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Related Experiment Video

Updated: May 15, 2025

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LLM-Driven Adjustments in Serious Games: A Feasibility Analysis.

Ivana Mostachetti1, Andrea Vitali1, Daniele Regazzoni1

  • 1Department of Management, Information and Production Engineering, University of Bergamo, via Pasubio, Dalmine (Bergamo), Italy.

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Summary

This study shows that large language models (LLMs) can personalize serious games (SGs) for neurological rehabilitation by adjusting difficulty and recommending settings. While feasible, LLM-driven adjustments require further refinement for improved accuracy in telerehabilitation.

Keywords:
ExergamingLarge Language ModelNeurological RehabilitationTelerehabilitationUpper Extremity

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Rehabilitation Medicine

Background:

  • Serious games (SGs) and telerehabilitation are crucial for neurological recovery.
  • Personalization and adaptive difficulty are key features for effective rehabilitation.

Purpose of the Study:

  • To explore integrating a large language model (LLM) into an Assessment Serious Game (ASG).
  • To analyze exercise data and generate personalized rehabilitation recommendations.
  • To assess the LLM's capability in real-time SG adjustments and parameter suggestions.

Main Methods:

  • Acquired medical knowledge from professionals to define target pathologies and parameters.
  • Integrated the ASG with GroqCloud, utilizing a specialized LLM prompt.
  • Designed the prompt to function as a virtual physiotherapist and SG developer for real-time adjustments.

Main Results:

  • The LLM demonstrated effectiveness in recognizing real-time adjustments and following parameter setting instructions.
  • Limitations were observed in the precision of adjustments and numerical parameter recommendations.
  • Preliminary testing confirmed the system's basic capabilities.

Conclusions:

  • A designed LLM prompt is feasible for adjusting SG difficulty and recommending setup parameters in telerehabilitation.
  • Further improvements are needed in LLM reliability and accuracy for clinical application.
  • This approach shows potential for enhancing personalized neurological rehabilitation through AI.