pp. 2495-2508
S&M3682 Research Paper of Special Issue https://doi.org/10.18494/SAM4818 Published: June 27, 2024 Performance of Weighted Random Reference Patterns on Wireless Channel Model for Gesture Recognition [PDF] Yung-Fa Huang, Hua-Jui Yang, Yung-Hoh Sheu, and Ching-Mu Chen (Received December 12, 2023; Accepted April 12, 2024) Keywords: wireless sensor network, received signal strength, channel model, gesture recognition, weighted random reference pattern
In recent years, wireless sensor devices have become able to perform multiple functions such as detecting human sleep conditions, blood pressure, heartbeat, and running paths. We use the wireless channel model of a wearable Zigbee wireless sensing node to conduct research on human posture recognition. The received signal strength indicator (RSSI) obtained through the transmission and reception of wireless signals is used to obtain the model of the wireless channel. The wireless sensor nodes receive different RSSI patterns of human gesture based on which they recognize a gesture through their respective wireless channels by performing distance processing on the collected signal data. However, in this paper, we propose a weighted random reference pattern (WRRP) to achieve a higher recognition accuracy. Experimental results show that WRRP can achieve a recognition accuracy of 98%.
Corresponding author: Yung-Hoh Sheu and Ching-Mu Chen![]() ![]() This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yung-Fa Huang, Hua-Jui Yang, Yung-Hoh Sheu, and Ching-Mu Chen, Performance of Weighted Random Reference Patterns on Wireless Channel Model for Gesture Recognition , Sens. Mater., Vol. 36, No. 6, 2024, p. 2495-2508. |