Leveraging inertial sensors and dynamic time warping to identify biomarkers for gait impairment in hereditary spastic paraplegia: an exploratory study

Abstract Hereditary Spastic Paraplegia (HSP) is a rare neurodegenerative disorder characterized by progressive spasticity and weakness of the lower limbs, associated with gait impairment. While inertial measurement units (IMUs) have shown promise in monitoring mobility, objective biomechanical biomarkers of gait dysfunction remain underdeveloped. This study aimed to evaluate whether normalized root mean square error (nRMSE), calculated after Dynamic Time Warping (DTW) alignment of gyroscopic gait cycle profiles, could serve as a candidate biomarker for gait impairment in HSP. We conducted a cross-sectional pilot study involving six individuals with clinically diagnosed HSP and six broadly comparable healthy controls. Participants walked along a nine-meter walkway under standardized conditions, wearing two IMUs on the shanks. Sagittal plane angular velocity was extracted from each gait cycle. Gait parameters were computed, and each participant’s mean gait cycle was aligned to the control mean using DTW. Deviations were quantified using nRMSE. Clinical assessments included the SPATAX-EUROSPA Disability Score and observable items from the Spastic Paraplegia Rating Scale (SPRS). HSP participants showed longer stance times, slower speeds, and higher nRMSE scores. nRMSE was strongly correlated with SPATAX ( R = 0.967) and SPRS ( R = 0.982) in linear regression models. These findings support the potential utility of nRMSE as an objective, sensor-derived marker of gait impairment in HSP, warranting further validation in larger and longitudinal cohorts.