Pattern Classification of Hand Movements 2_0.pdf (1.41 MB)
Download filePattern classification of hand movements using time domain features of electromyography
conference contribution
posted on 2017-09-04, 14:39 authored by Carl Robinson, Baihua LiBaihua Li, Qinggang MengQinggang Meng, Matthew PainMatthew PainMyoelectric 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.
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