ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. sEMG Data Description
3.2. Data Preparation
3.3. Signal Preprocessing
- Baseline wander removal: Baseline wander refers to low-frequency fluctuations in the sEMG signal caused by breathing, slight movements, or other non-electrophysiological sources. By using a bandpass filter with a minimum frequency of 5 Hz, the baseline wander is attenuated, reducing its influence on the signal [45].
- Power line noise removal: Electrical devices and power lines emit electrical signals at the power line frequency, which can contaminate the sEMG signal. In many regions, the power line frequency is 60 Hz. The bandpass filter with a notch at 60 Hz effectively removes this power line noise from the signal [46].
- External environment noise removal: sEMG signals can also be affected by external noise sources, such as electromagnetic interference or muscle noise from adjacent muscle groups. The bandpass filter with its selected frequency range helps to minimize the impact of these external noise sources [47].
- Frequency range selection: the bandpass filter retains frequencies between 5 Hz and 500 Hz, which are typically relevant for capturing muscle activity signals while excluding frequencies outside this range that might not be related to the desired sEMG data.
- Filter order: A filter’s order of 3 determines the sharpness of the filter’s roll-off characteristics. A higher-order filter allows for a steeper reduction in frequencies outside the passband, providing more effective noise removal [48].
3.4. Analyzing the ResNet1D Model for Personal Identification
4. Results and Discussion
4.1. Model Performance Metrics
4.2. Use-Case Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
sEMG | Surface electromyography |
DB | Database |
DNA | Deoxyribonucleic Acid |
EMG | Electromyography |
GRU | Gated Recurrent Unit |
NF | Notch filter |
LSTM | Long Short-Term Memory |
CNN | Convolutional Neural Network |
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Subject IDs | Recall | Precision | F1-Score |
---|---|---|---|
0 | 0.92 | 1.00 | 0.96 |
1 | 0.97 | 0.95 | 0.96 |
2 | 1.00 | 1.00 | 1.00 |
3 | 0.97 | 0.95 | 0.96 |
4 | 1.00 | 0.97 | 0.99 |
Accuracy | - | - | 0.97 |
Macro avg | 0.97 | 0.97 | 0.97 |
Weighted avg | 0.97 | 0.97 | 0.97 |
0 | 0.98 | 0.94 | 0.96 |
1 | 1.00 | 0.93 | 0.97 |
2 | 1.00 | 0.97 | 0.99 |
3 | 0.90 | 1.00 | 0.95 |
4 | 0.96 | 1.00 | 0.98 |
5 | 0.90 | 1.00 | 0.95 |
6 | 0.85 | 0.97 | 0.90 |
7 | 1.00 | 0.90 | 0.95 |
8 | 0.95 | 1.00 | 0.97 |
9 | 1.00 | 0.89 | 0.94 |
Accuracy | - | - | 0.96 |
Macro avg | 0.95 | 0.96 | 0.96 |
Weighted avg | 0.96 | 0.96 | 0.96 |
Subject IDs | Recall | Precision | F1-Score |
---|---|---|---|
0 | 0.92 | 0.87 | 0.89 |
1 | 0.98 | 0.98 | 0.98 |
2 | 1.00 | 0.80 | 0.89 |
3 | 0.50 | 1.00 | 0.67 |
4 | 0.86 | 1.00 | 0.92 |
5 | 0.60 | 1.00 | 0.75 |
6 | 0.74 | 1.00 | 0.85 |
7 | 0.84 | 0.84 | 0.84 |
8 | 1.00 | 0.97 | 0.99 |
9 | 0.76 | 1.00 | 0.87 |
10 | 1.00 | 0.54 | 0.70 |
11 | 1.00 | 0.74 | 0.85 |
12 | 0.93 | 0.97 | 0.95 |
13 | 0.96 | 0.88 | 0.92 |
14 | 0.87 | 0.93 | 0.90 |
Accuracy | - | - | 0.87 |
Macro avg | 0.86 | 0.90 | 0.86 |
Weighted avg | 0.90 | 0.87 | 0.87 |
0 | 0.89 | 0.62 | 0.73 |
1 | 1.00 | 1.00 | 1.00 |
2 | 0.97 | 0.49 | 0.65 |
3 | 0.57 | 1.00 | 0.73 |
4 | 0.73 | 1.00 | 0.85 |
5 | 0.64 | 1.00 | 0.78 |
6 | 0.93 | 0.87 | 0.90 |
7 | 0.67 | 1.00 | 1.00 |
8 | 1.00 | 1.00 | 1.00 |
9 | 0.51 | 1.00 | 0.68 |
10 | 0.94 | 0.78 | 0.85 |
11 | 0.77 | 0.83 | 0.80 |
12 | 1.00 | 0.91 | 0.96 |
13 | 0.87 | 0.95 | 0.91 |
14 | 0.86 | 0.94 | 0.90 |
15 | 0.95 | 0.67 | 0.79 |
16 | 0.90 | 1.00 | 0.95 |
17 | 0.91 | 0.97 | 0.94 |
18 | 1.00 | 0.67 | 0.80 |
19 | 0.39 | 1.00 | 0.56 |
Accuracy | - | - | 0.82 |
Macro avg | 0.83 | 0.88 | 0.83 |
Weighted avg | 0.88 | 0.82 | 0.83 |
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Ganiga, R.; S. N., M.; Choi, W.; Pan, S. ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration. Sensors 2024, 24, 3140. https://doi.org/10.3390/s24103140
Ganiga R, S. N. M, Choi W, Pan S. ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration. Sensors. 2024; 24(10):3140. https://doi.org/10.3390/s24103140
Chicago/Turabian StyleGaniga, Raghavendra, Muralikrishna S. N., Wooyeol Choi, and Sungbum Pan. 2024. "ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration" Sensors 24, no. 10: 3140. https://doi.org/10.3390/s24103140
APA StyleGaniga, R., S. N., M., Choi, W., & Pan, S. (2024). ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration. Sensors, 24(10), 3140. https://doi.org/10.3390/s24103140