Fusion of Multiple Sensors Sources in a Smart Home to Detect Scenarios of Activities in Ambient Assisted Living

Fusion of Multiple Sensors Sources in a Smart Home to Detect Scenarios of Activities in Ambient Assisted Living

Norbert Noury (University of Lyon, France), Pierre Barralon (Tecnalia Health Technologies Unit, Spain & University of Grenoble, France), Nicolas Vuillerme (University of Grenoble, France) and Anthony Fleury (University of Lille and University of Grenoble, France)
Copyright: © 2012 |Pages: 16
DOI: 10.4018/jehmc.2012070103
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This work takes place within the framework of Smart Homes, with the goal to monitor the activities of elderly people, living independently at home, in order to continuously assess their level of activity and therefore their autonomy. A method is proposed for the selection of a range of sensors and for multiple data fusion. The system was evaluated on 7 young and 4 elderly healthy subjects who performed scenarios of daily activities (sleeping, eating, walking, and transfer) within a controlled environment. These activities were correctly classified with an overall sensitivity and specificity of 67.0% (out of 267 activities) and 52.6% (502) for the group of young people, and of 86.9% (222) and 59.3% (492) for the elderly group. The results were better with activities commonly performed in a dedicated location (i.e., taking meals in the kitchen, toileting in the bathroom). The results are acceptable with a reduced set of sensors although numerous and/or more informative sensors (i.e., video, sound detection, sensitive floors, etc.) give higher results at the cost of more cumbersome and costly systems, difficult to deploy in a private home and eventually more intrusive.
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The ability of an elderly person to live independently in their own home depends on their autonomy to perform the basic actions involved in daily living: to transfer to/from bed and in/out of a chair, to move around and out of the flat, to wash, to use the toilet, etc. Actually, there is a direct relationship between the number of activities performed daily by the person and their level of autonomy.

The level of autonomy of a fragile person is currently estimated by geriatricians with using manual scales. One such scale, the Activities of Daily Living (ADL) (Katz, 1963), involves 6 tasks (bathing, dressing, toileting, transferring, continence and eating) which are individually assessed by the professional as being “autonomous,” “partially autonomous,” or “non autonomous” for a given patient. This assessment is operator-dependent and cannot be performed with sufficient frequency to detect the slow trends characteristic of a loss of autonomy. Thus, there is a need for a system which can perform this evaluation in a more objective manner and on a more regular basis.

The Eureka project “DynaPort” (Van Lummel, 1996) proposed a method to monitor the activities of daily living using a wearable accelerometer sensor; it did not aim at detecting the criteria included in the ADL scale. Glascock (2000) used several sensors fixed on household appliances and furniture (e.g., fridge or cupboard doors) to detect some tasks on the scale Instrumented Activities of Daily Living (IADL) (food preparation, housekeeping, use of the telephone, etc). Duchene (2004) proposed a data fusion method to extract patterns which present similarities, in multidimensional and heterogeneous signals. She considered 4 parameters (displacement, postures, activity level and heart rate) and extracted similar patterns using different metrics. However, the current activity was not identified precisely.

At present, no system proposes to automatically and continuously detect ADLs. We therefore seek to address this goal, while using a reduced set of sensors in order to keep the solution practicable and cost effective. In a previous study we developed a kinematic sensor, called “Actimometer” (Noury, 2002; Barralon, 2005), fixed onto the chest of the person to detect the kinematics (postures, transfers, walking) of the subject, and we placed presence detectors in the rooms of our experimental flat to determine the patient’s spatial context (kitchen, bedroom, etc.).

In the first part of this paper we present the framework of multi-sensor fusion, then the selection of various sources of information and eventually the method we proposed for the data fusion. In the second part we describe our experimental protocol and results obtained with two groups of people, young and elderly.

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