Please use this identifier to cite or link to this item:
|Title:||Muscle force estimation with surface EMG during dynamic muscle contractions: A wavelet and ANN based approach|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 13, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.