Wednesday, 4 December 2013

Real Life Applicable Fall Detection System Based on Wireless Body Area Network

ABSTRACT:

     Falls can cause moderate to severe injuries owing to the impact to floor or ground at any age. These falls are more commonly experienced by old people. These falls are more commonly experienced by old people. 35% and 50% of people aged above 70 and 80 years, respectively, suffer falls only due to aging. As a result, many complications are faced by older people such as fracture, physical harm, and functional disorder. In this domain, observing the physical condition of elderly people or patients in personal environments such as home, office, and restroom has special significance because they might be unassisted in these locations. The elderly people have limited physical abilities and are more vulnerable to serious physical damages even with small accidents, e.g. fall. The falls are unpredictable and unavoidable. In case of a fall, early detection and prompt notification to emergency services is essential for quick recovery. However, the existing fall detection devices are bulky and uncomfortable to wear. Also, detection system using the devices requires the higher computation overhead to detect falls from activities of daily living (ADL). In this paper, we propose a new fall detection system using one sensor node which can be worn as a necklace to provide both the comfortable wearing and low computation overhead. The proposed necklace-shaped sensor node includes tri-axial accelerometer and gyroscope sensors to classify the behaviour and posture of the detection subject. The simulated experimental results performed 5 fall scenarios 50 times by 5 persons show that our proposed detection approach can successfully distinguish between ADL and fall, with sensitivities greater than 80% and specificities of 100%.

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