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William Taylor
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

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Journal article
Published: 04 June 2021 in Sensors
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The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.

ACS Style

William Taylor; Kia Dashtipour; Syed Shah; Amir Hussain; Qammer Abbasi; Muhammad Imran. Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors 2021, 21, 3881 .

AMA Style

William Taylor, Kia Dashtipour, Syed Shah, Amir Hussain, Qammer Abbasi, Muhammad Imran. Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors. 2021; 21 (11):3881.

Chicago/Turabian Style

William Taylor; Kia Dashtipour; Syed Shah; Amir Hussain; Qammer Abbasi; Muhammad Imran. 2021. "Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning." Sensors 21, no. 11: 3881.

Review
Published: 03 October 2020 in Sensors
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COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

ACS Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors 2020, 20, 5665 .

AMA Style

William Taylor, Qammer H. Abbasi, Kia Dashtipour, Shuja Ansari, Syed Aziz Shah, Arslan Khalid, Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors. 2020; 20 (19):5665.

Chicago/Turabian Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. 2020. "A Review of the State of the Art in Non-Contact Sensing for COVID-19." Sensors 20, no. 19: 5665.

Journal article
Published: 06 May 2020 in Sensors
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Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

ACS Style

William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors 2020, 20, 2653 .

AMA Style

William Taylor, Syed Aziz Shah, Kia Dashtipour, Adnan Zahid, Qammer H. Abbasi, Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors. 2020; 20 (9):2653.

Chicago/Turabian Style

William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare." Sensors 20, no. 9: 2653.