Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography | Ziai and Menon Journal of NeuroEngineering and Rehabilitation 2011 8 56 http content 8 1 56 Iril JOURNAL OF NEUROENGINEERING NCR AND REHABILITATION RESEARCH Open Access Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography Amirreza Ziai and Carlo Menon Abstract Background Several regression models have been proposed for estimation of isometric joint torque using surface electromyography SEMG signals. Common issues related to torque estimation models are degradation of model accuracy with passage of time electrode displacement and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig equipped with a torque sensor was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session for the purpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. Acquired SEMG signals were filtered rectified normalized and then fed to models for training. Results It was shown that mean adjusted coefficient of determination R2 values decrease between 20 -35 for different models after one hour while altering arm posture decreased mean R2 values between 64 to 74 for different models. Conclusions Model estimation accuracy drops significantly with passage of time electrode displacement and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training .
