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

Abstract

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 threedimensional 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.

Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis

“…The DTW algorithm is a method of determining the optimal alignment between two sequences using dynamic programming. A detailed description of DTW is beyond the current scope, but may be found in [22], and its application to gait pathology is described in [23]. The approach is essentially one of template matching: test sequences are compared to baseline sequences from the same subject, so that accumulated differences between current gait cycles and the known baseline can be quantified on an ongoing basis.…”

“…The approach is essentially one of template matching: test sequences are compared to baseline sequences from the same subject, so that accumulated differences between current gait cycles and the known baseline can be quantified on an ongoing basis. This algorithm has been incorporated in gait recognition [24] and more recently to evaluate gait pathology [23]. In the current work, gait cycles are aligned with DTW to generate two distinct measures: the DTW Score and the Warp Score.…”

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients

“…It is an effective clustering strategy for time-series data across a broad range of application domains [31]. Examples of biomedical applications are speech recognition [16], gait pathology [32][33][34], and electro-cardiogram analysis [35]. The DTW approach could be well-suited to cluster individual symptoms based on the temporal features that they share, using ROM or ecological momentary assessment EMA [36] time-series data.…”

Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects

“…Instead, pairs of gait cycles from different portions of the walk can be directly compared using the dynamic time warping (DTW) algorithm. DTW has been applied to inertial gait data to recognize persons by their gait patterns 9 and distinguish normal walking from simulated pathology 10 .…”