Robot-Assisted Gait Assessment Using Azure Kinect: A Pilot Clinical Validation Against Vicon Including Individuals with Multiple Sclerosis

Integrating depth sensors into mobile robots enables automated gait monitoring with potential applications in neurological disorders. This pilot study aims to evaluate the preliminary feasibility of robot-assisted gait assessment using Azure Kinect against Vicon, including individuals with multiple sclerosis, while simultaneously examining between-system, within-system, and environmental effects. A total of 20 participants were recruited to complete the eight-meter straight-line and 32 m corridor walking tests in the laboratory on the same day. Following independent data acquisition by both systems, temporal alignment was achieved through foot-event anchoring and interval trimming. On a unified timeline, 8 joint kinematic signals and 26 descriptors were extracted. Generalized estimating equations were applied, with a Bonferroni correction implemented for the 26 parallel tests to control the family error rate. The results showed: The spatiotemporal gait metrics exhibited general stability between systems and environments. Vicon better revealed variations in hip and pelvic amplitudes and restricted extension phenotypes, while the robotic system demonstrated greater sensitivity to knee posture and relative swing amplitude. The corridor environment induced an increase in stride length and a reduced step time compared to the laboratory, accompanied by a greater peak of hip and knee flexion and a greater forward lean of the trunk, with a largely preserved temporal organization. Within the Vicon-referenced framework, Azure Kinect-based robotic assessment demonstrated preliminary feasibility for capturing gait-related characteristics in individuals with multiple sclerosis. However, due to the limited number of analyzed MS participants, these findings should be interpreted as exploratory rather than as definitive clinical validation. The two systems exhibit complementary kinematic advantages. We recommend adopting an evaluation protocol that combines laboratory baseline with corridor validation, supplemented by descriptor-level mapping for cross-system data integration when necessary. This approach may support future tiered assessment, disease progression monitoring, and efficacy evaluation, but larger clinical cohorts are required to confirm its applicability in individuals with multiple sclerosis.