Please use this identifier to cite or link to this item: https://doi.org/10.1109/EMBC.2013.6610569
Title: Muscle force estimation with surface EMG during dynamic muscle contractions: A wavelet and ANN based approach
Authors: Bai, F.
Chew, C.-M. 
Issue Date: 2013
Source: Bai, F.,Chew, C.-M. (2013). Muscle force estimation with surface EMG during dynamic muscle contractions: A wavelet and ANN based approach. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS : 4589-4592. ScholarBank@NUS Repository. https://doi.org/10.1109/EMBC.2013.6610569
Abstract: Human muscle force estimation is important in biomechanics studies, sports and assistive devices fields. Therefore, it is essential to develop an efficient algorithm to estimate force exerted by muscles. The purpose of this study is to predict force/torque exerted by muscles under dynamic muscle contractions based on continuous wavelet transform (CWT) and artificial neural networks (ANN) approaches. Mean frequency (MF) of the surface electromyography (EMG) signals power spectrum was calculated from CWT. ANN models were trained to derive the MF-force relationships from the subset of EMG signals and the measured forces. Then we use the networks to predict the individual muscle forces for different muscle groups. Fourteen healthy subjects (10 males and 4 females) were voluntarily recruited in this study. EMG signals were collected from the biceps brachii, triceps, hamstring and quadriceps femoris muscles to evaluate the proposed method. Root mean square errors (RMSE) and correlation coefficients between the predicted forces and measured actual forces were calculated. © 2013 IEEE.
Source Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
URI: http://scholarbank.nus.edu.sg/handle/10635/73660
ISBN: 9781457702167
ISSN: 1557170X
DOI: 10.1109/EMBC.2013.6610569
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