Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients | IGI Global Scientific Publishing
Welcome to the InfoSci Platform
Reference Hub1
Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients

Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients

Daniel Adu-Gyamfi (C K Tedam University of Technology and Applied Sciences, Navrongo, Ghana) and Fengli Zhang (University of Electronic Science and Technology of China, China)
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 18
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.2022010109
Cite Article Cite Article

MLA

Adu-Gyamfi, Daniel, and Fengli Zhang. "Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients." JITR vol.15, no.1 2022: pp.1-18. https://doi.org/10.4018/JITR.2022010109

APA

Adu-Gyamfi, D. & Zhang, F. (2022). Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients. Journal of Information Technology Research (JITR), 15(1), 1-18. https://doi.org/10.4018/JITR.2022010109

Chicago

Adu-Gyamfi, Daniel, and Fengli Zhang. "Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients," Journal of Information Technology Research (JITR) 15, no.1: 1-18. https://doi.org/10.4018/JITR.2022010109

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Asymptomatic patients (AP) travel through neighborhoods in communities. The mobility dynamics of the AP makes it hard to tag them with specific interests. The lack of efficient monitoring systems can enable the AP to infect several vulnerable people in the communities. This article studied the monitoring of AP through their mobility and trajectory towards reducing the stress of socio-economic complications in the case of pandemics. Mobility and Trajectory based Technique for Monitoring Asymptomatic Patients (MTT-MAP) was established. The time-ordered spatial and temporal trajectory records of the AP were captured through their activities. A grid-based index data structure was designed based on network topology, graph theory and trajectory analysis to cater for the continuous monitoring of the AP over time. Also, concurrent object localisation and recognition, branch and bound, and multi-object instance strategies were adopted. The MTT-MAP has shown efficient when experimented with GeoLife dataset and can be integrated with state-of-the-art patients monitoring systems.