Interest of the minimum edit distance to detect behaviour change of the elderly person.

In this article, a solution to detect the change of behaviour of the elderly person based on the person's activities of daily living is proposed. This work is based on the hypothesis that the person attaches importance to a rhythmic sequence of days and activities per day. The day of the elderly person is described by a succession of activities, and each activity is associated to a posture (lying down, sitting, standing, absent). Postures are estimated from image analysis measured by thermal or depth cameras in order to preserve the anonymity of the person. The change in posture succession is calculated using the minimum edit distance with respect to the routine day. The number of permutations/inversions reflects the change in the person's behaviour. The method was tested on two elderly persons recorded by thermal and depth cameras during 85 days in a retirement home. It is shown that for a person with a life change behaviour, the average number of permutations and interquartile range, before and after changes, are 41 [28], [48] and 57 [55-62] respectively compared to the learned routine day. The Wilcoxon test confirmed the significant difference between these two periods.Clinical Relevance- Monitoring the daily routine provides indicators for detecting changes in the behaviour of an elderly person.

Detection of changes in the behaviour of the elderly person.

In this paper, we propose a solution for detecting changes in the behaviour of the elderly person based on the monitoring of activities of daily living (ADL). The elderly person's daily routine is characterized by the following five indexes: 1) percentage of time lying down, 2) percentage of time sitting, 3) percentage of time standing, 4) percentage of time absent from home, and 5) number of falls during the day. In our framework, these indexes are computed using characteristics extracted from depth and thermal data. We hypothesize that elderly persons have a well-defined, regular life routine, organized around their environment, habits, and social relations. Then, given the indexes values, a day is defined as routine or non-routine day. Thus, looking for changes of day type allows to detect changes in a person's routine. The method has been tested on a database of depth and thermal images recorded in a nursing home over an 85 days period. These tests proved the reliability of the proposed method.

Detecting falls and estimation of daily habits with depth images using machine learning algorithms.

Different approaches have been proposed in the literature to detect the fall of an elderly person. In this paper, we propose a fall detection method based on the classification of parameters extracted from depth images. Three supervised learning methods are compared: decision tree, K-Nearest Neighbors (K-NN) and Random Forests (RF). The methods have been tested on a database of depth images recorded in a nursing home over a period of 43 days. The Random Forests based method yields the best results, achieving 93% sensitivity and 100% specificity when we restrict our study around the bed. Furthermore, this paper also proposes a 37 days follow-up of the person, to try and estimate his or her daily habits.