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Background  Fear of frailty is a main concern for seniors. Surface electromyography (sEMG) controlled assistive devices for the upper extremities could potentially be used to augment seniors' force while training their muscles and reduce their fear of frailty. In fact, these devices could both improve self confidence and facilitate independent leaving in domestic environments. The successful implementation of sEMG controlled devices for the elderly strongly relies on the capability of properly determining seniors' actions from their sEMG signals. In this research we investigated the viability of classifying hand postures in seniors from sEMG signals of their forearm muscles.    Methods  Nineteen volunteers, including seniors (70 years old in average) and young people (27 years old in average), participated in this study and sEMG signals from four of their forearm muscles (i.e. Extensor Digitorum, Palmaris Longus, Flexor Carpi Ulnaris and Extensor Carpi Radialis) were recorded. The feature vectors were built by extracting features from each channel of sEMG including autoregressive (AR) model coefficients, waveform length and root mean square (RMS). Multi-class support vector machines (SVM) was used as a classifier to distinguish between fifteen different essential hand gestures including finger pinching.    Results  Classification of hand gestures both in the pronation and supination positions of the arm was possible. Classified hand gestures were: rest, ulnar deviation, radial deviation, grasp and four different finger pinching configurations. The obtained average classification accuracy was 90.6% for the seniors and 97.6% for the young volunteers.    Conclusions  The obtained results proved that the pattern recognition of sEMG signals in seniors is feasible for both pronation and supination positions of the arm and the use of only four EMG channel is sufficient. The outcome of this study therefore validates the hypothesis that, although there are significant neurological and physical changes occurring in humans while ageing, sEMG controlled hand assistive devices could potentially be used by the older people.

A preliminary investigation assessing the viability of classifying hand postures in seniors