让医生能质疑和干预AI对帕金森步态的判断，提升诊疗透明度。
Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson's Disease Gait Interpretation
- 用可穿戴设备的垂直地面反作用力数据，结合1D-CNN预测病情阶段。
- 模型错误时解释差异提升五倍（7.45%→1.56%），有效识别不可靠预测。
- 医生可通过对话界面验证或挑战AI结果，兼顾可解释性与临床反馈。
人工智能辅助步态分析有望改善帕金森病（PD）诊疗，但现有临床仪表盘缺乏透明度，无法让医生质疑或反驳AI决策。为此，我们提出Motion2Meaning框架，通过集成化界面实现可质疑的AI。该系统利用可穿戴传感器获取的垂直地面反作用力（vGRF）时间序列作为帕金森病运动状态客观指标。核心包含三部分：步态数据可视化界面（GDVI）、用于预测Hoehn & Yahr分期的一维卷积神经网络（1D-CNN），以及融合新型跨模态解释差异（XMED）防护机制的可质疑语言模型接口（CII）。1D-CNN在公开的PhysioNet步态数据集上达到89.0%的F1分数。XMED在错误预测中检测到解释差异提升五倍（7.45%对比正确预测的1.56%），有效识别模型不可靠情况；语言模型接口使医生可验证正确结果，并成功质疑部分模型错误。人机评估揭示了语言模型事实准确性与临床反馈响应速度之间的权衡。本工作证明，将可穿戴传感分析、可解释AI与可质疑大模型结合，可构建透明、可审计的帕金森步态解读系统，既保留临床监督能力，又发挥先进AI优势。代码已开源：https://github.com/hungdothanh/motion2meaning。
原文摘要 · Abstract (English)
AI-assisted gait analysis holds promise for improving Parkinson's Disease (PD) care, but current clinical dashboards lack transparency and offer no meaningful way for clinicians to interrogate or contest AI decisions. To address this issue, we present Motion2Meaning, a clinician-centered framework that advances Contestable AI through a tightly integrated interface designed for interpretability, oversight, and procedural recourse. Our approach leverages vertical Ground Reaction Force (vGRF) time-series data from wearable sensors as an objective biomarker of PD motor states. The system comprises three key components: a Gait Data Visualization Interface (GDVI), a one-dimensional Convolutional Neural Network (1D-CNN) that predicts Hoehn & Yahr severity stages, and a Contestable Interpretation Interface (CII) that combines our novel Cross-Modal Explanation Discrepancy (XMED) safeguard with a contestable Large Language Model (LLM). Our 1D-CNN achieves 89.0% F1-score on the public PhysioNet gait dataset. XMED successfully identifies model unreliability by detecting a five-fold increase in explanation discrepancies in incorrect predictions (7.45%) compared to correct ones (1.56%), while our LLM-powered interface enables clinicians to validate correct predictions and successfully contest a portion of the model's errors. A human-centered evaluation of this contestable interface reveals a crucial trade-off between the LLM's factual grounding and its readability and responsiveness to clinical feedback. This work demonstrates the feasibility of combining wearable sensor analysis with Explainable AI (XAI) and contestable LLMs to create a transparent, auditable system for PD gait interpretation that maintains clinical oversight while leveraging advanced AI capabilities. Our implementation is publicly available at: https://github.com/hungdothanh/motion2meaning.
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