Abstract:
Electrical muscle stimulation (EMS) can restore or increase function in skeletal muscle by stimulating motor neurons through adhesive skin electrodes, which can greatly increase the quality of life for those suffering from paralysis. However, the unnatural muscle fiber recruitment pattern from EMS causes muscles to fatigue and lose force rapidly, which limits the utility. Although technologies such as external exoskeletons exist to supplement muscle function, there is an open need for muscle fatigue feedback to gauge muscle strength in freely moving humans for EMS applications, as the current gold standard for measuring muscle fatigue, surface electromyography (sEMG), is generally incompatible. I believe that Doppler ultrasound will provide a better solution with fast (millisecond) time resolution, depth resolved measurements, and compatibility with EMS. Further, Doppler ultrasound provides information that is both independent of and complementary to sEMG and has utility outside of EMS applications in the fields of biomechanics, sports science, rehabilitation, and beyond.In this work, I explored signs of muscle fatigue and recovery induced by EMS using tissue Doppler imaging (TDI) with a bulky commercial ultrasound machine to establish the feasibility of this method. With this setup, I found that recovery in muscle twitch toque can be predicted from features of the average tissue velocity waveform, and that this model is subject specific. Next, for a portable solution, I performed experiments using a low power continuous wave (CW) ultrasound probe. The CW probe is portable, low power, and has much lower computational and memory requirements than the commercial TDI machine. For these experiments, I observed that the duration of audio signal at the onset of muscle contraction correlates with the peak join torque (a proxy for muscle force), and that audio signals are generated at both the onset and the release of muscle contraction in both EMS and voluntary movements. Finally, I conclude this study with multi-scale muscle modeling using a modified Hill-type model to help understand and explain our observations and predict muscle behavior.
