Analysis Of A Low-Cost Sensor Towards An Emg-Based Robotic Exoskeleton Controller

Abstract

This paper describes the evaluation of the MyoWare Electromyographic (EMG) sensor performance during a typical end-use application to help determine if it could be used for an EMG-based controller of an upper-limb robotic exoskeleton. Tests were conducted to study the signal-to-noise ratio (SNR) and a series of experiments were performed to determine the sensor’s capability of capturing key EMG signal features while a subject performed bicep curls. LabVIEW was used for data collection and processing, and Matlab was used for statistical analysis. The results revealed that the SNR was between 10dB and 33dB for the average peak root mean square (RMS) EMG, and between 1dB and 27dB for the average voluntary contraction (AVC) EMG which – except for one case – were all above the acceptable level in the field. The validation of the sensor performance showed a correlation consistent with literature between the force exerted and the RMS EMG signal under both dynamic and static loading. These initial results indicate that the MyoWare EMG sensor could be used in a more advanced robotic exoskeleton EMG-based controller beyond its current popular use as an EMG-level threshold-based ON/OFF switch.