Identifying and Evaluating User-Centered Requirements for Pro-Adaptive Assistive Systems in Parkinson Disease

Area of Science:

• Neurology
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Parkinson disease (PD) presents progressive motor and non-motor symptoms, impacting quality of life for individuals with PD (PwPD) and increasing caregiver burden.
• Current assistive systems (AS) often lack the dynamic adaptability required to meet the evolving needs of PwPD.
• Pro-adaptive AS leveraging digital twin technology show promise in addressing these limitations.

Purpose of the Study:

• To identify Parkinson disease-specific requirement clusters for adaptive assistive systems.
• To derive user-centered key requirements for these systems.
• To assess the technical feasibility and availability of solutions for developing adaptive AS.

Main Methods:

• Compiled relevant needs from ICD-10, scientific literature, and German care-level criteria.
• Filtered needs based on addressability by AS and measurability of AS effectiveness.
• Grouped needs into 16 heuristic clusters, developed interview questions, and conducted interviews with PwPD and informal caregivers.
• Assessed technical feasibility and solution availability via expert ratings.

Main Results:

• Identified physical symptoms and memory impairments as the most critical requirements for adaptive AS in PD.
• A total of 16 heuristic requirement clusters were defined.
• Technical experts provided feasibility and availability ratings for each cluster.

Conclusions:

• Proposes an AI-based digital twin model as a pro-adaptive solution for detecting PD symptoms and monitoring disease progression.
• Emphasizes the need for user-centered requirements in developing adaptive assistive systems for Parkinson disease.
• Highlights the potential of digital twin technology to personalize and improve care for PwPD.