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Optimizing sensor deployment for multi-sensor based HAR system with improved glowworm swarm optimization algorithm

Lookup NU author(s): Dr Jie ZhangORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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.

Publication metadata

Author(s): Tian Y, Zhang J

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2020

Volume: 20

Issue: 24

Online publication date: 14/12/2020

Acceptance date: 10/12/2020

Date deposited: 19/01/2021

ISSN (print): 1424-8220

ISSN (electronic): 1424-8220

Publisher: MDPI


DOI: 10.3390/s20247161


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