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Related Concept Videos

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
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Updated: Sep 22, 2025

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Applying Goal-Oriented Modelling for Machine Learning Based Rehabilitation Care.

Carola Gißke1, Jin Liu2, Kai Gand1

  • 1Technische Universität Dresden, Faculty of Business and Economics, Research Group Digital Health, Germany.

Studies in Health Technology and Informatics
|May 25, 2022
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Summary
This summary is machine-generated.

Virtual coaches offer personalized rehabilitation at home. Conceptual modeling and machine learning enable adaptive patient support for better recovery outcomes.

Keywords:
Goal-oriented modelinge-healthmachine learningvirtual coaching

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Area of Science:

  • Digital Health
  • Rehabilitation Technology
  • Artificial Intelligence in Medicine

Background:

  • Virtual coaches can provide continuous rehabilitation support in home environments for patients with acute illnesses.
  • Current systems require medically accurate instructions and individual patient responsiveness.
  • Personalization of rehabilitation is crucial for effective patient recovery.

Purpose of the Study:

  • To demonstrate how conceptual modeling, specifically the i* goal-oriented modeling language, can structure virtual coaching applications.
  • To explore the integration of machine learning algorithms for adaptive and personalized rehabilitation.
  • To compare the i* approach with Business Process Model and Notation (BPMN) for process modeling.

Main Methods:

  • Utilizing a virtual coaching scenario to illustrate conceptual structuring.
  • Employing the i* goal-oriented modeling language for application design.
  • Comparing i* with BPMN for process modeling effectiveness.
  • Outlining the implementation of machine learning algorithms within the conceptual model.

Main Results:

  • The study demonstrates a conceptual framework for virtual coaching systems using i* modeling.
  • It highlights the potential of i* to capture and structure medical knowledge for AI-driven rehabilitation.
  • The research provides a comparative analysis of i* and BPMN in the context of virtual coaching.

Conclusions:

  • Conceptual modeling with i* is effective for structuring virtual coaching applications.
  • Machine learning can be integrated to personalize rehabilitation processes.
  • This approach supports the development of intelligent virtual coaches for home-based rehabilitation.