Low-Mobility Complementary Tool for Patient Follow-Up: A Proof of Concept for E-Health
Résumé
This paper presents the work conducted as part of the E-health, Mobility, and Big Data project (EMOB), which aims to provide a platform for analyzing physical and sedentary activity features and providing an inter-patient interpretation of their healthcare conditions based on signature similarities in their sedentary behavior. Our focus is on designing a dedicated microcontroller-based device to record patients' daily activities. We provide a detailed description of the hardware developed and its role in the project. We also discuss future perspectives on AI integration at the device level and the impact of this project on understanding the relationship between physical activities and improving health conditions, especially for patients with chronic pain and diseases.