Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

Kushioka J, Sun R, Zhang W, Muaremi A, Leutheuser H, Odonkor CA, Smuck M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Sensors MDPI

Book Volume: 22

Article Number: 9301

Journal Issue: 23

DOI: 10.3390/s22239301

Abstract

Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)—neurogenic diseases, and knee osteoarthritis (KOA)—structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.

Authors with CRIS profile

Heike Leutheuser Machine Learning and Data Analytics Lab

Involved external institutions

Stanford University US United States (USA) (US) École Polytechnique Fédérale de Lausanne (EPFL) CH Switzerland (CH) Novartis AG CH Switzerland (CH) Yale University US United States (USA) (US)

How to cite

APA:

Kushioka, J., Sun, R., Zhang, W., Muaremi, A., Leutheuser, H., Odonkor, C.A., & Smuck, M. (2022). Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors. Sensors, 22(23). https://doi.org/10.3390/s22239301

MLA:

Kushioka, Junichi, et al. "Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors." Sensors 22.23 (2022).

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