Perspectives in Home TeleHealthCare System: Daily Routine Nycthemeral Rhythm Monitoring from Location Data

Free of most social constraints, elderly people tend to perform activities of daily living following the same sequence. This paper proposes a method for medical telesurveillance to detect and quantify a nycthemeral shift in this daily routine. While this common phenomenon is mostly mild, in acute cases, however, it may reveal a pathological behavior requiring a rapid medical examination. This method allows to compare two sequences of activities using the Hamming distance and to interpret the result according to the Gumbel distribution. It may be used to compare either consecutive sequences thereby taking into account evolution in the habits or a sequence to the person's individual activity profile to detect dementia's onset. In this early stage, only elementary activities were considered. That is the reason why location data were used to monitor the person's nycthemeral rhythm of activity. IR sensors placed in her own flat allowed us to follow-up the inhabitant's successive activities. Improvements of this method have already been planned. They include the use of a multi-sensors network to monitor both actimetric (location, movement, posture) and physiological nycthemeral rhythms (ECG, respiratory frequency) and to detect the use of particular items (fridge, chairs, bed). This more sophisticated sensors network will allow us to monitor more complex tasks execution and then to detect pathological behaviors such as perseveration in a task or wandering. On the other hand, multiplying sensors will require more storage capacities and the use of time-consuming data fusion tools. Therefore, a classification phase will be necessary to reduce as possible the number of relevant sensors.

I. Introduction

Aging in place at home is a natural wish which is more and more difficult to fulfill because of the numerous cares most of elderly people need. The ageing of the population and the growing prevalence of neurodegenerative diseases synonymous of extreme dependence make matters worse. Every 7 seconds in the world, someone develops dementia which is in half the cases due to Alzheimer's disease, the main cause of admission in institution [1]. This kind of disease is characterized by a slowly and ineluctable impairment of the nervous system which results in a loss of abilities. Activities of daily living (ADL) require more and more help, their sequence is forgotten as well as the way to achieve them. Detecting these diseases' onset as soon as possible is very important to improve the efficiency of the treatments which aim at stabilizing symptoms and put the entry in dependence back. The general purpose is to support the person's autonomy and to maintain her/him in her/his own environment as long as possible. This last point is particularly important for the person's quality and expectancy of life. Unfortunately, no automatic and noninvasive mean of detection is available yet.