Artificial Intelligence Applications to Support Physical Activity, Mobility, and Fatigue Management in People with...

Area of Science:

• Neurology
• Rehabilitation Science
• Artificial Intelligence

Background:

• Physical activity is crucial for managing multiple sclerosis (MS), but adherence is challenging due to symptoms like fatigue and mobility issues.
• Artificial intelligence (AI) offers potential for personalized monitoring and support by analyzing diverse data streams.

Purpose of the Study:

• To conduct a scoping review of original AI-based studies focused on physical activity, gait, mobility, fatigue, and fall risk in individuals with MS.
• To map the landscape of AI applications and identify research gaps in MS rehabilitation.

Main Methods:

• Systematic search of literature from January 2016 to May 2026 across multiple databases, adhering to PRISMA-ScR guidelines.
• Inclusion criteria focused on studies involving MS patients, explicit AI/ML components, and activity-related clinical domains.
• Screening of 332 records, with 21 original AI studies meeting eligibility criteria.

Main Results:

• The 21 included studies explored AI applications in gait analysis, fall risk prediction, fatigue detection, and physical activity monitoring using sensors, wearables, and smartphones.
• Specific AI techniques included deep learning for sensor data, gait speed estimation, and predictive modeling for fatigue and health states.

Conclusions:

• AI can effectively extract meaningful patterns from various data sources in MS, including gait, wearable, and patient-reported data.
• Current evidence is insufficient to confirm that AI-supported systems improve physical activity, adherence, fatigue, fall risk, or functional independence in MS.
• Future research requires prospective, validated, human-supervised studies focusing on fatigue-awareness and safety for patient-centered MS rehabilitation.