Stationary wavelet processing and data imputing in myoelectric pattern recognition on an embedded system
Autumn Naber · Chalmers Publication Library (Chalmers University of Technology) · 2017
Surface electromyography offers a low-cost, non-invasive method of predicting motor intention for prosthetic device control.Conventional active prostheses use individual muscle groups to trigger movement along one degree of freedom at a time, resulting in an effective, but slow and counter-intuitive control scheme.Pattern recognitionbased approaches to decoding muscle signals allow for more advanced, intuitive control, but at the cost of robustness to in-band noise and sensor faults.Signal processing to increase the distinguishability of muscle signals is an active area of research, but there has been little investigation in the implementation of a realtime, portable system that is robust against common noise sources.The aim of this work is to review the recent advances in electromyography signal processing and to investigate the effectiveness of wavelet-based signal processing and mean missing data imputing on the classification accuracy and controllability of myoelectric pattern recognition-based upper-limb prosthetic devices.The proposed algorithms were implemented on-board a standalone microprocessor to allow users of pattern recognition-based prosthetic devices to operate without being fixed to a PC.Nine able-bodied subjects were instructed to perform a series of Motion Tests while generating motion artifacts and electrode disconnect events.Four channels of untargeted forearm electromyogram signals were recorded and used for motor intention prediction with and without the proposed routines active.The results for tests comparing wavelet-based transient artifact reduction and conventional filtering showed no statistically significant change.Results for comparing missing data imputation with standard processing also showed no statistically significant change.Further tests were done using a recorded data set of 15 healthy subjects performing the same motion tests with artificially added pre-recorded motion artifacts and electrode disconnect events.In order to observe the effect of a higher number of episodes, further investigation was performed on a set of pre-recorded Motion Tests from 15 able-bodied subjects with artificially added noise and sensor faults.The tests with simulated interferences showed a statistically significant increase in classifier accuracy, specificity, and sensitivity for wavelet processing.Results also showed an increase in accuracy and specificity for data imputing, but at the cost of movement completion rate.These results suggest that the proposed routines can be implemented in real-time systems to improve prosthetic device controllability and that they are viable for use in further studies.