2134/26360
Carl Robinson
Carl
Robinson
Baihua Li
Baihua
Li
Qinggang Meng
Qinggang
Meng
Matthew Pain
Matthew
Pain
Pattern classification of hand movements using time domain features of electromyography
Loughborough University
2017
Electromyography
Myoelectric control
Time domain features
Machine learning
Medical and Health Sciences not elsewhere classified
2017-09-04 14:39:11
Conference contribution
https://repository.lboro.ac.uk/articles/conference_contribution/Pattern_classification_of_hand_movements_using_time_domain_features_of_electromyography/9405326
Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect the electrical signals of muscle activity and perform subsequent mechanical actions. Despite several decades’ research, robust, responsive and intuitive control schemes remain elusive. Current commercial hardware advances
offer a variety of movements but the control systems are unnatural, using sequential switching methods triggered by specific
sEMG signals. However, recent research with pattern recognition and simultaneous and proportional control shows good promise for
natural myoelectric control. This paper investigates several sEMG time domain features using a series of hand movements performed by 11 subjects, taken from a benchmark database, to determine if optimal classification accuracy is dependent on feature set size. The features were extracted from the data using a sliding window process and applied to five machine learning classifiers, of which Random Forest consistently performed best. Results suggest a few simple features such as Root Mean Square and Waveform Length achieve comparable performance to using the entire feature set, when identifying the hand movements, although further work is
required for feature optimisation.