Neurodegenerative Disease-Specific Relations Between Temporal and Kinetic Gait Features Identified Using InterCriteria Analysis

Gait analysis is a non-invasive, cost-effective method for detecting subtle motor changes in neurodegenerative disorders. This study uses an exploratory approach to identify temporal–kinetic gait feature relationships specific to amyotrophic lateral sclerosis (ALS) and Huntington (HUNT) and Parkinson (PARK) disease versus healthy controls (CONTROL) using recent advances in InterCriteria Analysis (ICrA). The novelty lies in the (i) comprehensive temporal–kinetic feature set, (ii) use of ICrA to characterize inter-feature coordination patterns at population and disease-group levels and (iii) interpretation in a neuromechanical context. Forty-one temporal/kinetic features were extracted from left/right leg ground reaction force and rate-of-force-development signals, considering laterality, gait phase (stance, swing, double support), magnitudes, waveform correlations, and inter-/intra-limb asymmetries. The analysis included 14,580 steps from 64 recordings in the Gait in Neurodegenerative Disease Database: 16 CONTROL (4054 steps), 13 ALS (2465), 20 HUNT (4730), 15 PARK (3331). Sensitivity analysis identified strict consonance thresholds (μ ≥ 0.75, ν ≤ 0.25), selecting <5% strongest inter-feature relations from 820 feature pairs: population level (16 positive, 14 negative), group-level (15–25 positive, 9–14 negative). ICrA identified group-specific consonances—present in one group but absent in others—highlighting disease-related alterations in gait coordination: ALS (15/11 positive/negative, disrupted bilateral stride coordination, prolonged stance/double-support, decoupled stride/cadence, desynchronized force-generation patterns—reflecting compensatory adaptations to muscle weakness and instability), HUNT (11/7, severe temporal–kinetic breakdown consistent with gait instability—loss of bilateral coordination, reduced swing time, slowed force development), PARK (1/2, subtle localized disruptions—prolonged stance and double-support intervals, reduced force during weight transfer, overall coordination remained largely preserved). Benchmarking vs. Pearson correlation showed strong linear agreement (R 2 = 0.847, p < 0.001), confirming that ICrA captures dominant dependencies while moderating the correlation via uncertainty. These results demonstrate that ICrA provides a quantitative, interpretable framework for characterizing gait coordination patterns and can guide principled feature selection in future predictive modeling.