GaitVista: Reliability-Aware AI Measurement toward Accessible Longitudinal Gait Assessment

Abstract:Tracking recovery of walking function requires detecting meaningful gait change across rehabilitation sessions, yet objective 3D measurement remains confined to specialized motion-capture laboratories. Small camera sets and body-worn inertial sensors broaden access, but reliability varies across joints and time, allowing sensing failures to masquerade as patient change. We present \textsc{GaitVista}, a reliability-aware measurement layer whose lightweight gate assigns joint- and frame-specific visual contributions using camera coverage, local visual quality, cross-modal disagreement, and root-motion continuity, and exposes them for inspection. Across seven clean and degraded sensing conditions on TotalCapture, \textsc{GaitVista} reduces average full-body and lower-body error by \textbf{27.7\%} and \textbf{27.8\%}, attains the lowest worst-condition error among fusion methods, and reduces the gap to a joint-frame oracle from $2.76$--$5.33$~cm for condition-blind baselines to $1.11$~cm. On MoVi with image-derived keypoints, it is the only deployable fusion method to improve over both unimodal streams, reducing marker-supported error by \textbf{6.4\%} relative to the strongest learned fusion baseline. On TotalCapture, it improves bilateral knee-flexion waveform accuracy by \textbf{18.9\%}. Raw inertial measurements from five TotalCapture participants show location- and time-varying magnetic disturbance, supporting the design's reliability premise. Both benchmarks contain neurologically healthy participants in controlled settings and retain participant-specific IMU calibration; we therefore report progress toward accessible gait assessment, not validated clinical deployment.

Artificial Intelligence (cs.AI)

arXiv:2609.22619 [cs.AI]

(or arXiv:2609.22619v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.22619 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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