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Updated: Jul 9, 2026

Assessment of Neuromuscular Function Using Percutaneous Electrical Nerve Stimulation
Published on: September 13, 2015
Effect of control frequency on closed-loop electrical muscle stimulation for biceps-triceps in healthy participants:
Yuki Kawawaki1, Shouren Huang2, Yuji Yamakawa3
1Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
Abstract:
The bandwidth of voluntary upper-limb movement is constrained by the inherent transmission and processing delays of the central nervous system (CNS). While spinal reflexes exhibit shorter latencies, the maximum achievable closed-loop control bandwidth of the musculoskeletal system remains poorly defined. We address this by introducing a high-speed closed-loop electrical muscle stimulation (EMS) system. By integrating high-speed machine vision with antagonistic stimulation of the biceps and triceps via a fuzzy logic controller, this platform enables closed-loop elbow joint actuation that bypasses voluntary CNS pathways to probe the fundamental limits of neuromuscular response. In this study, the feedback control frequency was synchronized with the electrical stimulation frequency. We evaluated the system using a dynamic target-tracking task (sinusoidal reference target with [Formula: see text] amplitude and frequency from 0.2 to 0.4 Hz) involving six healthy participants across four feedback and stimulation frequencies (10, 33, 100, and 333 Hz). Performance was quantified using Dynamic Target Tracking Accuracy (DTTA-MAE) and a Neural Biceps-Triceps Response model's mapping accuracy (NBTR-MAE). Our results show that both metrics improve with increasing feedback frequency. However, tracking accuracy (DTTA-MAE) exhibits no further improvement at 333 Hz compared to 100 Hz, whereas the mapping accuracy of the neural response model (NBTR-MAE) continues to improve up to 333 Hz. These findings suggest that the human neuromuscular system may adapt to and benefit from high-frequency feedback control far exceeding the rates of natural voluntary movement, providing critical insights for future applications of functional electrical stimulation (FES) systems.
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