Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques
Abstract
:1. Introduction
- A novel system leveraging non-contact and physiological techniques is proposed, enabling the continuous monitoring of pervasive biomedical signals for long-term stress detection.
- Hybrid DL networks and models for rPPG signal reconstruction and Heart Rate (HR) estimation to significantly improve accuracy and efficiency in stress detection up to 95.83% with the UBFC-Phys dataset.
- Extensive experiments and empirical evaluations of Deep Learning (DL) models for stress detection provide valuable insights and comparisons.
2. Related Work
3. Method
3.1. Dataset and Data Processing
3.2. Deep Learning Models
3.3. Performance Evaluation
4. Experimental Results
4.1. Classification Results
4.1.1. Performance Analysis of the DL Methods Applied to the GT Signal
4.1.2. Performance Analysis of the DL Methods Applied to the rPPG Signal
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Description |
---|---|
Window | The number of consecutive video frames processed to estimate the physiological signal. |
Holistic | Skin extraction technique that sets the stage for calculating the RGB trace, which is achieved by calculating the average intensity of facial skin colour for each channel separately. |
Convexhull | A skin extractor that subtracts the eyes and mouth regions from the rest of the entire face. It offers dependable real-time face and landmark detection and tracking. |
CuPy CHROM | A chrominance-based method used to infer the pulse signal from the RGB traces built with the CuPy Python library designed for GPU-accelerated computing with open-source arrays. |
Torch CHROM | Built with PyTorch, which is an open-source ML framework that facilitates building, training, and deploying DL models through a dynamic computational graph. |
Cupy POS | Plane POS is another method also used to infer the pulse signal from RGB traces, but from a projection plane that is perpendicular to the skin tone built with the CuPy library. |
DL Method | # Layers | Layer (Type) | Output Shape | Param # | Total Params | Trainable Params | Non-Trainable Params |
---|---|---|---|---|---|---|---|
LSTMv1 | 3 | lstm | 11,519 × 64 | 16,896 | 22,097 | 22,097 | 0 |
lstm | 16 | 5184 | |||||
dense | 1 | ||||||
LSTMv2 | 4 | lstm | 11,519 × 64 | 16,896 | 32,465 | 32,465 | 0 |
lstm | 11,519 × 32 | 12,416 | |||||
lstm | 16 | 3136 | |||||
dense | 1 | 17 | |||||
GRUv1 | 3 | gru | 11,519 × 64 | 12,864 | 16,817 | 16,817 | 0 |
gru | 16 | 3936 | |||||
dense | 1 | 17 | |||||
GRUv2 | 4 | gru | 11,519 × 64 | 12,864 | 24,689 | 24,689 | 0 |
gru | 11,519 × 32 | 9408 | |||||
gru | 16 | 2400 | |||||
dense | 1 | 17 | |||||
1D-CNNv1 | 5 | conv1d | 11,517 × 64 | 256 | 1,480,001 | 1,480,001 | 0 |
max_pooling | 5758 × 64 | 0 | |||||
conv1d | 5756 × 32 | 6167 | |||||
max_pooling | 5756 × 32 | 0 | |||||
flatten | 92,096 | 0 | |||||
dense | 16 | 1,473,552 | |||||
dense | 1 | 17 | |||||
1D-CNNv2 | 7 | conv1d | 5744 × 512 | 16896 | 2,765,441 | 2,765,441 | 1792 |
max_pooling | 1436 × 512 | 0 | |||||
batch_normalisation | 1436 × 512 | 2048 | |||||
