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According to the World Health Organization, the rate of people aged 60 ormore is growing faster than any other age group in almost every country, andthis trend is not going to change in a near future. Since senior citizens areat high risk of non communicable diseases requiring long-term care, this trendwill challenge the sustainability of the entire health system. Pervasivecomputing can provide innovative methods and tools for early detecting theonset of health issues. In this paper we propose a novel method to detectabnormal behaviors of elderly people living at home. The method relies onmedical models, provided by cognitive neuroscience researchers, describingabnormal activity routines that may indicate the onset of early symptoms ofmild cognitive impairment. A non-intrusive sensor-based infrastructure acquireslow-level data about the interaction of the individual with home appliances andfurniture, as well as data from environmental sensors. Based on those data, anovel hybrid statistical-symbolical technique is used to detect the abnormalbehaviors of the patient, which are communicated to the medical center.Differently from related works, our method can detect abnormal behaviors at afine-grained level, thus providing an important tool to support the medicaldiagnosis. In order to evaluate our method we have developed a prototype of thesystem and acquired a large dataset of abnormal behaviors carried out in aninstrumented smart home. Experimental results show that our technique is ableto detect most anomalies while generating a small number of false positives.

Extended Report: Fine-grained Recognition of Abnormal Behaviors for Early Detection of Mild Cognitive Impairment