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

Enhancements in the rehabilitation of motor and cognitive functions are significantly attainable through proactive patient engagement. The difficulty of rehabilitation tasks and the environment in which they are conducted directly impact patient motivation. Consequently, this study introduces a dynamic difficulty adjustment method for rehabilitation training tasks based on attention levels, designed to adjust task difficulty in real-time and augment the focus of participants on their training tasks. 
Approach: EEG signals from participants were harnessed to train an attention classification model, enabling the acquisition of real-time attention level signals. Task difficulty levels were adjusted based on the fluctuating attention levels. A cohort of 30 participants was engaged to evaluate: (1) the impact on engagement when attention levels are utilized as dynamic difficulty 18 triggers; (2) the influence of various task environments on concentration. The experiment was assessed through EEG signals and questionnaire data, with frequency domain analysis conducted on EEG signals to calculate concentration values and statistical analysis performed on additional data. 
Main Results: The findings reveal that within an identical virtual reality (VR) environment, leveraging attention levels as triggers for difficulty adjustment markedly improves participants' task concentration. Compared to 2D environments, VR environments substantially enhance participants' sense of immersion, interest, and flow state, albeit with increased physical exertion during training. The integration of VR and attention level feedback is deemed the most effective strategy. 
Significance: These exploratory insights indicate that the proposed method paves a novel path for boosting patient engagement in rehabilitation. Immersive rehabilitation training, driven by attention levels, promises a more effective and captivating patient experience. This study advances the field by offering data-driven, personalized rehabilitation approaches, potentially culminating in superior patient outcomes and enhanced quality of life.