Human activity recognition in smart homes: Combining passive RFID and load signatures of electrical devices

Modern societies are facing an important aging of their population, leading to rising economic and social challenges such as the pressure on health support services for semi-autonomous persons. Smart home technology is considered by many researchers as a promising potential solution to help support the needs of elders. It aims to provide cognitive assistance by taking decisions, such as giving hints, suggestions and reminders, with different kinds of effectors (light, sound, screen, etc.) to a resident suffering from cognitive deficits in order to foster their autonomy. To implement such a technology, the first challenge we need to overcome is the recognition of the ongoing inhabitant activity of daily living (ADL). Moreover, to assist them correctly, we also need to be able to detect the perceptive errors they perform. Therefore, we present in this paper a new affordable activity recognition system, based on passive RFID technology and load signatures of appliances, able to detect errors related to cognitive impairment. The whole computational intelligent system is based on a multi-layer model that promotes scalability and adaptability. This system has been implemented and deployed in a real smart home prototype. We also present the promising results of our experiment conducted on real case scenarios about morning routines.