Research Article

Poster: Foot-Floor Friction Based Walking Surface Detection for Fall Prevention Using Wearable Motion Sensors

Published: 2023-6-21

Journal: Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies

DOI: 10.1145/3580252.3589414

Abstract

Poster: Foot-Floor Friction Based Walking Surface Detection for Fall Prevention Using Wearable Motion Sensors **Authors:** Shuangquan Wang, Gang Zhou **Published in:** CHASE '23: Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies **Pages:** 179 - 180 **DOI:** [10.1145/3580252.3589414](https://doi.org/10.1145/3580252.3589414) **Published:** 22 January 2024 **Abstract:** Automatic walking surface detection helps people adapt their gait to different surfaces and reduce fall risk. Walking on different surfaces causes different foot-floor friction patterns. We proposed to deploy motion sensors near the ankle to sense foot-floor friction and recognize walking surfaces. There are two contributions in this proposed research work. First, we demonstrated that the proposed method is capable of distinguishing five most-common walking surfaces in daily living. Second, we compare the detection accuracy between walking normally and dragging feet while walking. Experimental results show the proposed method obtains higher accuracy for dragging feet while walking, which reaches 90.6% using only five seconds of data. **Index Terms:** Poster: Foot-Floor Friction Based Walking Surface Detection for Fall Prevention Using Wearable Motion Sensors Human-centered computing Ubiquitous and mobile computing Ubiquitous and mobile computing systems and tools **Acknowledgements:** Chair: Shiwen Mao, Proceedings Editor: Shuangquan Wang, Program Chairs: Hua Fang, Wei Gao, Gang Zhou **Sponsor:** SIGBED **Conference Dates:** June 21 - 23, 2023 **Location:** Orlando, FL, USA

Faculty Members

  • Shuangquan Wang - Salisbury University, Salisbury, MD, USA
  • Gang Zhou - William & Mary, Williamsburg, VA, USA

Themes

  • Fall prevention
  • Wearable technology
  • Surface recognition
  • Gait adaptation
  • Human-centered computing

Categories

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