Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors | Arjunan and Kumar Journal of NeuroEngineering and Rehabilitation 2010 7 53 http content 7 1 53 l dl JOURNAL OF NEUROENGINEERING NCR AND REHABILITATION RESEARCH Open Access Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors Sridhar Poosapadi Arjunan Dinesh Kant Kumar Abstract Background Identifying finger and wrist flexion based actions using a single channel surface electromyogram sEMG can lead to a number of applications such as sEMG based controllers for near elbow amputees human computer interface HCI devices for elderly and for defence personnel. These are currently infeasible because classification of sEMG is unreliable when the level of muscle contraction is low and there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion overcoming the earlier shortcomings. Methods SEMG signal was recorded when the participant maintained pre-specified wrist and finger flexion movements for a period of time. Various established sEMG signal parameters such as root mean square RMS Mean absolute value MAV Variance VAR and Waveform length WL and the proposed fractal features fractal dimension FD and maximum fractal length MFL were computed. Multi-variant analysis of variance MANOVA was conducted to determine the p value indicative of the significance of the relationships between each of these parameters with the wrist and finger flexions. Classification accuracy was also computed using the trained artificial neural network ANN classifier to decode the desired subtle movements. Results The results indicate that the p value for the proposed feature set consisting of FD and MFL of single channel sEMG was while that
