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http://dx.doi.org/10.14372/IEMEK.2022.17.4.229

A Light-weight ANN-based Hand Motion Recognition Using a Wearable Sensor  

Lee, Hyung Gyu (Duksung Women's University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.4, 2022 , pp. 229-237 More about this Journal
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
Hand Motion Recognition; Artificial Neural Network; Wearable Sensor; AIoT; Flex Sensor;
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Times Cited By KSCI : 2  (Citation Analysis)
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