Comparison of gait event detection from shanks and feet in single-task and multi-task walking of healthy older adults
conference contributionposted on 21.06.2017 by Weisheng Kong, J. Lin, Lauren Waaning, Salvatore Sessa, Sarah Cosentino, Daniele Magistro, Massimiliano Zecca, Ryuta Kawashima, Atsuo Takanishi
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
Automatic and objective detection algorithms for gait events from MEMS Inertial Measurement Units data have been developed to overcome subjective inaccuracy in traditional visual observation. Their accuracy and sensitivity have been verified with healthy older adults, Parkinson's disease and spinal injured patients, using single-task gait exercises, where events are precise as the subject is focusing only on walking. Multi-task walking instead simulates a more realistic and challenging scenario where subjects perform secondary cognitive task while walking, so it is a better benchmark. In this paper, we test two algorithms based on shank and foot angular velocity data in single-task, dual-task and multi-task walking. Results show that both algorithms fail when the subject slows extremely down or pauses due to high cognitive and attentional load, and, in particular, the first stride detection error rate of the foot-based algorithm increases. Stride time is accurate with both algorithms regardless of walking types, but the shank-based algorithm leads to an overestimation on the proportion of swing phase in one gait cycle. Increasing the number of cognitive tasks also causes this error with both algorithms.
This research has been supported by the JSPS Grant-in-Aid for Young Scientists (Wakate B) [15K21437], FY2016 Grant Program for Promotion of International Joint Research of Waseda University, and also in part by the Program for Leading Graduate Schools, Graduate Program for Embodiment Informatics of the Ministry of Education, Culture, Sports, Science and Technology.
- Sport, Exercise and Health Sciences