Nonintrusive system for assistance and guidance in smart homes based on electrical devices identification. Issue 19 (1st November 2015)

• Record Type: Journal Article
• Authors: Belley, Corinne; Gaboury, Sebastien; Bouchard, Bruno; Bouzouane, Abdenour
• Is Part Of: Expert systems with applications. Volume 42:Issue 19(2015)
• Page Start: 6552
• Page End: 6577
• Publication Date: 2015-11-01
• Subjects: Assistive service -- Guidance -- Cognitive impairment -- Activity recognition -- Smart home -- Load signature -- Nonintrusive; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.04.024
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We present an assistive system for guiding cognitively-impaired people in performing daily activities. The system is based on electrical signal analysis with a single sensor. The system is able to detect cognitive errors and send prompts accordingly. The system has been implemented, deployed and tested in real home environment. The simulation of real-case scenarios has shown promising results. Abstract: Recently, sensors and actuators have quickly spread throughout our everyday life. These devices are robust, cheap, accessible, connected to the Internet, etc. With the growing needs in terms of human and medical resources to help cognitively-impaired people to remain at home, researchers are investing in new ways to exploit this technology with artificial intelligence, in order to build expert systems to assist the residents in their daily activities. Several systems have been proposed in the last few years, mostly based on binary sensors, cameras and other sensors such as Radio-frequency identification (RFID) tags. Cameras are very intrusive, binary sensors (such as movement detectors) give only basic information, and other types of sensors (such as RFID) need complex deployment. In this context, this paper presents a new assistive expert system based on electric device identification to address the problem of guidance and supervision in the performance of activities for people with cognitive disorders living in a smart home. This system is solely based on a single power analyzer placed in the electric panel. We propose an algorithmic approach used to recognize erratic behaviors related to cognitive deficits and provides cues to guide the person in the completion of an ongoing task. This is achieved through load signatures study of appliances represented by three features (active power (P), reactive power (Q) and line-to-neutral), which allows to determine the errors committed by the resident. We implemented this system within a genuine smart-home prototype equipped with household appliances used by the patient during his morning routines. Different multimedia prompting devices (iPad, screen, speakers, etc.) were used. We tested the system with real-case scenarios modeled from former clinical trials, allowing demonstration of accuracy and effectiveness of our system in assisting a cognitively-impaired resident in the completion of daily activities.