Concordance of Wearable Sensors and Depth Cameras for Quantitative Gait Assessment in Multiple Sclerosis

Gait impairment is a primary driver of disability in multiple sclerosis (MS). However, traditional disability measures are nonlinear and lack sensitivity to subtle changes in motor function. Objective, scalable tools for gait assessment are critically needed.

To evaluate the agreement between a depth-sensing video system and wearable inertial measurement unit (IMU) sensors in quantifying gait metrics in people with MS (pwMS), to assess the relationship to patient-reported disability.

Twenty-six pwMS completed a 6-meter walk wearing IMU sensors (Clario OPAL) and were simultaneously recorded with a depth-sensing camera (Microsoft Kinect). Disability was self-reported using the Patient Determined Disease Step (PDDS) scale.

Participants had a median age of 54 years (range 23 - 71), 69 % were female, and the median disease duration was 65 months (0 - 382). Strong correlations with low differences were observed between devices for cadence (r > 0.9, p < 0.001; mean difference -5 steps/min), stride duration (r > 0.9, p < 0.001, mean difference 0.03-0.06 s), and gait speed (r = 0.7 - 0.9, p < 0.001, mean difference 0.04 - 0.15 m/s). Gait cycle phases were moderately correlated (r = 0.4 - 0.7). PDDS scores correlated most strongly with speed (r= -0.61), with weaker correlations across gait parameters.

Wearable and depth video systems yielded highly correlated spatiotemporal gait data obtained in a clinical setting and are more sensitive to motor abnormalities than the PDDS. These tools could potentially be widely employed for clinical gait assessment, democratizing access to sensitive qualitative measurement of disease progression in MS.