Background:
The elderly are prone to do some abnormal behaviors, such as tumbling or
stepping out of the guardians’ monitoring area. These abnormal behaviors bring enormous hidden
dangers to the health of the elderly, which need to be monitored effectively in order to be dealt with
in time.
Objective:
Provide an approach based on Wireless Sensor Network (WSN) and Multi-Layer Perceptron
(MLP) to establish the behaviors monitoring system for the elderly.
Methods:
A behavior monitoring system based on wireless sensor network and neural network is
proposed in this paper, according to the behavior characteristics of the elderly. The system collects
real-time behavior data of the elderly by wearing a bracelet with acceleration sensors wore on their
hands. And then a behavior recognition model of the elderly is established through the MLP and the
collected behavior data. The established behavior recognition model is used to classify and identify
the five typical behavior characteristics of the elderly, such as walking, sitting, lying, standing and
tumbling. At the same time, the location information of the elderly is estimated by the centroid
localization technology based on Received Signal Strength Indication (RSSI) ranging.
Results:
The experiment results show that the designed system can timely acquire the behavior
characteristic parameters of the elderly, and it can accurately identify the five typical behaviors with
a 100% recognition accuracy rate. And also, it can timely give the warning of the abnormal
behaviors of the elderly, such as tumbling or walking out of the active area.
Conclusion:
The proposed system in this paper can accurately identify the abnormal behaviors of
elderly and timely inform the guardians. The proposed monitoring method can effectively reduce
the hurt injury elderly, and can improve the work efficiency of guardians. And it has its theoretical
and practical value.
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