A method for dynamically adjusting the difficulty of rehabilitation training tasks driven by attention level.

Area of Science:

• Neuroscience
• Rehabilitation Medicine
• Human-Computer Interaction

Background:

• Patient engagement is crucial for motor and cognitive rehabilitation.
• Task difficulty and environment influence motivation and adherence.
• Dynamic difficulty adjustment can personalize rehabilitation training.

Purpose of the Study:

• To introduce a dynamic difficulty adjustment method for rehabilitation tasks based on real-time attention levels.
• To evaluate the impact of attention-triggered difficulty adjustments on engagement.
• To assess the influence of different task environments (2D vs. VR) on concentration and immersion.

Main Methods:

• Electroencephalography (EEG) signals were used to train an attention classification model for real-time attention level detection.
• Task difficulty was dynamically adjusted based on participants' fluctuating attention levels.
• A cohort of 30 participants completed tasks in various environments, with data collected via EEG and questionnaires.

Main Results:

• Using attention levels to adjust difficulty significantly improved task concentration within a virtual reality (VR) environment.
• VR environments enhanced immersion, interest, and flow state compared to 2D environments, despite increased physical exertion.
• The combination of VR and attention level feedback proved most effective for engagement.

Conclusions:

• Dynamic difficulty adjustment based on attention levels offers a novel approach to enhance patient engagement in rehabilitation.
• Immersive VR rehabilitation training, guided by attention feedback, can lead to more effective and captivating patient experiences.
• This data-driven, personalized approach holds potential for superior patient outcomes and quality of life.