16. Human Computer Confluence in the Smart Home Paradigm: Detecting Human States and Behaviours for 24/7 Support of Mild-Cognitive Impairments
Georgios Papamakarios, Dimitrios Giakoumis, Manolis Vasileiadis, Anastasios Drosou, Dimitrios K. Tzovaras · 2015
The research advances of recent years in the area of smart homes highlight the prospect of future homes equipped with sophisticated systems that monitor the resident and cater for her/his needs.A basic prerequisite for this is the development of non-obtrusive methods to detect human states and behaviours at home.Especially in the case of residents with mild cognitive impairments (MCI), such systems should be able to identify abnormal behaviours and trends, supporting independent living and well-being through appropriate interventions.The integration of monitoring and intervention mechanisms within a home needs special attention, given the fact that after a period of time, these will be perceived from the resident as inherent home features, altering the traditional way that the notion of home is perceived by the mind, transforming it into a Human Computer Confluence (HCC) paradigm.Activity detection and behaviour monitoring in smart homes is typically based on sensors (e.g. on appliances) or computer vision techniques.In this chapter, both approaches are explored and a system that integrates sensors with resident visionbased location tracking is presented.Location tracking is based herein on low-cost depth cameras (Kinect), allowing for privacy preserving, unobtrusive monitoring.The focus is on detecting the MCI resident's Activities of Daily Living (ADLs), as well as extracting parameters, toward identifying abnormalities within her/his behaviour.Preliminary results show that the sole use of user position trajectories has potential toward effective ADL and abnormality detection, whereas the addition of sensors further enhances effectiveness, with increase however in system cost and complexity.