conv1d | 1429 × 256 | 1,048,832 | |||||
max_pooling | 357 × 256 | 0 | |||||
batch_normalisation | 357 × 356 | 1024 | |||||
conv1d | 350 × 128 | 262,272 | |||||
max_pooling | 87 × 128 | 0 | |||||
batch_normalisation | 87 × 128 | 512 | |||||
flatten | 11136 | 0 | |||||
dense | 128 | 1,425,536 | |||||
dropout | 128 | 0 | |||||
dense | 64 | 8256 | |||||
dropout | 64 | 0 | |||||
dense | 1 | 65 | |||||
1D-CNNv3 | 7 | conv1d | 57,44 × 512 | 16,896 | 4,199,169 | 4,199,169 | 1792 |
max_pooling | 1436 × 512 | 0 | |||||
batch_normalisation | 1436 × 512 | 2048 | |||||
conv1d | 1429 × 256 | 1,048,832 | |||||
max_pooling | 357 × 256 | 0 | |||||
batch_normalisation | 357 × 256 | 1024 | |||||
conv1d | 350 × 128 | 262,272 | |||||
max_pooling | 87 × 128 | 0 | |||||
batch_normalisation | 87 × 128 | 512 | |||||
flatten | 11,136 | 0 | |||||
dense | 256 | 2,851,072 | |||||
dropout | 256 | 0 | |||||
dense | 64 | 16,448 | |||||
dropout | 64 | 0 | |||||
dense | 1 | 65 |
DL Method | Domain | Epochs | Accuracy | Precision | Recall | F1-Score | Time [s] |
---|---|---|---|---|---|---|---|
1D-CNNv2 | time | 100 | 83.33% | 83.33% | 83.33% | 83.33% | 28.08 |
LSTMv1 | time | 100 | 79.17% | 100.00% | 58.33% | 73.68% | 122.62 |
GRUv1 | time | 50 | 79.17% | 81.82% | 75.00% | 78.26% | 60.68 |
GRUv1 | time | 100 | 79.17% | 81.82% | 75.00% | 78.26% | 119.67 |
1D-CNNv3 | time | 50 | 79.17% | 81.82% | 75.00% | 78.26% | 15.21 |
LSTMv2 | time | 50 | 75.00% | 87.50% | 58.33% | 70.00% | 92.04 |
LSTMv2 | time | 100 | 75.00% | 87.50% | 58.33% | 70.00% | 182.08 |
GRUv2 | time | 50 | 75.00% | 80.00% | 66.67% | 72.73% | 89.58 |
GRUv2 | time | 100 | 75.00% | 80.00% | 66.67% | 72.73% | 175.43 |
1D-CNNv1 | time | 50 | 75.00% | 87.50% | 58.33% | 70.00% | 5.48 |
1D-CNNv1 | time | 100 | 75.00% | 87.50% | 58.33% | 70.00% | 7.62 |
1D-CNNv3 | time | 100 | 75.00% | 100.00% | 50.00% | 66.67% | 28.49 |
1D-CNNv3 | frequency | 100 | 75.00% | 100.00% | 50.00% | 66.67% | 28.74 |
LSTMv1 | time | 50 | 70.83% | 100.00% | 41.67% | 58.82% | 62.28 |
GRUv1 | frequency | 100 | 66.67% | 61.11% | 91.67% | 73.33% | 119.30 |
LSTMv1 | frequency | 50 | 62.50% | 57.89% | 91.67% | 70.97% | 61.96 |
GRUv2 | frequency | 100 | 62.50% | 57.89% | 91.67% | 70.97% | 175.16 |
LSTMv1 | frequency | 100 | 58.33% | 55.00% | 91.67% | 68.75% | 122.32 |
GRUv1 | frequency | 50 | 58.33% | 56.25% | 75.00% | 64.29% | 60.47 |
1D-CNNv1 | frequency | 100 | 54.17% | 52.63% | 83.33% | 64.52% | 7.49 |
LSTMv2 | frequency | 100 | 50.00% | 50.00% | 100.00% | 66.67% | 181.87 |
GRUv2 | frequency | 50 | 50.00% | 50.00% | 8.33% | 14.29% | 89.23 |
1D-CNNv1 | frequency | 50 | 50.00% | 50.00% | 100.00% | 66.67% | 4.24 |
1D-CNNv2 | time | 50 | 50.00% | 50.00% | 100.00% | 66.67% | 19.75 |
1D-CNNv2 | frequency | 50 | 50.00% | 50.00% | 8.33% | 14.29% | 15.42 |
1D-CNNv2 | frequency | 100 | 50.00% | 50.00% | 8.33% | 14.29% | 28.14 |
1D-CNNv3 | frequency | 50 | 50.00% | 50.00% | 8.33% | 14.29% | 15.07 |
LSTMv2 | frequency | 50 | 41.67% | 41.67% | 41.67% | 41.67% | 92.44 |
Work | PPG Method | ML-Method | Accuracy |
---|---|---|---|
