Taehee Kim
Yonsei University
6 Papers
Taehee Kim is an academic researcher from Yonsei University. The author has contributed to research in topics: Computer science & Acceleration. The author has an hindex of 2, co-authored 6 publications.
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Papers
Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags.
TL;DR: A threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body and achieved a high accuracy using the experimental data, which included some highly dynamic motions that had not been tested previously.
34
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.
25
Patent
Fall detection device and method
Young Ho Kim,Jongman Kim,Bummo Koo,Jeong Hanul,Taehee Kim +4 more
- 10 Dec 2020
TL;DR: In this paper, the authors proposed a fall detection method that consists of measuring a first to a third acceleration corresponding to a user, measuring a second to a three angular velocity corresponding to the user, and calculating a triangular characteristic by using the acceleration value, the angular velocity, the angle value, and the triangular characteristic.
Post-fall Detection Using ANN Based on Ranking Algorithms
TL;DR: The T-score was found to be the most optimal among the five ranking algorithms used and is expected to assist in the construction of subsets of feature vectors based on ranking algorithms for post-fall detection with high accuracy and less computational cost.
Effects of Sampling Rate and Window Length on Motion Recognition Using sEMG Armband Module
Taehee Kim,Jongman Kim,Bummo Koo,Haneul Jung,Yejin Nam,Yunhee Chang,Se-Hoon Park,Youngho Kim +7 more
TL;DR: This study investigates the effects of the sEMG-signal sampling rate and feature extraction window length on the classification accuracy in hand-motion recognition and shows that for all classifiers and all subjects, the hand- motion classification accuracy increases with an increase in the sampling rate.