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|dc.contributor.author||Ram Mohana Reddy, Guddeti||-|
|dc.identifier.citation||Advances in Intelligent Systems and Computing, 2018, Vol.708, , pp.69-76||en_US|
|dc.description.abstract||Health monitoring devices have flooded the market. But there are very few that cater specifically to the needs of elderly people. Continuously monitoring some critical health parameters like heart rate, body temperature can be lifesaving when the elderly is not physically monitored by a caretaker. An important difference between a general health tracking device and one meant specifically for the elderly is the pressing need in the latter to be able to detect a fall. In case of an elderly person or a critical patient, an unexpected fall, if not attended to within a very short time span, can lead to disastrous consequences including death. We present a solution in the form of a wearable device which, along with monitoring the critical health parameters of the elderly person, can also detect an event of a fall and alert the caretaker. We make use of a 3-axis accelerometer embedded into the wearable to collect acceleration data from the movements of the elderly. We have presented two algorithms for fall detections�one based on a threshold and the other based on a neural network and provided a detailed comparison of the two in terms of accuracy, performance, and robustness. � Springer Nature Singapore Pte Ltd. 2018.||en_US|
|dc.title||Zigbee-based wearable device for elderly health monitoring with fall detection||en_US|
|Appears in Collections:||2. Conference Papers|
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