Design and Implementation of a Contactless AI-enabled Human Motion Detection System for Next-Generation Healthcare

Song, Y., Taylor, W. , Ge, Y., Dashtipour, K., Imran, M. A. and Abbasi, Q. H. (2021) Design and Implementation of a Contactless AI-enabled Human Motion Detection System for Next-Generation Healthcare. In: IEEE SmartIoT 2021, 13-15 Aug 2021, pp. 112-119. ISBN 9781665445115 (doi: 10.1109/SmartIoT52359.2021.00027)

Abstract

In the field of Artificial Intelligence-driven healthcare systems, human motion detection is becoming increasingly popular as it can be applied to give remote healthcare for vulnerable people. This paper aims to develop a contactless AI-enabled Healthcare system, aimed to detect human motion using Channel State Information (CSI) from wireless signals, which can record patterns of human movements. Although human motion detection systems have been developed using wearable devices, this system still leaves many issues that cannot be solved. For some disabled and elderly people, it is difficult and easily forgotten to wear the devices. Thus, to tackle those issues, a novel method is proposed by using non-wearable methods. We first produced a dataset of CSI that contains patterns of human motion by using software-defined radios. Next, machine learning algorithms like Neural Network (NN), K Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM) were applied to processed CSI data to classify different human activities. Finally, we ensembled the three best-performed classifiers as the healthcare system to reduce the possibility of False Positive cases or True Negative cases. The ensemble classifier can achieve an accuracy of around 98% using 70% data for training and 30% data for testing. This is much higher in contrast with a benchmark dataset measured by accelerators of wearable devices with an accuracy of around 93%, proving the effectiveness of the non-invasive method.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Ge, Yao and Abbasi, Professor Qammer and Dashtipour, Dr Kia and Taylor, William
• Authors: Song, Y., Taylor, W., Ge, Y., Dashtipour, K., Imran, M. A., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781665445115
• Copyright Holders: Copyright © 2021 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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