Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Sensors
Dylan Kobsar, Reed Ferber

Abstract

Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual's typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual's movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers ob...Continue Reading

References

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Nov 28, 2016·Journal of Biomechanics·Dylan KobsarReed Ferber
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Sep 14, 2017·Journal of Neuroengineering and Rehabilitation·Dylan KobsarReed Ferber
Jun 5, 2018·Journal of Bodywork and Movement Therapies·Hamid Reza BokaeianJamileh Moghimi

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Citations

Mar 3, 2020·Frontiers in Bioengineering and Biotechnology·Marion MundtBernd Markert
Feb 18, 2020·Journal of Diabetes Science and Technology·Malte JacobsenLutz Heinemann
Mar 4, 2021·Scientific Reports·Yuta SekiYoshinori Katsumata

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Software Mentioned

OCSVM
Matlab

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