Preliminary Exploration of a Gait Alteration Index to Detect Abnormal Walking Through a RGB-D Camera and Human Pose Estimation

Quantitative gait analysis is essential for assessing motor function, as altered walking patterns are linked to functional decline and increased fall risk. Although recent advances in markerless motion analysis and human pose estimation enable gait feature extraction from low-cost video systems compared to expensive motion analysis laboratories, clinical translation remains limited by fragmented descriptors or approaches that directly regress clinical scores, often reducing interpretability and generalizability. We propose the Gait Alteration Index (GAI), an interpretable index that quantifies gait abnormality as a functional deviation from typical walking patterns, independently of specific pathologies. The GAI is computed from a small set of gait parameters and integrates three complementary domains: spatio-temporal characteristics, surrogates of dynamic stability, and arm swing behaviour, providing both a global index and domain-specific sub-indices. Preliminary evaluation on a heterogeneous cohort using clinician-derived assessments showed that the GAI captures clinically meaningful gait alterations (Spearman’s ρ=0.65), with the strongest agreement for spatio-temporal features (ρ=0.77). These results suggest that the GAI is a promising low-cost, and interpretable tool for objective gait assessment, screening, and longitudinal monitoring.