Inclusive Monitoring in Dementia: Progress Towards a Wrist Wearable Based Solution

INTRODUCTION: The rapidly aging global population presents significant challenges, particularly in managing neurodegenerative diseases such as dementia. Addressing these challenges requires innovative solutions that ensure continuous care and monitoring of elderly individuals. Wearable technology offers a path to more inclusive and efficient care for older adults living with cognitive decline. OBJECTIVES: This paper presents the design, development, and refinement of an integrated wrist-worn wearable system to support the monitoring and care of elderly people living with dementia, expanding a previous proof of concept into a compact and energy-efficient prototype optimized for real-world usability. METHODS: The proposed system integrates advanced sensor technologies. It includes optical (MAX30101), inertial (BMI270), and GNSS (CAM-M10Q) sensors for monitor heart rate, blood oxygen levels, motion, and location. Data are transmitted via Bluetooth Low Energy (BLE) for indoor communication and LoRa for outdoor connectivity. A custom 4×4 cm PCB was designed to consolidate all components—microcontroller, sensors, power efficiency, communication modules, and display—into a single compact and robust layout. The design process also emphasized ergonomic comfort and usability through iterative prototyping and 3D modelling. RESULTS: The refined prototype significantly reduced the physical dimensions compared with the original proof of concept, while improving power efficiency, mechanical stability, and user comfort. The device layout and button interface were optimized to ensure intuitive interaction and safe daily use by elderly individuals. CONCLUSION: The developed prototype establishes the foundation for a fully functional, energy-efficient wearable system tailored to dementia monitoring. Future work will include firmware optimization, extended functional validation, and the integration of intelligent analytics for behaviour recognition and anomaly detection, aiming to provide caregivers with timely and actionable health insights.