Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks
Abstract
:1. Introduction
2. Participants and Methods
2.1. Participants
2.2. Method
2.2.1. Study Design
2.2.2. Bafa Wubu Formulation in Tai Chi Exercise
2.2.3. IMU Data Acquisition and Processing
2.2.4. Neural Network Construction
2.3. Deployment on Mobile Devices
2.3.1. WeChat Applet Deployment
2.3.2. Server-Side Deployment
2.3.3. Exercise Intervention Program
2.3.4. Health Outcome Measures
2.4. Statistical Analysis
3. Results
4. Discussion
4.1. Advantage
4.2. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Precision Intervention Group (n = 21) | Standard Intervention Group (n = 29) | Skilled Tai Chi Practitioner (n = 70) | p-Value for Differences between Groups | p-Value for Normality Test | |
---|---|---|---|---|---|
Age | 65.2 ± 4.2 | 64.9 ± 3.7 | 36.1 ± 3.5 | N/A | N/A |
Height (cm) | 162.85 ± 6.53 | 164.43 ± 7.82 | 169.64 ± 11.85 | N/A | N/A |
Weight (kg) | 69.31 ± 6.92 | 70.87 ± 7.88 | 75.48 ± 7.11 | N/A | N/A |
Blance (s) | 8.61 ± 4.21 | 7.06 ± 3.13 | N/A | 0.14 | 0.33/0.07 |
Gripstrength (kg) | 36.17 ± 4.82 | 33.69 ± 6.88 | N/A | 0.16 | 0.97/0.75 |
SF-12 | 30.39 ± 1.61 | 30.07 ± 4.68 | N/A | 0.76 | 0.42/0.20 |
BDI | 19.13 ± 8.03 | 19.92 ± 7.07 | N/A | 0.71 | 0.53/0.98 |
Motion | True Count | False Count | Accuracy |
---|---|---|---|
WOF | 174 | 26 | 87.0% |
LF | 167 | 35 | 82.6% |
RBB | 181 | 15 | 92.3% |
PB | 187 | 11 | 94.4% |
PPS | 176 | 29 | 85.8% |
ELS | 177 | 15 | 92.1% |
SKR | 173 | 34 | 83.6% |
Precision Intervention Group (n = 21) | Standard Intervention Group (n = 29) | Interaction | ||||||
---|---|---|---|---|---|---|---|---|
Pre | Post | P | Pre | Post | P | P | η2p | |
Blance (s) | 8.61 ± 4.21 | 10.72 ± 3.50 | 0.010 | 7.06 ± 3.13 | 7.21 ± 2.18 * | 0.136 | 0.059 | 0.075 |
Gripstrength (kg) | 36.17 ± 4.82 | 39.78 ± 3.22 | 0.000 | 33.69 ± 6.88 | 38.98 ± 5.15 | 0.000 | 0.002 | 0.196 |
SF-12 | 30.39 ± 1.61 | 31.46 ± 2.57 | 0.005 | 30.07 ± 4.68 | 30.87 ± 5.94 | 0.005 | 0.670 | 0.004 |
BDI | 19.13 ± 8.03 | 16.06 ± 3.44 | 0.000 | 19.92 ± 7.07 | 17.90 ± 5.90 | 0.001 | 0.241 | 0.030 |
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Li, X.; Zou, L.; Li, H. Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks. Sensors 2024, 24, 4208. https://doi.org/10.3390/s24134208
Li X, Zou L, Li H. Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks. Sensors. 2024; 24(13):4208. https://doi.org/10.3390/s24134208
Chicago/Turabian StyleLi, Xiongfeng, Limin Zou, and Haojie Li. 2024. "Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks" Sensors 24, no. 13: 4208. https://doi.org/10.3390/s24134208
APA StyleLi, X., Zou, L., & Li, H. (2024). Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks. Sensors, 24(13), 4208. https://doi.org/10.3390/s24134208