Abstract Summary
Wearable sensors enable collection of large quantities of data from real-world movement, but the variety of behaviors recorded makes it challenging to distill generalizable knowledge from these data. This research develops a method for comparing the effects of mobility interventions on frequently-repeated movements in data recorded during everyday life. A case study compares gait kinematics from week-long data sets using two types of footwear.
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