Wearable devices have been developed to assist walking based on the wearer’s intention. However, it would be dangerous if a device misidentifies falling as intentional motion; it is necessary to detect falls in real time. In particular, backward slip is the most common and dangerous type of falls. Fifteen participants walked on a split-belt instrumented treadmill while random backward slip perturbation of belt speed acceleration was provided to the foot. We aimed to identify slip within 0.35s after the onset of the perturbation, the typical window of slip, using lower limb kinematic data obtained within 0.3s; only 0.05s was allowed for the identification. We developed 5 machine learning models, and the logistic regression model showed the highest accuracy of 87.5%. The initial study is expected to contribute to the prevention of falls by developing and applying the results to wearable devices.
New Investigator Award
Lee, Chihyeong and Ahn, Jooeun
"REAL-TIME BACKWARD SLIP DETECTION USING A SLIP-INDUCING SYSTEM AND MACHINE LEARNING METHODS,"
ISBS Proceedings Archive: Vol. 41:
1, Article 70.
Available at: https://commons.nmu.edu/isbs/vol41/iss1/70