Since the population of the elderly grows highly, the improvement of the quality of life of elderly at home is of a great importance. This can be achieved through the development of technologies for monitoring their activities at home. In this context, we propose an activity monitoring system which aims to achieve behavior analysis of elderly people. The proposed system consists of an approach combining heterogeneous sensor data to recognize activities at home. This approach combines data provided by video cameras with data provided by environmental sensors attached to house furnishings. In this paper, we validate the proposed activity monitoring system for the recognition of a set of daily activities (e.g. using kitchen equipment, preparing meal) for 9 real elderly volunteers living in an experimental apartment. We compare the behavioral profile between the 9 elderly volunteers. This study shows that the proposed system is thoroughly accepted by the elderly and it is also well appreciated by the medical staff.
Mailland, O. Guerin, A computer system to monitor older adults at home: Preliminary results. Gerontechnology 2009;8(3):129-139, doi: 10.4017/gt.2009.08.03.011.00 Determining the individual transition from the 3 rd to the 4 th or frailty phase of life is important for both the safety of the older person and to support the care provider. We developed an automatic monitoring system consisting of cameras and different sensors that analyze human behaviors and looks for changes in activities by detecting the presence of people, their movements, and automatically recognizing events and Activities of Daily Living (ADLs). Assessment took place in a laboratory environment (GERHOME) comprised of four rooms (kitchen, living-room, bedroom, and bathroom). Data from 2 volunteers (64 and 85 years old) were analyzed. Precision in recognizing postures and events ranged from 62-94%, while sensitivity fell in the range of 62-87%. The system could differentiate ADL levels for the 64 and 85 year old subjects. These results are promising and merit replication and extension. Considerable work remains before the complete transition from 3 rd to 4 th life phase can be reliably detected. The GERHOME system is promising in this respect
Abstract. This paper presents a cognitive vision approach to recognize a set of interesting activities of daily living (ADLs) for elderly at home. The proposed approach is composed of a video analysis component and an activity recognition component. A video analysis component contains person detection, person tracking and human posture recognition. A human posture recognition is composed of a set of postures models and a dedicated human posture recognition algorithm. Activity recognition component contains a set of video event models and a dedicated video event recognition algorithm. In this study, we collaborate with medical experts (gerontologists from Nice hospital) to define and model a set of scenarios related to the interesting activities of elderly. Some of these activities require to detect a fine description of human body such as postures. For this purpose, we propose ten 3D key human postures usefull to recognize a set of interesting human activities regardless of the environment. Using these 3D key human postures, we have modeled thirty four video events, simple ones such as "a person is standing" and composite ones such as "a person is feeling faint". We have also adapted a video event recognition algorithm to detect in real time some activities of interest by adding posture. The novelty of our approach is the proposed 3D key postures and the set of activity models of elderly person living alone in her/his own home.To validate our proposed models, we have performed a set of experiments in the Gerhome laboratory which is a realistic site reproducing the environment of a typical apartment. For these experiments, we have acquired and processed ten video sequences with one actor. The duration of each video sequence is about ten minutes and each video contains about 4800 frames.
In order to fully capture the complexity of the behavioural, functioning and cognitive disturbances in Alzheimer Disease (AD) and related disorders information and communication techniques (ICT), could be of interest. This article presents using 3 clinical cases the feasibility results of an automatic video monitoring system aiming to assess subjects involved in a clinical scenario. Method and population: The study was conducted in an observation room equipped with everyday objects for use in activities of daily living. The overall aim of the clinical scenario was to enable the participants to undertake a
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