Citation

Gurchiek RD, Choquette RH, Beynnon BD, Slauterbeck JR, Tourville TW, Toth MJ, McGinnis RS. Sci. Rep. 2019; 9(1): e17966.

Affiliation

M-Sense Research Group, University of Vermont, Burlington, VT, 05405, USA.

Copyright

(Copyright © 2019, Nature Publishing Group)

DOI

10.1038/s41598-019-54399-1

PMID

31784691

Abstract

Critical to digital medicine is the promise of improved patient monitoring to allow assessment and personalized intervention to occur in real-time. Wearable sensor-enabled observation of physiological data in free-living conditions is integral to this vision. However, few open-source algorithms have been developed for analyzing and interpreting these data which slows development and the realization of digital medicine. There is clear need for open-source tools that analyze free-living wearable sensor data and particularly for gait analysis, which provides important biomarkers in multiple clinical populations. We present an open-source analytical platform for automated free-living gait analysis and use it to investigate a novel, multi-domain (accelerometer and electromyography) asymmetry measure for quantifying rehabilitation progress in patients recovering from surgical reconstruction of the anterior cruciate ligament (ACL). Asymmetry indices extracted from 41,893 strides were more strongly correlated (r = -0.87, p < 0.01) with recovery time than standard step counts (r = 0.25, p = 0.52) and significantly differed between patients 2- and 17-weeks post-op (p < 0.01, effect size: 2.20-2.96), and controls (p < 0.01, effect size: 1.74-4.20).

RESULTS point toward future use of this open-source platform for capturing rehabilitation progress and, more broadly, for free-living gait analysis.

Language: en