Daniele Riboni1, Claudio Bettini2, Gabriele Civitarese3, Zaffar Haider Janjua4, Rim Helaoui5. 1. Department of Mathematics and Computer Science, Università degli Studi di Cagliari, Via Ospedale 72, I-09124 Cagliari, Italy. Electronic address: riboni@unica.it. 2. Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, I-20135 Milano, Italy. Electronic address: claudio.bettini@unimi.it. 3. Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, I-20135 Milano, Italy. Electronic address: gabriele.civitarese@unimi.it. 4. Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, I-20135 Milano, Italy. Electronic address: zaffar.janjua@unimi.it. 5. Philips Research Personal Health, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands. Electronic address: rim.helaoui@philips.com.
Abstract
OBJECTIVE: In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective. METHODS: A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level. RESULTS: We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates.
OBJECTIVE: In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective. METHODS: A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level. RESULTS: We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates.
Authors: Monika A Myszczynska; Poojitha N Ojamies; Alix M B Lacoste; Daniel Neil; Amir Saffari; Richard Mead; Guillaume M Hautbergue; Joanna D Holbrook; Laura Ferraiuolo Journal: Nat Rev Neurol Date: 2020-07-15 Impact factor: 42.937