Abstract
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.
Highlights
Human behavior recognition [1, 2] and status monitoring are the current research hotspots in the field of artificial intelligence and pattern recognition
Human behavior recognition is classified by the method of obtaining human behavior data and can be divided into behavior recognition methods based on computer vision and behavior recognition methods based on sensor devices
In the field of mobile health, wearable devices and other miniature sensor devices have become an indispensable part of daily life [7]. ese wearable sensor products can detect human sleep and movement and can provide a data reference for our daily life or whether the amount of exercise is reasonable
Summary
Human behavior recognition [1, 2] and status monitoring are the current research hotspots in the field of artificial intelligence and pattern recognition. The continuous detection of human behavior using WBSN through the continuous working mechanism of all sensors in the network consumes a lot of energy and computing resources, but the limited computing resources, wireless transmission, and power of the sensor network greatly limit WBSN’s monitoring and evaluation of PD motor symptoms in daily life. At present, whether it is using wearable sensor devices or BSNs to continuously monitor human behaviors, all sensors or devices in the network will continue to work. While the proposed method realizes the perception of user behavior, it minimizes the number of working sensors and the amount of data transmitted, thereby reducing the energy and resource consumption of the entire network
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