Bai, A.; Song, H.; Wu, Y.; Dong, S.; Feng, G.; Jin, H.
Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation. Sensors 2025, 25, 1275.
https://doi.org/10.3390/s25041275
AMA Style
Bai A, Song H, Wu Y, Dong S, Feng G, Jin H.
Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation. Sensors. 2025; 25(4):1275.
https://doi.org/10.3390/s25041275
Chicago/Turabian Style
Bai, Aoyang, Hongyun Song, Yan Wu, Shurong Dong, Gang Feng, and Hao Jin.
2025. "Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation" Sensors 25, no. 4: 1275.
https://doi.org/10.3390/s25041275
APA Style
Bai, A., Song, H., Wu, Y., Dong, S., Feng, G., & Jin, H.
(2025). Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation. Sensors, 25(4), 1275.
https://doi.org/10.3390/s25041275
