Software-defined radio based contactless localization for diverse human activity recognition

Saeed, U., Shah, S. A. , Khan, M. Z., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Imran, M. A. and Abbasi, Q. H. (2023) Software-defined radio based contactless localization for diverse human activity recognition. IEEE Sensors Journal, 23(11), pp. 12041-12048. (doi: 10.1109/JSEN.2023.3265867)

Abstract

This paper presents a study on contactless localization for activity recognition based on radio-frequency sensing. The focus of this study is on the quantitative analysis of the collected data, which is in the form of channel state information (CSI). The proposed method utilizes a software-defined radio (SDR) system in combination with an ensemble learning technique to localize and identify daily living activities such as leaning, sitting, standing and walking. Specifically, SDR device, Universal Software Radio Peripheral (USRP) models X300/X310 are utilized to collect data on the aforementioned activities. The data is collected from an empty space and a participant performing daily living activities in different territories. The acquired data is labelled based on the region, zone and performed activity. The CSI data is subsequently preprocessed and fed into an ensemble-based machine learning algorithm for classification. Furthermore, the stability analysis of the proposed method is performed to evaluate its reliability and robustness. The performance of the algorithm is evaluated using various metrics, including a confusion matrix, accuracy, cross-validation score and training time. The results demonstrate that the proposed ensemble-based approach achieves a high accuracy of up to 90% in activity recognition, highlighting the effectiveness of the proposed method in contactless localization for activity recognition.

• Item Type: Articles
• Additional Information: This work is supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/W037076/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Khan, Muhammad Zakir and Imran, Professor Muhammad and Abbasi, Professor Qammer and Shah, Mr Syed
• Authors: Saeed, U., Shah, S. A., Khan, M. Z., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Imran, M. A., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 13 April 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Sensors Journal 23(11): 12041-12048
• Publisher Policy: Reproduced with the permission of the Publisher