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RISS 인기검색어
- 검색키워드
- 저자 : Yupeng Ren (검색결과 3 건)
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Lee, Song Joo,Ren, Yupeng,Press, Joel M.,Lee, Jungwha,Zhang, Li-Qun IEEE 2017 IEEE transactions on neural systems and rehabilita Vol.25 No.11
<P>Knee injuries are usually associated with offaxis loadings in the transverse and frontal planes. Thus, improvement of lower limb offaxis neuromuscular control is important in knee injury prevention and post-injury rehabilitation. The goal of this paper was to investigate the effects of six-week pivoting offaxis intensity adjustable neuromuscular control training (POINT) using a custom-made offaxis elliptical trainer on lower limb offaxis neuromuscular control performance in trained and untrained functional tasks under slippery conditions. Twenty-six subjects participated in 18 sessions of POINT (three sessions per week for six weeks) and 25 subjects served as controls who did a regular workout. Offaxis neuromuscular control performance measures in terms of pivoting instability, sliding instability, and time-to-peak offaxis EMG entropy were evaluated on both groups under slippery conditions including a trained free pivoting task and untrained free sliding task and free pivoting and sliding task. Compared with the control group, the training group significantly decreased pivoting instability and the time-to-peak offaxis EMG entropy in lower limb muscles, indicating improvement in offaxis neuromuscular control performance. Furthermore, the training group showed reduced pivoting instability and sliding instability during the untrained free pivoting and sliding task. This paper may help us develop more focused and effective offaxis training programs to reduce knee injuries associated with offaxis loadings.</P>
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Liu, Jie,Kang, Sang Hoon,Xu, Dali,Ren, Yupeng,Lee, Song Joo,Zhang, Li-Qun Frontiers Media S.A. 2017 Frontiers in neuroscience Vol.11 No.-
<P>Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.</P>
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Changes of Shoulder, Elbow, and Wrist Stiffness Matrix Post Stroke
Li-Qun Zhang,Jongsang Son,Hyung-Soon Park,Sang Hoon Kang,Yunju Lee,Yupeng Ren IEEE 2017 IEEE transactions on neural systems and rehabilita Vol.25 No.7
<P>Stroke affects multiple joints in the arm with stereotypical patterns of arm deformity involving the shoulder, elbow, wrist, and hand and with disrupted coordination of multiple joints in active movements. However, there is a lack of systematicmethods to evaluatemulti-joints and multi-degree of freedoms (DOF) neuro-mechanical changes, especially for complex systemswith three ormore joints/ DOFs involved. This paper used a novel systematic method to characterize dynamics and control of the shoulder, elbow, and wrist of the human arm individually and simultaneously, including the couplings across themultiple joints during controlled movements. A novel method was developed to decompose the complex system into manageable single-joint level for more reliable characterizations. The method was used in clinical studies to characterize the multi-joint changes associated with spastic impaired arm of 11 patients post stroke and 12 healthy controls. It was found that stroke survivors showed not only increased stiffness at the individual joints locally but also significantly higher couplings across the joints. The relative increases in couplings are often higher than that of the local joint stiffness. The multi-joint characterization provided a tool to characterize impairment of individual patients, which would allow more focused impairment-specific treatment. In general, the decomposition method can be used for even more complex systems, making characterization of intractable system dynamics of three or more joints/DOFs manageable.</P>
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