Rehabilitation

We show that the effects of fatigue on muscle coordination and neural drive can be reliably characterized using a non-parametric functional muscle network. The network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. The proposed approach has the potential to be a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.

We explored the potential of a functional muscle network to differentiate vocal tasks. The network robustly differentiated vocal tasks, while classic muscle activation assessment failed to differentiate. The study also discovered tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation.

This project involved adapting a Desk Cycle to incorporate a controllable resistance mechanism and a tachometer, both interfaced with a Raspberry Pi Zero W using I2C and PWM protocols. The customized device enables wireless communication of exercise data to Simulink for analysis. The integration of mechanical design, control systems, and electronic communication demonstrates innovative solutions to enhance interactive locomotion exercises, aiming to improve neuromuscular engagement and potentially benefit balance and fall-risk assessment in elderly populations.