Background: Fear of frailty is a main concern for seniors. Surface electromyography(sEMG) controlled assistive devices for the upper extremities could potentially be usedto augment seniors’ force while training their muscles and reduce their fear of frailty.In fact, these devices could both improve self confidence and facilitate independentleaving in domestic environments. The successful implementation of sEMG controlleddevices for the elderly strongly relies on the capability of properly determining seniors’actions from their sEMG signals. In this research we investigated the viability ofclassifying hand postures in seniors from sEMG signals of their forearm muscles.Methods: Nineteen volunteers, including seniors (70 years old in average) andyoung people (27 years old in average), participated in this study and sEMG signalsfrom four of their forearm muscles (i.e. Extensor Digitorum, Palmaris Longus, FlexorCarpi Ulnaris and Extensor Carpi Radialis) were recorded. The feature vectors werebuilt by extracting features from each channel of sEMG including autoregressive (AR)model coefficients, waveform length and root mean square (RMS). Multi-classsupport vector machines (SVM) was used as a classifier to distinguish between fifteendifferent essential hand gestures including finger pinching.Results: Classification of hand gestures both in the pronation and supination positionsof the arm was possible. Classified hand gestures were: rest, ulnar deviation, radialdeviation, grasp and four different finger pinching configurations. The obtained averageclassification accuracy was 90.6% for the seniors and 97.6% for the young volunteers.Conclusions: The obtained results proved that the pattern recognition of sEMGsignals in seniors is feasible for both pronation and supination positions of the armand the use of only four EMG channel is sufficient. The outcome of this studytherefore validates the hypothesis that, although there are significant neurologicaland physical changes occurring in humans while ageing, sEMG controlled handassistive devices could potentially be used by the older people.
Tavakolan et al. BioMedical Engineering OnLine 2011, 10:79
BioMedical Engineering OnLine
A Preliminary Investigation Assessing the Viability of Classifying Hand Postures in Seniors
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