Abstract
Human activity recognition (HAR) technology that analyzes and fuses the data acquired from various homogeneous or heterogeneous sensor sources has motivated the development of enormous human-centered applications such as healthcare, fitness, ambient assisted living and rehabilitation. The concurrent use of multiple sensor sources for HAR is a good choice because the plethora of user information provided by the various sensor sources may be useful. However, a multi-sensor system with too many sensors will bring large power consumption and some sensor sources may bring little improvements to the performance. Therefore, the multi-sensor deployment research that can gain a tradeoff among computational complexity and performance is imperative. In this paper, we propose a multi-sensor-based HAR system whose sensor deployment can be optimized by selective ensemble approaches. With respect to optimization of the sensor deployment, an improved binary glowworm swarm optimization (IBGSO) algorithm is proposed and the sensor sources that have a significant effect on the performance of HAR are selected. Furthermore, the ensemble learning system based on optimized sensor deployment is constructed for HAR. Experimental results on two datasets show that the proposed IBGSO-based multi-sensor deployment approach can select a smaller number of sensor sources while achieving better performance than the ensemble of all sensors and other optimization-based selective ensemble approaches.
Highlights
Wearable sensor-based human activity recognition (HAR) systems have gained incredible popularity in many human-centered applications such as assisted living [1], intelligent interactive applications [2], athletic activities training [3,4] and factory workers monitoring [5]
The covariance matrix-based processing is beneficial for improving the recognition performance and has improved the mean accuracy of fall detection
In order to verify the effeteness of the proposed approach for optimizing the sensor layout of the multi-sensor HAR system, we compared it with the following algorithms: genetic algorithm (GA) (Genetic algorithm) [33], BAFSA (Binary artificial fish swarm algorithm) [34] and BGSO (Binary glowworm swarm optimization) [35]
Summary
Wearable sensor-based human activity recognition (HAR) systems have gained incredible popularity in many human-centered applications such as assisted living [1], intelligent interactive applications [2], athletic activities training [3,4] and factory workers monitoring [5]. Through sensors-based HAR system, accurate and reliable information of people’s activity can be provided for ensuring a safe and sound living environment [6]. Video-based HAR systems are not practical in many indoor environments especially when illumination and privacy are considered. Video-based approaches only monitor users in the camera’s specific area. Sensor-based HAR is a challenging but promising research area which has been drawing the attention of researchers from the community of ubiquitous computing, machine learning, medical and healthcare
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