Surface electromyography interface study with neuromechanical models = 신경역학모델을 이용한 표면근전도 인터페이스 연구

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Recently, advanced solutions such as exoskeletons and prosthetic robots are getting great attention for the elderly and physically disabled to support or restore their mobility. Unfortunately, relaying information according to the user’s intention and volition (in terms of desired motor tasks) to drive, command, and control a machine is the weakest link in the chain of components that includes electronics, computing, actuators, mechanisms, and materials, all of which are adequate for the application. Despite loss of motor function, the elderly and physically disabled would still possess a central nervous system that can send motor commands to muscles. Therefore, neural signals can be used as a source to directly bridge the gap between the human and the machine. Among neural signals, surface electromyography (sEMG) is a noninvasive technique to measure superimposed action potentials of recruited motor units (MUs) for muscle contraction. Even though sEMG has been widely used to observe muscle contraction levels, it has been challenging to estimate limb joint force or motion that reflects control intents by sEMG for a machine. If a muscle is regarded as a mechanical system, sEMG could represent a system input and joint force or motion could be a system output. Therefore, system models are required to express the relationship between sEMG and limb motions. This thesis proposes models to extract human movement intents by sEMG, and presents experimental results to validate the models. First, a non-negative muscle synergy matrix is proposed to estimate fluid wrist movements described as a universal joint by sEMG. The rationale behind this approach is that the central nervous system does not control individual muscles to manipulate the multiple degrees of freedom of the musculo-skeletal systems. The central nervous system combines the muscles into groups whose desired joint torques produce a variety of natural movement behavior by combining activations of the muscle gr...
Kim, Jungresearcher김정researcher
한국과학기술원 : 기계공학전공,
Issue Date
466298/325007  / 020057621

학위논문(박사) - 한국과학기술원 : 기계공학전공, 2011.2, [ vi, 85 p. ]


보조공학; 근육모델링; 생체신호처리; Assistive Technology; Muscle Modeling; Biosignal Processing; Surface Electromyography (sEMG); 표면근전도

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