Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array

Abstract

The robustness and usability of pattern recognition based myoelectric control systems degrade significantly if the sensors are displaced during usage. This effect inevitably occurs during donning, doffing or using an upper-limb prosthesis over a longer period of time. Electrode shift has been previously studied but remains an unsolved problem. In this study we investigate if increasing the number of electrode channels and recording locations can improve the degraded classification accuracy caused by electrode shift. In our experiment we use a 96 channel high density electrode array to distinguish 11 different hand and wrist movements. Our results show that for electrode shifts up to 1 cm an array of about 32 sensors in combination with state-of-the-art pattern recognition algorithms is sufficient to compensate the electrode displacement effect.