An EMG Signal Processing System for Control of an Ankle-foot Orthosis

An EMG Signal Processing System for Control of an Ankle-foot Orthosis. Gmerek, A. J., M., D., Meskin, N., & Jaber, F In CoDIT 2017, International Conference on Control, Decision and Information Technologies, 2017.
abstract bibtex

This paper describes an EMG-based digital processing system which estimates force and direction of motion that can be used in control frameworks of ankle-foot orthoses (AFOs). To control an AFO in a reliable way it is usually necessary to estimate ankle's muscles voluntary contraction (VC) and user's intention of motion. Thus, a system was created that can designate these parameters from EMG signals (also known as myopotentials or myosignals). The voluntary contraction was calculated based on the root mean square (RMS) of EMG signals, and a direction of motion was estimated by using a feature-based classifier. The experiments were performed on five healthy, sitting subjects and research includes a selection of discriminative features and comparison of basic classifiers. The results of the study showed that the direction of motion could be estimated in the real time with high accuracy.

@InProceedings{emg_ankle_orthosis,
  author    = {Gmerek, A. J. and Davoodi M. and Meskin, N. and Jaber, F},
  title     = {An {EMG} {S}ignal {P}rocessing {S}ystem for {C}ontrol of an {A}nkle-foot {O}rthosis},
  booktitle = {Co{DIT} 2017, {I}nternational {C}onference on {C}ontrol, {D}ecision and {I}nformation {T}echnologies},
  year      = {2017},
  abstract  = {This paper describes an EMG-based digital processing system which estimates force and direction of motion that can be used in control frameworks of ankle-foot orthoses (AFOs). To control an AFO in a reliable way it is usually necessary to estimate ankle's muscles voluntary contraction (VC) and user's intention of motion. Thus, a system was created that can designate these parameters from EMG signals (also known as myopotentials or myosignals). The voluntary contraction was calculated based on the root mean square (RMS) of EMG signals, and a direction of motion was estimated by using a feature-based classifier. The experiments were performed on five healthy, sitting subjects and research includes a selection of discriminative features and comparison of basic classifiers. The results of the study showed that the direction of motion could be estimated in the real time with high accuracy.},
}

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