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Bae, Joonbum
Bio-Robotics and Control (BiRC) Lab
Research Interests
  • Design and control of physical human-robot interaction systems
  • Soft robotics
  • Intelligent interaction algorithms for virtual reality, tele-operation and rehabilitation
  • Bio-inspired robotics for improved mobility

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Deep Learning based Real-time Recognition of Dynamic Finger Gestures using a Data Glove

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Title
Deep Learning based Real-time Recognition of Dynamic Finger Gestures using a Data Glove
Author
Lee, MinhyukBae, Joonbum
Issue Date
2020-11
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE ACCESS, v.8, pp.219923 - 219933
Abstract
In this article, a real-time dynamic finger gesture recognition using a soft sensor embedded data glove is presented, which measures the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joint angles of five fingers. In the gesture recognition field, a challenging problem is that of separating meaningful dynamic gestures from a continuous data stream. Unconscious hand motions or sudden tremors, which can easily lead to segmentation ambiguity, makes this problem difficult. Furthermore, the hand shapes and speeds of users differ when performing the same dynamic gesture, and even those made by one user often vary. To solve the problem of separating meaningful dynamic gestures, we propose a deep learning-based gesture spotting algorithm that detects the start/end of a gesture sequence in a continuous data stream. The gesture spotting algorithm takes window data and estimates a scalar value named gesture progress sequence (GPS). GPS is a quantity that represents gesture progress. Moreover, to solve the gesture variation problem, we propose a sequence simplification algorithm and a deep learning-based gesture recognition algorithm. The proposed three algorithms (gesture spotting algorithm, sequence simplification algorithm, and gesture recognition algorithm) are unified into the real-time gesture recognition system and the system was tested with 11 dynamic finger gestures in real-time. The proposed system took only 6 ms to estimate a GPS and no more than 12 ms to recognize the completed gesture in real-time.
URI
https://scholarworks.unist.ac.kr/handle/201301/48766
URL
https://ieeexplore.ieee.org/document/9264164
DOI
10.1109/ACCESS.2020.3039401
ISSN
2169-3536
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