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Walking ability can be degraded by a number of pathologies, including movement disorders, stroke, and injury. Personal activity tracking devices gather inertial data needed to measure walking quality, but the required algorithmic methods are an active area of study. To detect changes in walking ability, the similarity between a person's current gait cycles and their known baseline gait cycles may be measured on an ongoing basis. This strategy requires a similarity measure robust to variability encountered in an outpatient scenario, including changes in walking surface, walking speed, and sensor orientation. Here we propose rotation, scale, and offset invariant dynamic time warping (RSOI-DTW), a variant of the well-known dynamic time warping (DTW) algorithm, as a generalization of DTW appropriate for three-dimensional inertial data. RSOI-DTW is invariant under rotation, scaling, and offset, yet it preserves the salient features of gait cycles required for gait monitoring. To support this claim, gait cycles from 21 subjects walking with four different styles were compared using both DTW and RSOI-DTW. The data show that RSOI-DTW converges quickly and achieves rotation, scale, and offset invariance. Both algorithms distinguish persons and detect abnormal walking, but only RSOI-DTW does so in the presence of sensor rotation. Variations in walking speed pose a challenge for both algorithms, but performance is improved by collecting baseline information at a variety of speeds.

Toward Detection and Monitoring of Gait Pathology using Inertial Sensors under Rotation, Scale, and Offset Invariant Dynamic Time Warping