Abstract
Behavioral biometrics aim at providing algorithms for the automatic recognition of individual behavioral traits, stemming from a person’s actions, attitude, expressions and conduct. In the field of ambient assisted living, behavioral biometrics find an important niche. Individuals suffering from the early stages of neurodegenerative diseases (MCI, Alzheimer’s, dementia) need supervision in their daily activities. In this context, an unobtrusive system to monitor subjects and alert formal and informal carers providing information on both physical and emotional status is of great importance and positively affects multiple stakeholders. The primary aim of this paper is to describe a methodology for recognizing the emotional status of a subject using facial expressions and to identify its uses, in conjunction with pre-existing risk-assessment methodologies, for its integration into the context of a smart monitoring system for subjects suffering from neurodegenerative diseases. Paul Ekman’s research provided the background on the universality of facial expressions as indicators of underlying emotions. The methodology then makes use of computational geometry, image processing and graph theory algorithms for the detection of regions of interest and then a neural network is used for the final classification. Findings are coupled with previous published work for risk assessment and alert generation in the context of an ambient assisted living environment based on Service oriented architecture principles, aimed at remote web-based estimation of the cognitive and physical status of MCI and dementia patients.
Highlights
People older than 64 represent an average of 18.5% of the total European population and growing notably is the age group over 84, as reported by Eurostat in 2015[1]
An aging population is associated with an increase in the number of people suffering from mild cognitive impairment (MCI), such as the range of dementia [24]
Because it is obvious that emotion is a dynamic feature that cannot be used for identification or verification as other biometric traits- it has to fulfil the fundamental requirements of biometric characteristics, as they were earlier described
Summary
People older than 64 represent an average of 18.5% of the total European population and growing notably is the age group over 84, as reported by Eurostat in 2015[1]. An aging population is associated with an increase in the number of people suffering from mild cognitive impairment (MCI), such as the range of dementia [24] These conditions are very challenging from the outset, ranging from the problem of reaching and obtaining a diagnosis, to the tailoring of appropriate care and support for the individual and their caregivers both family and professional [5,6]. There is the need for an innovative approach, to explore different ways of working with increased collaboration across disciplines: healthcare experts and technologists and greater working partnerships between older people and their formal and informal caregivers [7,8,9]
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