Robust and Lightweight Decoder for Unsupervised Multi-Finger Force Predictions Towards the Internet of Medical Things-based Applications

Finger force monitoring has become increasingly prevalent in the field of the Internet of Medical Things (IoMT) as a key indicator of muscle strength and health status, facilitating remote rehabilitation and personalized health monitoring. However, existing methods are limited by inaccurate decoding performance or complex procedures when derived in a supervised manner. To address these challenges, we developed a novel unsupervised approach featuring a robust and lightweight neural-drive decoder for multi-finger force predictions. High-density surface electromyogram (sEMG) signals were recorded from the finger extensor muscles during isometric finger extension tasks. Each MU was then assigned a probability indicating its association with the target finger, based on its mean firing rates during the activation periods of individual fingers. MUs with probabilities exceeding a predefined threshold were retained for the final force prediction. Our results demonstrate that the neural-drive decoder achieved a computation time of 68.83±13.63 ms, making it suitable for real-time applications. Furthermore, our decoder outperformed the sEMG-amplitude-based approach (R2: 0.79±0.039 vs. 0.64±0.080, root mean square error: 4.89±0.73 vs. 7.31±1.88% of maximum force, Pearson correlation coefficient: 0.87±0.028 vs. 0.76±0.06, mean absolute error: 3.86±0.62 vs. 6.08±1.51 % of maximum force). The developed neural decoder demonstrated advantages over the state-of-the-art neural decoders in terms of accuracy, training procedures, and practicality. Additionally, our approach exhibited robust performance across various probability thresholds, data sources, and background noise, highlighting its potential for finger force monitoring applications in diverse IoMT scenarios.