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<p>Robotic rehabilitation is becoming a standard in post-stroke physical rehabilitation, and these setups, often coupled with virtual exercises, collect a large and finely grained amount of data about patients’ motor performance, in terms of kinematics and force interactions. Given the high resolution of data throughout the rehabilitation treatment, invaluable information is concealed, especially if oriented towards </p><p>predictive systems and decision support systems. Nevertheless, a comprehensive understanding of how manipulating these datasets with machine-learning to produce such outputs is still missing. This study leverages comprehensive robotic-assisted rehabilitation data to systematically investigate clinical outcome predictions (FMA, ARAT and MI) and robot parameters suggestions based solely on kinematic and demographic data. Our method significantly outperforms conventional approaches on both tasks demonstrating the potential of systematic data handling in advancing rehabilitation practices. Moreover, under the explainable-AI policies, a focus on prediction power of variables and a clinical knowledge base of predicted outcome are provided.</p>

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Systematic data management for effective AI-driven decision support systems in robotic rehabilitation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月30日

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  • Anastasios Tzepkenlis,
  • Cristian Camardella,
  • Marco Germanotta,
  • Irene Giovanna Aprile,
  • Maria Cristina Mauro,
  • Daniele Leonardis,
  • Antonio Frisoli

摘要

Robotic rehabilitation is becoming a standard in post-stroke physical rehabilitation, and these setups, often coupled with virtual exercises, collect a large and finely grained amount of data about patients’ motor performance, in terms of kinematics and force interactions. Given the high resolution of data throughout the rehabilitation treatment, invaluable information is concealed, especially if oriented towards

predictive systems and decision support systems. Nevertheless, a comprehensive understanding of how manipulating these datasets with machine-learning to produce such outputs is still missing. This study leverages comprehensive robotic-assisted rehabilitation data to systematically investigate clinical outcome predictions (FMA, ARAT and MI) and robot parameters suggestions based solely on kinematic and demographic data. Our method significantly outperforms conventional approaches on both tasks demonstrating the potential of systematic data handling in advancing rehabilitation practices. Moreover, under the explainable-AI policies, a focus on prediction power of variables and a clinical knowledge base of predicted outcome are provided.