用新模型分析康复运动数据，提升中风后肢体恢复预测精度
Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large Models
- 提出xLSTM和Lag-Llama模型，用于下肢康复动作时序建模
- 在SIAT-LLMD数据集上实现关节运动与动力学参数的精准短期预测
- 为个性化康复方案设计提供可落地的AI技术支撑
本研究探讨新型模型架构与大规模基础模型在下肢康复运动时序数据分析中的应用，旨在利用机器学习与人工智能进展，推动中风后患者肢体运动功能恢复的主动康复指导策略。基于中国科学院深圳先进技术研究院提出的SIAT-LLMD下肢运动数据集，系统阐述了创新的xLSTM架构与基础模型Lag-Llama在关节运动学与动力学参数短期时序预测任务中的实现与分析结果。研究为人工智能赋能医疗康复应用提供了新视角，展示了前沿模型架构与大模型在康复医学时序预测中的潜力。这些发现为未来个性化康复方案的实施奠定了理论基础，对临床实践中定制化治疗干预的发展具有重要意义。
原文摘要 · Abstract (English)
This study investigates the application of novel model architectures and large-scale foundational models in temporal series analysis of lower limb rehabilitation motion data, aiming to leverage advancements in machine learning and artificial intelligence to empower active rehabilitation guidance strategies for post-stroke patients in limb motor function recovery. Utilizing the SIAT-LLMD dataset of lower limb movement data proposed by the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, we systematically elucidate the implementation and analytical outcomes of the innovative xLSTM architecture and the foundational model Lag-Llama in short-term temporal prediction tasks involving joint kinematics and dynamics parameters. The research provides novel insights for AI-enabled medical rehabilitation applications, demonstrating the potential of cutting-edge model architectures and large-scale models in rehabilitation medicine temporal prediction. These findings establish theoretical foundations for future applications of personalized rehabilitation regimens, offering significant implications for the development of customized therapeutic interventions in clinical practice.
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