Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Sensors

Marcin Bernas¹  Bartłomiej Płaczek²  Marcin Lewandowski² 

[1] Department of Computer Science and Automatics, University of Bielsko-Biała, Willowa 2, 43-309 Bielsko-Biała, Poland;Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland;

关键词: wireless sensor network;    wearable sensors;    activity recognition;    lifetime;    energy consumption;    transmission suppression;   

DOI  :  10.3390/s21010085

来源: DOAJ

【 摘 要 】

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.

【 授权许可】

Unknown