IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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A Light-weight ANN-based Hand Motion Recognition Using a Wearable Sensor

웨어러블 센서를 활용한 경량 인공신경망 기반 손동작 인식기술

  • Received : 2022.03.21
  • Accepted : 2022.05.23
  • Published : 2022.08.31
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Abstract

Motion recognition is very useful for implementing an intuitive HMI (Human-Machine Interface). In particular, hands are the body parts that can move most precisely with relatively small portion of energy. Thus hand motion has been used as an efficient communication interface with other persons or machines. In this paper, we design and implement a light-weight ANN (Artificial Neural Network)-based hand motion recognition using a state-of-the-art flex sensor. The proposed design consists of data collection from a wearable flex sensor, preprocessing filters, and a light-weight NN (Neural Network) classifier. For verifying the performance and functionality of the proposed design, we implement it on a low-end embedded device. Finally, our experiments and prototype implementation demonstrate that the accuracy of the proposed hand motion recognition achieves up to 98.7%.

Keywords

Acknowledgement

본 연구는 2021년도 덕성여자대학교 교내연구비 지원에 의해 이루어졌음

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