SPEEDY: A Fall Detector in a Wrist Watch
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
A Smart Sensor to Detect the Falls of the Elderly
IEEE Pervasive Computing
Ontology Based Approach to the Detection of Domestics Problems for Independent Senior People
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
A Wireless Sensor Network for Assisted Living at Home of Elderly People
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Routing techniques in wireless sensor networks: a survey
IEEE Wireless Communications
The problem of medium access control in wireless sensor networks
IEEE Wireless Communications
Behavioral Patterns of Older Adults in Assisted Living
IEEE Transactions on Information Technology in Biomedicine
Throughput efficiency in body sensor networks: A clean-slate approach
Expert Systems with Applications: An International Journal
Sensor-driven agenda for intelligent home care of the elderly
Expert Systems with Applications: An International Journal
INT3-Horus framework for multispectrum activity interpretation in intelligent environments
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Accidental falls of our elderly, and physical injuries resulting, represent a major health and economic problem. Falls are the most common cause of serious injuries and are a major health threat in the stratum of older population. Early detection of a fall is a key factor when trying to provide adequate care to elderly person who has suffered an accident at home. Therefore, the detection of falls in the elderly remains a major challenge in the field of public health. Specific actions aimed at the fall detection can provide urgent care which allows, on the other hand, drastically reduce the cost of medical care, and improve primary care service. In this paper, we present a support system for detecting falls of an elder person by the combination of a wearable wireless sensor node based on an accelerometer and a static wireless non-intrusive sensory infrastructure based on heterogeneous sensor nodes. This previous infrastructure called DIA (Dispositivo Inteligente de Alarma, in Spanish) is an AAL (Ambient Assisted Living) system that allows to infer a potential fall. This inference is reinforced for prompt attention by a specific sensorisation at portable node sensor in order to help distinguish between falls and daily activities of assisted person. The wearable node will not determine a falling situation, it will advice the reasoner layer about specific acceleration patterns that could, eventually, imply a falling. Is at the higher layer where the falling is determined from the whole context produced by mesh of fixed nodes. Experimental results have shown that the proposed system obtains high reliability and sensitivity in the detection of the fall.