Identification of Walking Balance using Acceleration Sensors
(This article belongs to the Special Issue on SP8 (Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26)) and the Section Medical Informatics (MDI))
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Chen, J. , Hirokane, M. and Horiguchi, Y. (2026). Identification of Walking Balance using Acceleration Sensors. Journal of Engineering Research and Sciences, 5(5), 1–11. https://doi.org/10.55708/js0505001
Junyu Chen, Michiyuki Hirokane and Yukio Horiguchi. "Identification of Walking Balance using Acceleration Sensors." Journal of Engineering Research and Sciences 5, no. 5 (May 2026): 1–11. https://doi.org/10.55708/js0505001
J. Chen, M. Hirokane and Y. Horiguchi, "Identification of Walking Balance using Acceleration Sensors," Journal of Engineering Research and Sciences, vol. 5, no. 5, pp. 1–11, May. 2026, doi: 10.55708/js0505001.
The risk of falling increases with age, affecting approximately one in three individuals over 65 and one in two over 80 annually. In Japan, the fall rate among older adults ranges from 8.5% to 25.3%, and falls are a major cause of fractures and long-term care needs. Balance impairment is one of the key factors contributing to falls, as maintaining the body’s center of gravity within the base of support is essential for stable walking. To address this issue, this study proposes a method for classifying balance conditions during walking using acceleration data collected from four sensors attached to the waist. Two classification approaches were examined: MiniROCKET as a kernel-based method, and InceptionTime as a deep learning method. By comparing these two representative time-series classification paradigms, the study aims to clarify which approach is more suitable for gait balance assessment. Furthermore, a preliminary analysis was conducted to determine the optimal number and placement of sensors. The results suggest that the proposed method can effectively identify deviations from normal gait, indicating its potential for anomaly detection in walking balance. Identifying the minimal number of sensors required to maintain sufficient accuracy is crucial for practical applications, and this study provides a foundation for future verification with elderly populations to enhance long-term daily monitoring and fall-risk prevention.
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