Seongjung Kim
Yonsei University
6 Papers
Seongjung Kim is an academic researcher from Yonsei University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 4, co-authored 6 publications.
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Papers
Optimization of a pre-impact fall detection algorithm and development of hip protection airbag system
TL;DR: In this study, a pre-impact fall detection algorithm using a custom-made inertial sensor was optimized, and a spring-trigger airbag system was developed for preventing injuries from falls.
Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.
TL;DR: A finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors and the accuracy of the E-ANN was significantly higher than that of the general ANN.
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Development of an Armband EMG Module and a Pattern Recognition Algorithm for the 5-Finger Myoelectric Hand Prosthesis
Seongjung Kim,Jongman Kim,Bummo Koo,Taehee Kim,Haneul Jung,Se-Hoon Park,Seung-Gi Kim,Youngho Kim +7 more
TL;DR: The algorithm was successfully applied to provide seven different hand postures in a 5-finger myoelectric hand prosthesis and showed that the major misclassifications were lateral pinch versus palmar pinch, and index versus thumb-up, however, with the classification training for seven or more sessions, the probability of misclassification significantly decreased.
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Mechanomyography for the Measurement of Muscle Fatigue Caused by Repeated Functional Electrical Stimulation
TL;DR: In this article, an attempt at utilizing mechanomyography (MMG) to quantify muscle fatigue, which occurs on account of repeated functional electrical stimulations (FES), is presented Twenty-one subjects participated in the experiment, wherein a constant electrical stimulation was repeatedly applied to the tibialis anterior muscle MMG signals were measured simultaneously, as the stimulations were applied, and subsequently quantified using 8 different methods.