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

arXiv: https://arxiv.org/abs/2609.22619 · HTML: https://mlsanity.com/paper/2609.22619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 18 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [GauHuman: Articulated Gaussian Splatting from Monocular Human Videos](https://mlsanity.com/paper/2312.02973/) (2023)
- [MoVi: A Large Multipurpose Motion and Video Dataset](https://mlsanity.com/paper/2003.01888/) (2020)
- [Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture](https://mlsanity.com/paper/2309.00310/) (2023)
- [Motion Capture from Inertial and Vision Sensors](https://mlsanity.com/paper/2407.16341/) (2024)
- [DiffCap: Diffusion-based Real-time Human Motion Capture using Sparse IMUs and a Monocular Camera](https://mlsanity.com/paper/2508.06139/) (2025)
- [Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera](https://mlsanity.com/paper/2603.02130/) (2026)
- [Topology-aware Human Avatars with Semantically-guided Gaussian Splatting](https://mlsanity.com/paper/2408.09665/) (2024)
- [Mixture-of-Modality-Experts with Holistic Token Learning for Fine-Grained Multimodal Visual Analytics in Driver Action Recognition](https://mlsanity.com/paper/2604.05947/) (2026)
- [VIMCAN: Visual-Inertial 3D Human Pose Estimation with Hybrid Mamba-Cross-Attention Network](https://mlsanity.com/paper/2605.07552/) (2026)

## Abstract (arXiv)

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 textscGaitVista, 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, textscGaitVista reduces average full-body and lower-body error by 27.7% and 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 6.4% relative to the strongest learned fusion baseline. On TotalCapture, it improves bilateral knee-flexion waveform accuracy by 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.