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

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 $\text{textsc}GaitVista$, 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, $\text{textsc}GaitVista$ reduces average full-body and lower-body error by $\mathbf{\text{27.7\%}}$ and $\mathbf{\text{27.8\%}}$, 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 $\mathbf{\text{6.4\%}}$ relative to the strongest learned fusion baseline. On TotalCapture, it improves bilateral knee-flexion waveform accuracy by $\mathbf{\text{18.9\%}}$. 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.