GaitVista：面向可及纵向步态评估的可靠性感知AI测量
GaitVista: Reliability-Aware AI Measurement toward Accessible Longitudinal Gait Assessment
- University of Illinois Chicago（伊利诺伊大学芝加哥分校）
- Illinois Mathematics and Science Academy（伊利诺伊数学与科学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
GaitVista提出可靠性感知测量层，通过轻量级门控融合视觉与惯性数据，在多种条件下显著降低步态评估误差，提升可及纵向评估的准确性。
AI中文摘要：
追踪步行功能的恢复需要在康复疗程之间检测有意义的步态变化，然而客观的3D测量仍局限于专门的运动捕捉实验室。小型相机组和穿戴式惯性传感器拓宽了可及性，但可靠性在不同关节和时间上存在差异，使得传感故障可能被误认为患者变化。我们提出GaitVista，一个可靠性感知的测量层，其轻量级门控根据相机覆盖范围、局部视觉质量、跨模态不一致性和根运动连续性，为每个关节和帧分配视觉贡献，并将其暴露以供检查。在TotalCapture上的七种干净和退化传感条件下，GaitVista将平均全身和下肢误差分别降低了27.7%和27.8%，在融合方法中实现了最低的最差条件误差，并将与关节帧神谕的差距从条件不可知基线的2.76–5.33厘米缩小到1.11厘米。在带有图像派生关键点的MoVi上，它是唯一一种能同时优于两种单模态流的可部署融合方法，相对于最强的学习融合基线，将标记支持的误差降低了6.4%。在TotalCapture上，它将双侧膝关节屈曲波形准确性提高了18.9%。来自五名TotalCapture参与者的原始惯性测量显示位置和时间变化的磁干扰，支持了该设计可靠性前提。两个基准均包含受控环境中的神经健康参与者，并保留了参与者特定的IMU校准；因此，我们报告的是迈向可及步态评估的进展，而非经过验证的临床部署。
英文摘要：
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.
