Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
Abstract
:1. Introduction
- We verify the feasibility of non-contact sensing using USRP by exploiting AI for post-surgery activities detection, especially weight lifting activity. To the great of our knowledge, this is the first work using universal software-defined radio peripheral (USRP) to extract WCSI for healthcare purposes and to monitor patients’ activities.
- We suggest a dynamic feature extraction technique of WCSI, which efficiently decreases the influence of environmental changes.
- We only depend on training data derived in ordinary situations. We collect data when a patient is normally lifting things and feed them into an algorithm to avoid collecting the data after when the patient had surgery, which is hard to collect in practical use.
- We measured the execution of this technique in an actual environment, which we perform in our lab. The experimental outcomes show that the post-surgery activities monitoring testbed has high detection performance in a line of sight (LOS) as well as non-line-of sight (NLOS) scenario.
2. Related Work
2.1. Traditional Health Care Techniques
2.2. Using Wi-Fi Communication Signals for Sensing
3. Materials and Methods
3.1. Hardware Functionality
3.2. Software Functionality
3.2.1. Transmitter Process
3.2.2. Receiver Process
3.3. Classification Approach
4. Experimental Setup
5. Results and Discussion
- I.
- Correct and wrong activity magnitude response in the frequency domain.
- II.
- Accuracy of the machine-learning algorithms model.
5.1. Correct and Wrong Activity Magnitude Response in the Frequency Domain
5.2. Accuracy of the Machine Learning Algorithms Model
- Fine decision tree (FDT)
- Linear discriminant analysis (LDA)
- Linear support vector machine (LSVM)
- Fine k-nearest neighbor (FKNN)
- Ensemble boosted trees (EBT)
6. Conclusions and Future Recommendations
Author Contributions
Funding
Conflicts of Interest
References
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Abbreviation | Description | Abbreviation | Description |
---|---|---|---|
ADC | Analog to Digital Converter | LOS | Line of Sight |
AI | Artificial Intelligence | LPA | Log Periodic Antenna |
CP | Cyclic Prefix | LPF | Low Pass Filter |
DAC | Analog Conversion | LSVM | Linear Support Vector Machine |
DAC | Digital to Analog Converter | NLOS | Non-Line-of Sight |
DC | Direct Current | OFDM | Orthogonal Frequency Division Multiplexing |
DDC | Digital down Converter | PCA | Principal Component Analysis |
DUC | Digital up Conversion | QPSK | Quadrature Phase Shift Keying |
EBT | Ensemble Boosted Trees | RF | Radio Frequency |
FDT | Fine Decision Tree | RGB | Red Green Blue |
FFT | Fast Fourier Transform | RSS | Received Signal Strength |
FKNN | Fine K-Nearest Neighbor | TA | Transmit Amplification |
IFFT | Inverse Fast Fourier Transform | ToF | Time of Flight |
ISCOS | International Spinal Cord Society | USRP | Universal Software-Defined Radio Peripheral |
ISI | Inter Symbol Interference | WCSI | Wireless Channel State Information |
LDA | Linear Discriminant Analysis | WHO | World Health Organization |
Testbed | USRP B210 |
---|---|
Antenna | Omni-directional |
Device Frequency Range | 70 MHz–6 GHz |
Channel Mapping Rx | 1 |
Channel Mapping Tx | 1 |
Center Frequency | 5.32 GHz |
Clock Source & PPS Source | Internal |
Master Clock Rate | 200 MHz |
Interpolation Factor | 250 |
Enable Burst mode | False |
Transport data type | int16 |
Decimation Factor | 250 |
Output data type | Same as transport data type |
Transmitter serial number | 30AD2FE |
Receiver serial number | 30AD311 |
Transmitter Gain | 70 |
Receiver Gain | 50 |
Samples per frames | 80 |
Parameter | Values/Type |
---|---|
Input Bits | 104 |
Bits per Symbols (M) | 2 |
Modulation type | QPSK |
OFDM subcarriers | 64 |
Data subcarriers | 52 |
Null subcarriers | 11 |
DC | 1 |
Used subcarriers | 52 |
NFFT | 64 |
Cyclic prefix | NFFT - Data subcarriers |
Sampling Frequency | 80,000 |
Samples per frames | 80 |
Sr.No | Subject | Body structure | Height (cm) | Weight (Kg) |
---|---|---|---|---|
1 | Male | Ectomorph | 168 | 55 |
2 | Male | Endomorph | 180 | 95 |
3 | Female | Mesomorph | 168 | 60 |
4 | Male | Mesomorph | 174 | 76 |
5 | Male | Ectomorph | 176 | 60 |
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Al-hababi, M.A.M.; Khan, M.B.; Al-Turjman, F.; Zhao, N.; Yang, X. Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence. Appl. Sci. 2020, 10, 4886. https://doi.org/10.3390/app10144886
Al-hababi MAM, Khan MB, Al-Turjman F, Zhao N, Yang X. Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence. Applied Sciences. 2020; 10(14):4886. https://doi.org/10.3390/app10144886
Chicago/Turabian StyleAl-hababi, Mohammed Ali Mohammed, Muhammad Bilal Khan, Fadi Al-Turjman, Nan Zhao, and Xiaodong Yang. 2020. "Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence" Applied Sciences 10, no. 14: 4886. https://doi.org/10.3390/app10144886
APA StyleAl-hababi, M. A. M., Khan, M. B., Al-Turjman, F., Zhao, N., & Yang, X. (2020). Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence. Applied Sciences, 10(14), 4886. https://doi.org/10.3390/app10144886