# Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson's Disease Gait Interpretation

arXiv: https://arxiv.org/abs/2512.08934 · HTML: https://mlsanity.com/paper/2512.08934/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 21 of 21)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [Capabilities of GPT-5 on Multimodal Medical Reasoning](https://mlsanity.com/paper/2508.08224/) (2025)
- [Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait](https://mlsanity.com/paper/1910.11509/) (2019)
- [Towards Better Explanations for Object Detection](https://mlsanity.com/paper/2306.02744/) (2023)
- [Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2108.01737/) (2021)
- [LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks](https://mlsanity.com/paper/2402.12525/) (2024)
- [XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approach](https://mlsanity.com/paper/2407.11771/) (2024)
- [1D-Convolutional transformer for Parkinson disease diagnosis from gait](https://mlsanity.com/paper/2311.03177/) (2023)
- [Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors](https://mlsanity.com/paper/2505.11612/) (2025)
- [ODExAI: A Comprehensive Object Detection Explainable AI Evaluation](https://mlsanity.com/paper/2504.19249/) (2025)
- [Making Sense of the Unsensible: Reflection, Survey, and Challenges for XAI in Large Language Models Toward Human-Centered AI](https://mlsanity.com/paper/2505.20305/) (2025)
- [ConGaIT: A Clinician-Centered Dashboard for Contestable AI in Parkinson's Disease Care](https://mlsanity.com/paper/2507.22300/) (2025)
- [Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach](https://mlsanity.com/paper/2404.15335/) (2024)

## Built on by (most cited first, 6 of 6)

- [Adaptive Collaboration of Arena-Based Argumentative LLMs for Explainable and Contestable Legal Reasoning](https://mlsanity.com/paper/2602.18916/) (2026)
- [Contestable Multi-Agent Debate with Arena-based Argumentative Computation for Multimedia Verification](https://mlsanity.com/paper/2605.14495/) (2026)
- [Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework](https://mlsanity.com/paper/2605.14550/) (2026)
- [Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation](https://mlsanity.com/paper/2607.15202/) (2026)
- [CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs](https://mlsanity.com/paper/2608.05107/) (2026)
- [Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination](https://mlsanity.com/paper/2608.05391/) (2026)

## Abstract (arXiv)

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.