Automatic Fall Detection and Activity Monitoring for Elderly

International audienceOur modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people to live longer in their own home, ensuring the necessary vigilance and security at the lowest cost, some tele-assistance systems are already available commercially. This article presents a specific neural network used on a portable system for classifying activities in ambulatory monitoring. After more precisions about this specific neural network, we present in the second part some experimental results from our prototype stemming from gerontologic institute Ingema

A specific neural network used on a portable system for classifying activities in ambulatory monitoring

International audienceOur modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people living longer at their own home, ensuring the necessary vigilance and security at the lower cost possible, some tele-assistance systems are already available commercially. This article presents a specific neural network used on a portable system for classifying activities in ambulatory monitoring. After more precisions about this specific neural network in the second part we will present some results from our prototype stemmed from gerontologic institute Ingema

Embedded System used for Classifying Motor Activities of Elderly and Disabled People

International audienceOur modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people to live longer in their own home, ensuring the necessary vigilance and security at the lowest cost, some tele-assistance systems are already available commercially. This article presents a specific neural network used on a portable system for classifying activities in ambulatory monitoring. After more precisions about this specific neural network, we present in the second part some experimental results from our prototype stemming from gerontologic institute Ingema

Sturzerkennung mit am Körper getragenen Sensoren: Ein systematischer Review

Background and aims. Falls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information available in published studies, as well as the characteristics of these investigations (fall documentation and technical characteristics). Methods. The searching of publically accessible electronic literature databases for articles on fall detection with body-worn sensors identified a collection of 96 records (33 journal articles, 60 conference proceedings and 3 project reports) published between 1998 and 2012. These publications were analysed by two independent expert reviewers. Information was extracted into a custom-built data form and processed using SPSS (SPSS Inc., Chicago, IL, USA). Results. The main findings were the lack of agreement between the methodology and documentation protocols (study, fall reporting and technical characteristics) used in the studies, as well as a substantial lack of real-world fall recordings. A methodological pitfall identified in most articles was the lack of an established fall definition. The types of sensors and their technical specifications varied considerably between studies. Conclusion. Limited methodological agreement between sensor-based fall detection studies using body-worn sensors was identified. Published evidence-based support for commercially available fall detection devices is still lacking. A worldwide research group consensus is needed to address fundamental issues such as incident verification, the establishment of guidelines for fall reporting and the development of a common fall definition