https://doi.org/10.3390/s20205953
APA StyleAlinia, P., Samadani, A., Milosevic, M., Ghasemzadeh, H., & Parvaneh, S. (2020). Pervasive Lying Posture Tracking. Sensors, 20(20), 5953. https://doi.org/10.3390/s20205953