Fine-grained recognition of abnormal behaviors for early detection of mild cognitive impairment

Abstract

Abstract-According to the World Health Organization, the rate of people aged 60 or more is growing faster than any other age group in almost every country, and this trend is not going to change in a near future. Since senior citizens are at high risk of non communicable diseases requiring long-term care, this trend will challenge the sustainability of the entire health system. Pervasive computing can provide innovative methods and tools for early detecting the onset of health issues. In this paper we propose a novel method relying on medical models, provided by cognitive neuroscience researchers, describing abnormal activity routines that may indicate the onset of early symptoms of mild cognitive impairment. A non-intrusive sensor-based infrastructure acquires low-level data about the interaction of the individual with home appliances and furniture, as well as data from environmental sensors. Based on those data, a novel hybrid statistical-symbolical technique is used to detect first the activities being performed and then the abnormal aspects in carrying out those activities, which are communicated to the medical center. Differently from related works, our method can detect abnormal behaviors at a fine-grained level, thus providing an important tool to support the medical diagnosis. In order to evaluate our method we have developed a prototype of the system and acquired a large dataset of abnormal behaviors carried out in an instrumented smart home. Experimental results show that our technique has a high precision while generating a small number of false positives.

Human Activity Recognition in Smart-Home Environments for Health-Care Applications

“…In Chapter 6, we propose a novel technique for fine-grained abnormal behavior recognition [31,3,17]. Our method relies on medical models describing abnormal activity routines that may indicate the onset of early symptoms of MCI.…”

Modeling patterns of activities using activity curves

“…In another work, Hodges et al [24] correlated sensor events gathered during a coffee-making task with an individual’s neuropsychological score. Similarly, in an another research effort by Riboni et al[25] researchers developed a Fine-grained Abnormal BEhavior Recognition (FABER) algorithm to detect abnormal behaviour using a statistical-symbolic technique. These researchers hypothesize that such abnormal activity routines may indicate the onset of early symptoms of cognitive decline.…”

Self-tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data

“…In this context, researcher also focus on pattern detection, i.e., analyzing a specic sequence of activities [17,27] to verify given references. In contrast, process mining enables to infer and extract routines that occur during the daily routine of a patient from a hidden structure.…”