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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Study of stability of time-domain features for electromyographic pattern recognition | Tkach et al. Journal of NeuroEngineering and Rehabilitation 2010 7 21 http www.jneuroengrehab.eom content 7 1 21 J NER JOURNAL OF NEUROENGINEERING AND REHABILITATION RESEARCH Open Access Study of stability of time-domain features for electromyographic pattern recognition Dennis Tkach 1 2 He Huang 1 3 and Todd A Kuiken1 4 Abstract Background Significant progress has been made towards the clinical application of human-machine interfaces HMIs based on electromyographic EMG pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges such as physical and physiological changes that result in variations in EMG signals and systems that are unreliable for long-term use. In this study we aimed to address these challenges by 1 investigating the stability of time-domain EMG features during changes in the EMG signals and 2 identifying the feature sets that would provide the most robust EMG pattern recognition. Methods Variations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals EMG electrode location shift variation in muscle contraction effort and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study. Results Muscle fatigue had the smallest effect on the studied EMG features while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances the most stable EMG feature set with combination of four features produced at least 16.0 higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied .
