title: SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment.
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.
authors.affiliation: Department of Mathematics and Computer Science, Università degli Studi di Cagliari, Via Ospedale 72, I-09124 Cagliari, Italy. Electronic address: riboni@unica.it.
authors.forename: Daniele
authors.initials: D
authors.lastname: Riboni
authors.affiliation: Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, I-20135 Milano, Italy. Electronic address: claudio.bettini@unimi.it.
authors.forename: Claudio
authors.initials: C
authors.lastname: Bettini
authors.affiliation: Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, I-20135 Milano, Italy. Electronic address: gabriele.civitarese@unimi.it.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Civitarese
authors.affiliation: Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, I-20135 Milano, Italy. Electronic address: zaffar.janjua@unimi.it.
authors.forename: Zaffar Haider
authors.initials: ZH
authors.lastname: Janjua
authors.affiliation: Philips Research Personal Health, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands. Electronic address: rim.helaoui@philips.com.
authors.forename: Rim
authors.initials: R
authors.lastname: Helaoui
mesh_terms: D003072:Cognition Disorders / Q000175:diagnosis*; D042241:Early Diagnosis; D006801:Humans; D001523:Mental Disorders / Q000175:diagnosis*; D008962:Models, Theoretical
keywords: Abnormal behavior detection; Activity recognition; Cognitive decline; Mild cognitive impairment; Pervasive computing