INTELLIGENT SYSTEM FOR ASSISTING ELDERLY PEOPLE AT HOME

Encouraging elderly people to stay at home as long as possible is associated with a higher risk of social isolation. Nowadays, aging well at home cannot be reduced to the management of physical and cognitive frailties and technologies should also tackle the quality of life of the elderly by fostering their social interactions. However, designing appropriate services and ensuring their adoption remain open questions, to which we try to provide answers at the methodological and instrumental levels. The authors present here a Living Lab approach to design communication services for elderly people at home. They illustrate this approach by describing their participation in a European project aiming at developing and evaluating Social TV services and they conclude with recommendations for the successful socio-technical design of services that foster the social engagement of elderly people.

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Randomised trial of case finding and surveillance of elderly people at home

The Lancet ◽

10.1016/0140-6736(92)92548-t ◽

1992 ◽

Vol 340 (8831) ◽

pp. 1359

Author(s):

Michael Clarke ◽

Carol Jagger ◽

M.S.John Pathy ◽

Antony Bayer

Keyword(s):

Elderly People ◽

Randomised Trial ◽

Case Finding ◽

At Home

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Factors related to the urination methods of elderly people with incontinence who require at-home nursing care

Nippon Ronen Igakkai Zasshi Japanese Journal of Geriatrics ◽

10.3143/geriatrics.53.133 ◽

2016 ◽

Vol 53 (2) ◽

pp. 133-142

Author(s):

Kumiko Tanaka ◽

Keiko Takeda ◽

Keiko Suyama ◽

Akiko Kooka ◽

Satsuki Nakamura

Keyword(s):

Elderly People ◽

Nursing Care ◽

Home Nursing ◽

Home Nursing Care ◽

At Home

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RFID-Based Human Behavior Modeling and Anomaly Detection for Elderly Care

Mobile Information Systems ◽

10.1155/2010/460103 ◽

2010 ◽

Vol 6 (4) ◽

pp. 341-354 ◽

Cited By ~ 15

Author(s):

Hui-Huang Hsu ◽

Chien-Chen Chen

Keyword(s):

Anomaly Detection ◽

Intelligent System ◽

Elderly Care ◽

The Elderly ◽

Behavior Modeling ◽

Abnormal Behavior ◽

Daily Movement ◽

Movement Data ◽

Novel Approach ◽

At Home

This research aimed at building an intelligent system that can detect abnormal behavior for the elderly at home. Active RFID tags can be deployed at home to help collect daily movement data of the elderly who carries an RFID reader. When the reader detects the signals from the tags, RSSI values that represent signal strength are obtained. The RSSI values are reversely related to the distance between the tags and the reader and they are recorded following the movement of the user. The movement patterns, not the exact locations, of the user are the major concern. With the movement data (RSSI values), the clustering technique is then used to build a personalized model of normal behavior. After the model is built, any incoming datum outside the model can be viewed as abnormal and an alarm can be raised by the system. In this paper, we present the system architecture for RFID data collection and preprocessing, clustering for anomaly detection, and experimental results. The results show that this novel approach is promising.

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