Design and Development of a Deep Learning-Based Pain Behavior Monitoring System through Individual Recognition of Hand Positions and Hand Gestures 


Vol. 48,  No. 2, pp. 227-236, Feb.  2023
10.7840/kics.2023.48.2.227


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  Abstract

With the rapid development of deep learning technology, research on recognizing patients' biometric information or complex conditions by incorporating deep learning into the healthcare field is being actively conducted. In particular, effective management of pain leads to an improvement in the patient's health condition and an increase in medical satisfaction. This paper proposes a deep learning-based pain behavior monitoring system with IMU sensor data collected through wristband devices as input. For precise recognition of pain behavior, we designed a combination of two CNN models that individually recognize hand positions and hand movements and an HMM model that reduces misrecognition of pain behavior as daily behavior. The wristband device, which collects IMU sensor data, is designed based on an nRF5240, based MDBT50Q module for low-power design. The proposed system recognizes 28 pain behaviors selected with advice from Pusan National University Hospital with an average accuracy of 87.06%

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[IEEE Style]

Y. Cho, H. Lee, Y. Baek, "Design and Development of a Deep Learning-Based Pain Behavior Monitoring System through Individual Recognition of Hand Positions and Hand Gestures," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 2, pp. 227-236, 2023. DOI: 10.7840/kics.2023.48.2.227.

[ACM Style]

Yonghun Cho, Hyunwook Lee, and Yunju Baek. 2023. Design and Development of a Deep Learning-Based Pain Behavior Monitoring System through Individual Recognition of Hand Positions and Hand Gestures. The Journal of Korean Institute of Communications and Information Sciences, 48, 2, (2023), 227-236. DOI: 10.7840/kics.2023.48.2.227.

[KICS Style]

Yonghun Cho, Hyunwook Lee, Yunju Baek, "Design and Development of a Deep Learning-Based Pain Behavior Monitoring System through Individual Recognition of Hand Positions and Hand Gestures," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 2, pp. 227-236, 2. 2023. (https://doi.org/10.7840/kics.2023.48.2.227)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880