The increase in the elderly population has led to the need for new medical, social, and care services, resulting in a significant rise in health costs and the number of health workers involved. For example, IoT (Internet of Things) and wearable technologies can help contain healthcare spending and enable better living conditions of elderly. Moreover, nanotechnologies such as MEMS (micro-electromechanical system) that offer the advantage of small size, negligible need for power and motion acquisition are of considerable benefit. These technologies are able to detect and signal dangerous situations in order to ensure immediate action. In this article, we present an implementation of an IoT application on a latest-generation microcontroller. Kinematics and environmental data are transferred to a CNN (Convolutional Neural Network) to recognize the daily activities of the elderly in their homes or nursing homes. Finally, to determine the position of subjects, we associate the prototype with a positioning system on the ultrasonic platform. Finally, applying the Edge Machine Learning technique, we developed an application on the STM32L475VG microprocessor on which motion acquisition and activity recognition functions are activated.
|Communications in Computer and Information Science
|International Conference on Applied Intelligence and Informatics
|1/09/22 → 3/09/22
- Ehemaliges Research Field - Societal Resilience & Security