This work | contact | 1D-CNN-MLP | 83.33% |
remote | 95.83% | ||
UBFC-Phys [29] | contact | SVM-linear kernel | 73.00% |
remote | SVM-RBF kernel | 85.38% | |
Stress detection using PPG signal and combined deep CNN-MLP network [56] | contact | CNN-MLP | 82.00% |
pyVHR Method | DL-Method | Version | Aug. | Domain | Epochs | Accuracy | Precision | Recall | F1-Score | Time |
---|---|---|---|---|---|---|---|---|---|---|
CuPy_CHROM | LSTM | v1 | inter | freq | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 59.4 |
v2 | none | freq | 100 | 83.33% | 90.00% | 75.00% | 81.82% | 9.8 | ||
GRU | v2 | none | freq | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 6.3 | |
v1 | none | freq | 50 | 79.17% | 76.92% | 83.33% | 80.00% | 4.6 | ||
1D-CNN | v1 | wn | freq | 100 | 95.83% | 100.00% | 91.67% | 95.65% | 7.8 | |
v2 | inter | freq | 50 | 95.83% | 100.00% | 91.67% | 95.65% | 14.5 | ||
v3 | inter | time | 50 | 91.67% | 100.00% | 83.33% | 90.91% | 15.0 | ||
Torch_CHROM | LSTM | v1 | inter | freq | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 59.6 |
v2 | none | freq | 50 | 83.33% | 90.00% | 75.00% | 81.82% | 6.6 | ||
GRU | v3 | wn | freq | 100 | 83.33% | 78.57% | 91.67% | 84.62% | 114.7 | |
v2 | none | freq | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 6.5 | ||
1D-CNN | v3 | inter | time | 50 | 95.83% | 92.31% | 100.00% | 96.00% | 15.1 | |
v2 | inter | freq | 100 | 91.67% | 100.00% | 83.33% | 90.91% | 27.6 | ||
v1 | none | freq | 50 | 87.50% | 84.62% | 91.67% | 88.00% | 2.4 | ||
CuPy_POS | LSTM | v2 | none | freq | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 6.0 |
v1 | inter | time | 50 | 79.17% | 76.92% | 83.33% | 80.00% | 59.2 | ||
GRU | v1 | none | time | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 5.3 | |
v1 | none | time | 100 | 83.33% | 83.33% | 83.33% | 83.33% | 9.7 | ||
1D-CNN | v1 | inter | time | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 14.8 | |
v3 | wn | time | 50 | 83.33% | 83.33% | 83.33% | 83.33% | 14.5 | ||
v1 | none | freq | 50 | 79.17% | 73.33% | 91.67% | 81.48% | 2.1 |
pyVHR Method | dl_Method | Aug. | Domain | Epochs | Ac | Pr | Re | F1 | Time (s) |
---|---|---|---|---|---|---|---|---|---|
CuPy_CHROM | 1D-CNNv3 | inter | frequency | 50 | 1 | 1 | 1 | 1 | 14.59 |
CuPy_CHROM | 1D-CNNv3 | inter | frequency | 100 | 1 | 1 | 1 | 1 | 27.00 |
Torch_CHROM | 1D-CNNv2 | inter | frequency | 50 | 1 | 1 | 1 | 1 | 14.44 |
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Fontes, L.; Machado, P.; Vinkemeier, D.; Yahaya, S.; Bird, J.J.; Ihianle, I.K. Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques. Sensors 2024, 24, 1096. https://doi.org/10.3390/s24041096
Fontes L, Machado P, Vinkemeier D, Yahaya S, Bird JJ, Ihianle IK. Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques. Sensors. 2024; 24(4):1096. https://doi.org/10.3390/s24041096
Chicago/Turabian StyleFontes, Laura, Pedro Machado, Doratha Vinkemeier, Salisu Yahaya, Jordan J. Bird, and Isibor Kennedy Ihianle. 2024. "Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques" Sensors 24, no. 4: 1096. https://doi.org/10.3390/s24041096