Sang Hun Chung

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One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients' gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to(More)
Continuous EMG control of human-machine interfaces (HMIs) enables more direct and flexible control of output movements than discrete classification algorithms. However, EMG is a non-stationary signal and can add noise to continuous EMG control output. We studied the effect of an output speed threshold method to stabilize the movement prediction of a 2-DOF(More)
It is well known that the activation of plantar flexors have a strong influence on the walking speed. If the gait speed can be predicted using this relationship, a post-stroke hemiparetic patient could control a gait rehabilitation robot according to his or her gait intention, and the robotic gait rehabilitation effect could be further improved. To find out(More)
This paper presents a framework for classifying sit-to-stand and stand-to-sit from just two channel EMG signals taken from the left leg. Our proposed framework uses linear discriminant analysis (LDA) as the classifier and a multi-window feature extraction approach termed Consecutive Time-Windowed Feature Extraction (CTFE). We present the prelimnary results(More)
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