A Real-Time Muscle Fatigue Detection System Based on Multifrequency EIM and sEMG for Effective NMES

被引:3
作者
Fernandez Schrunder, Alejandro D. [1 ]
Huang, Yu-Kai [1 ]
Rodriguez, Saul [1 ]
Rusu, Ana [1 ]
机构
[1] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, Stockholm, Sweden
关键词
Muscles; Fatigue; Biomedical monitoring; Spectroscopy; Sensors; Real-time systems; Monitoring; Application-specific integrated circuit (ASIC); bioimpedance (bio-Z) spectroscopy; closed-loop neuromuscular electrical stimulation (NMES); electrical impedance myography (EIM); multimodal sensing; muscle fatigue; surface electromyography (sEMG); ELECTRICAL-IMPEDANCE MYOGRAPHY; SURFACE ELECTROMYOGRAPHY; TISSUE BIOIMPEDANCE; STIMULATION; HEALTHY; TORQUE; WIRELESS; EXERCISE;
D O I
10.1109/JSEN.2024.3409821
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Neuromuscular electrical stimulation (NMES) is a self-directed home-based therapeutic tool in early rehabilitation for musculoskeletal (MSK) conditions. However, the effectiveness of traditional NMES is fundamentally constrained by muscle fatigue. To address this limitation, this work proposes a detection system, which simultaneously records multifrequency electrical impedance myography (EIM) and surface electromyography (sEMG) in real-time for time-frequency analysis of muscle activation, contraction, and fatigue. To demonstrate the ability to monitor these muscle physiological states, two experiments involving weightless and weighted dynamic contractions of the biceps brachii muscle were performed. Results from these experiments show synchronous changes in sEMG and EIM spectra during contractions and clear trends in sEMG's mean power frequency (MPF) and EIM spectra with fatigue progression. In addition, the configurable four-channel NMES has been electrically evaluated for clinical use, demonstrating the feasibility of the proposed system for closed-loop stimulation. This work showcases the potential of sEMG and multifrequency EIM to enhance the effectiveness of NMES for MSK conditions by capturing the behavior of distinct mechanisms of muscle fatigue.
引用
收藏
页码:22553 / 22564
页数:12
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