Development and Initial Approach of a Pro-Adaptive Monitoring System for Parkinson Disease Symptoms

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Neurology

Background:

• Parkinson disease (PD) significantly impacts quality of life, particularly due to motor symptoms like tremors and falls.
• Progressive motor symptoms in PD increase fall risk, leading to injuries and reduced independence.

Purpose of the Study:

• To develop a wearable-based pro-adaptive assistive system (AS) for detecting PD symptoms and predicting disease progression.
• To enable adaptive assistance tailored to the patient's real-time condition.

Main Methods:

• Designed a wrist-worn wearable device incorporating an accelerometer for data collection.
• Integrated AI models for pro-adaptive decision-making based on patient sensor data.
• Implemented local data storage and an automatic fall detection alert system for caregivers.

Main Results:

• The wearable device successfully detects resting tremors and falls using accelerometer data.
• The system demonstrates the capability for local data storage and caregiver alerts upon fall detection.
• The pro-adaptive AS architecture facilitates condition-based adaptive assistance.

Conclusions:

• The developed wearable AS shows promise in monitoring Parkinson disease symptoms and falls.
• AI-driven pro-adaptive assistance can be tailored to individual patient needs in real-time.
• This technology has the potential to enhance safety and support for individuals with Parkinson disease.