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Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large Models

arXiv 2504.03799 ↗First posted 4 Apr 2025eess.SP cs.AIcited by 0

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Abstract (arXiv)

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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References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
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  8. 2023Large Language Models are Few-Shot Health Learnerscited by 118
  9. 2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 113
  10. 2021Dive into Deep Learningcited by 111
  11. 2014End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Resultscited by 68
  12. 2016Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMscited by 21
3 more
  1. 2024xLSTMTime : Long-term Time Series Forecasting With xLSTMcited by 14
  2. 2022Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfercited by 9
  3. 2021Lower-limb kinematics and kinetics during continuously varying human locomotioncited by 7

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