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Human behavior recognition and status monitoring are current research hotspots, especially in the fields of medical monitoring, smart home, and elderly care. With the development of sensor technology, low-power IC chips, and wireless body sensors, miniature sensor networks can be popularized and applied in daily life. Since the energy consumption of sensor networks is a bottleneck problem that limits its development, this paper designs a multimodal collaborative sensing method for the application scenarios of elderly people living alone to reduce energy consumption in the process of daily behavior perception of the elderly. This method subdivides behavior perception into behavior recognition and status monitoring, determines the optimal sensor combination for identifying monitoring different behaviors based on information theory, and then uses a behavior recognition model modeled by a multiclassifier and a status model modeled by a plurality of two classifiers that are used to perceive user behavior. A large number of experimental results show that compared with the traditional sensor network method, our proposed solution can achieve effective sensing while reducing the energy consumption in the process of data transmission and model calculation, thereby prolonging the working life of the sensing network and realizing long-term and reliable daily behavior perception.

Research on Behavior Monitoring of Elderly Living Alone Based on Wearable Devices and Sensing Technology