Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement
Abstract
:1. Introduction
2. System Overview
2.1. Hardware System
2.1.1. Antenna
2.1.2. Transceiver
2.1.3. Serial Port Module
2.2. Algorithm Design
2.2.1. Classification Algorithm
2.2.2. Physiological Signal Algorithm
3. Experiment
- (1)
- According to experimental physiology and electromagnetism, it could be known that others will greatly affect the accuracy of the experiment when measuring the equipment. Therefore, the experimental environment with less affected should be a better option.
- (2)
- When the system is turned on and off, it will affect the generated waveform to some extent. In addition, when the system is filtered in the frequency domain, it will also affect the waveform at the edge. Therefore, during the verification test of cardiac signal and respiratory signal extraction, more than one minute of data should be collected in the experiment, and the middle one minute of data should be used for calculation, so as to avoid the influence of the above phenomenon on the experimental results.
- (3)
- Try to ensure the diversity of samples when collecting classified actions, that is, each action has data collected by 10 subjects.
- (4)
- In the verification test of cardiac signal and respiratory signal extraction, try to ensure that the range of each breath of the subject is the same. This verification can be generally judged by the waveform form collected by the contact device.
4. Results and Discussion
4.1. Effective Distance
4.2. Action Classification
4.3. Physiological Signal Extraction
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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A | B | C | D | E | F | |
---|---|---|---|---|---|---|
A | 0.75 | 0.04 | 0.03 | 0.12 | 0.02 | 0.06 |
B | 0.11 | 0.79 | 0.02 | 0.07 | 0.03 | 0.05 |
C | 0.02 | 0.04 | 0.83 | 0.04 | 0.01 | 0.04 |
D | 0.09 | 0.04 | 0.03 | 0.76 | 0 | 0.01 |
E | 0.01 | 0.06 | 0.05 | 0.01 | 0.82 | 0.10 |
F | 0.02 | 0.03 | 0.04 | 0 | 0.12 | 0.74 |
A | B | C | D | E | F | |
---|---|---|---|---|---|---|
A | 0.91 | 0 | 0.01 | 0 | 0 | 0.04 |
B | 0.01 | 0.96 | 0 | 0 | 0.01 | 0 |
C | 0.08 | 0.02 | 0.99 | 0 | 0.01 | 0.01 |
D | 0 | 0 | 0 | 1 | 0 | 0.01 |
E | 0 | 0.02 | 0 | 0 | 0.76 | 0.30 |
F | 0 | 0 | 0 | 0 | 0.22 | 0.64 |
[16] | Proposed System | |
---|---|---|
Maximum distance | 1 m | 0.4 m |
Methods of classification | SVM | VGG-16 and LSTM |
Classification accuracy | 85% | >88% |
Error of respiratory detection | 13% | 0.779 beats/min |
Error of Heart rate detection | No experiment | 3.49 beats/min |
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Liang, Q.; Xu, L.; Bao, N.; Qi, L.; Shi, J.; Yang, Y.; Yao, Y. Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement. Biosensors 2019, 9, 58. https://doi.org/10.3390/bios9020058
Liang Q, Xu L, Bao N, Qi L, Shi J, Yang Y, Yao Y. Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement. Biosensors. 2019; 9(2):58. https://doi.org/10.3390/bios9020058
Chicago/Turabian StyleLiang, Qiancheng, Lisheng Xu, Nan Bao, Lin Qi, Jingjing Shi, Yicheng Yang, and Yudong Yao. 2019. "Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement" Biosensors 9, no. 2: 58. https://doi.org/10.3390/bios9020058
APA StyleLiang, Q., Xu, L., Bao, N., Qi, L., Shi, J., Yang, Y., & Yao, Y. (2019). Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement. Biosensors, 9(2), 58. https://doi.org/10.3390/bios9020058