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This study introduces an automated social robot system to assist elderly and brain-injured individuals with daily tasks. The robot enhances patient autonomy and quality of life through guided task completion and progress monitoring.

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Area of Science:

  • Gerontology and Rehabilitation Engineering
  • Artificial Intelligence in Healthcare
  • Human-Robot Interaction

Background:

  • Developed countries face challenges with aging populations and individuals with brain injuries requiring specialized daily assistance.
  • Current care models often necessitate constant supervision, impacting patient autonomy and caregiver burden.

Purpose of the Study:

  • To develop and validate an automated scheduling system on a social robot for assisting individuals with daily tasks.
  • To enhance patient self-autonomy and improve their overall quality of life.

Main Methods:

  • Deployment of a social robot equipped with an automated scheduling system for task management.
  • Utilization of an ensemble of deep learning techniques for monitoring task execution and detecting errors.
  • Customizable scheduling interface for caregivers and authorized relatives.

Main Results:

  • The social robot successfully guided patients through daily tasks, improving adherence and completion rates.
  • The deep learning system accurately detected deviations in task performance, triggering timely alerts.
  • Caregiver and patient feedback indicated enhanced autonomy and satisfaction with the system.

Conclusions:

  • The automated social robot system offers a promising solution for supporting elderly and brain-injured individuals in managing daily activities.
  • This technology has the potential to significantly improve the quality of life and independence of vulnerable populations.
  • The system's effectiveness was validated through supervised experimentation, demonstrating its practical applicability.