用注意力模型同时优化传感器配置并自动检测数据偏误，发现公开数据集严重偏向右侧。
A Dual-Use Framework for Clinical Gait Analysis: Attention-Based Sensor Optimization and Automated Dataset Auditing
- 通过多流注意力机制，自动识别传感器重要性与数据偏差。
- 发现左脚数据被忽略（<0.1%注意力），源于数据集中右腿病例占比过高。
- 可为临床研究提供无偏数据审计和传感器组合新思路，适合医疗AI研究者。
基于可穿戴传感器与人工智能的客观步态分析对神经与骨科疾病的管理至关重要。然而，现有模型易受隐藏数据偏差影响，且任务导向的传感器优化仍具挑战。本文提出一种多流注意力深度学习框架，兼具传感器优化与自动化数据审计功能。在Voisard等人（2025）的多队列步态数据集上应用于四种临床任务（帕金森病、骨关节炎、中风筛查；帕金森病与中风鉴别），其注意力机制定量揭示了严重数据混杂问题。在骨关节炎与中风筛查任务中，因临床需双侧评估，模型却将超过70%注意力集中于右脚，左脚关注度低于0.1%（95%置信区间[0.0–0.1]）。此非临床发现，而是反映公开数据集中严重的偏倚（例如15例骨关节炎均为右侧）。本工作主要贡献在于方法论：展示可解释框架能自动审计数据完整性。次要成果是提出新型数据驱动的传感器协同效应（如头部+脚部用于帕金森病筛查），为未来优化协议提供假设。
原文摘要 · Abstract (English)
Objective gait analysis using wearable sensors and AI is critical for managing neurological and orthopedic conditions. However, models are vulnerable to hidden dataset biases, and task-specific sensor optimization remains a challenge. We propose a multi-stream attention-based deep learning framework that functions as both a sensor optimizer and an automated data auditor. Applied to the Voisard et al. (2025) multi-cohort gait dataset on four clinical tasks (PD, OA, CVA screening; PD vs CVA differential), the model's attention mechanism quantitatively discovered a severe dataset confound. For OA and CVA screening, tasks where bilateral assessment is clinically essential, the model assigned more than 70 percent attention to the Right Foot while statistically ignoring the Left Foot (less than 0.1 percent attention, 95 percent CI [0.0-0.1]). This was not a clinical finding but a direct reflection of a severe laterality bias (for example, 15 of 15 right-sided OA) in the public dataset. The primary contribution of this work is methodological, demonstrating that an interpretable framework can automatically audit dataset integrity. As a secondary finding, the model proposes novel, data-driven sensor synergies (for example, Head plus Foot for PD screening) as hypotheses for future optimized protocols.
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