A Multimodal Platform for Adaptive Functional Electrical Stimulation Cycling

Abstract

Functional Electrical Stimulation (FES) cycling is typically operated in open-loop, which does not allow adaptation to muscle fatigue, the main factor limiting performance and therapeutic benefit. To support the development of fatigue-aware closed-loop strategies, we have developed a synchronized multimodal experimental platform integrating HD-EMG, instrumented pedals, optical motion capture, crank-angle sensing, and a certified multi-channel stimulator. A custom hardware trigger provides sub-millisecond synchronization across all devices, while a Python-based real-time interface manages data acquisition, safety functions, and online stimulation adjustments. Unlike previous setups, this platform provides high-resolution neuromuscular and mechanical measurements, essential for fatigue characterization and evaluation of stimulation strategies. Across three test sessions, the mean angular error between the delivered and EMG-measured activation patterns was 9.50° ± 2.35° (31.7 ± 7.8 ms at 50 RPM), remaining well below the maximum measured synchronization delay of 46.7 ms and confirming accurate temporal alignment.