title: Towards Real-Time Estimation of Muscle-Fiber Conduction Velocity Using Delay-Locked Loop.
abstract: Decrease in muscle-fiber conduction velocity (MFCV) during sustained contraction has been widely accepted as myoelectric manifestation of muscle fatigue. Several methods have been proposed in the literature for MFCV estimation by analysing surface electromyography (EMG), e.g., cross-correlation (CC) function and maximum likelihood (ML). However, for all the availablemethods, windowing of the EMG signal and computationally demanding calculations are required, limiting the possibility to continuously monitor muscle fatigue in real time. In the present study, an adaptive scheme is proposed that permits real-time estimation of MFCV. The proposed scheme is based on a delay-lockedloop (DLL). Asecond-orderloop is adopted to track the delay variationover time. An error filter is employed to approximate a ML estimation in case of colored noise. Furthermore, the DLL system is extended for multichannel CV estimation. The performance of the proposed method is evaluated by both dedicated simulations and real EMG signals. Our results show the accuracy of the proposed method to be comparable to that of theML method formuch lower (1/40) computational complexity, especially suited for real-time MFCV measurements. Use of this method can enable new studies onmyoelectric fatigue, possibly leading to new insight on the underlying physiological processes.
authors.forename: L
authors.initials: L
authors.lastname: Xu
authors.forename: C
authors.initials: C
authors.lastname: Rabotti
authors.forename: M
authors.initials: M
authors.lastname: Mischi
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D003199:Computer Systems; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009119:Muscle Contraction / Q000502:physiology*; D018485:Muscle Fibers, Skeletal / Q000502:physiology*; D009431:Neural Conduction / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio