Feasibility of Privacy-Preserving LiDAR-Based Continuous Gait and Activity Monitoring in Three People with Multiple Sclerosis: A Technical Proof-of-Concept Case Study

Multiple sclerosis (MS) is a chronic central nervous system disease with heterogeneous symptoms, including gait disturbances and motor fatigue, affecting daily functioning and quality of life. Episodic assessments may miss within-day functional fluctuations, whereas home-like monitoring may characterize them. This technical proof-of-concept case study quantified gait parameters (velocity, step length, and variability) during natural walking, explored temporal changes in gait and activity as potentially fatigue-relevant motor-performance patterns, and examined the feasibility of deriving candidate digital measures in MS. Three individuals with MS (one EDSS 1; two EDSS 3) were monitored in an instrumented apartment for 6.5–9.0 h using three LiDAR sensors. Gait parameters, region transitions, activity patterns, EDSS, FSMC, VAS-F, and available data-yield indicators were summarized descriptively. Compared with published healthy-adult references, P01 and P03 showed lower walking velocity, and P03 showed reduced step length. P01 maintained stable gait, P02 increased afternoon walking velocity, and P03 showed an afternoon velocity decline and a smaller step length decrease. The behavioral profiles described different spatial activity patterns, and the activity levels remained low during monitoring. LiDAR-based monitoring may provide a privacy-preserving approach to capture gait and activity variations as candidate variables for future validation without establishing fatigue specificity, clinical validity, or diagnostic thresholds.