Smart homes that detect sneeze, cough, and face touching

Miller, Elishiah; Banerjee, Nilanjan; Zhu, Ting

doi:10.1016/j.smhl.2020.100170

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…The IoMT framework addresses the shortage of resources faced by medical teams during the COVID-19 epidemic; not only can the geo-location of COVID-19 patients be tracked by Bluetooth to assess personnel risk [ 26 , 27 , 31 , 40 ], but the technology allows for tracking of patient recovery [ 22 , 33 ]. Chen’s research focuses on visualizing and discussing this important information [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation

Chiu

Wang

et al. 2023

Healthcare

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Since the outbreak of the novel coronavirus disease 2019 (COVID-19), the epidemic has gradually slowed down in various countries and people’s lives have gradually returned to normal. To monitor the spread of the epidemic, studies discussing the design of related healthcare information systems have been increasing recently. However, these studies might not consider the aspect of user-centric design when developing healthcare information systems. This study examined these innovative technology applications and rapidly built prototype systems for smart healthcare through a systematic literature review and a study of patient innovation. The design guidelines for the Smart Healthcare System (SHS) were then compiled through an expert review process. This will provide a reference for future research and similar healthcare information system development.

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Section: Resultsmentioning

confidence: 99%

Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation

Chiu

Wang

et al. 2023

Healthcare

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“…Tracking risk behaviours displayed by people with regard to virus spread can help to prevent increasing numbers of COVID-19 cases. Recent research used radiofrequency technology to capture human motion and detect coughing in a room [57]. This approach requires special devices and infrastructure setup.…”

Section: Methodsmentioning

confidence: 99%

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Honarparvar

Saeedi

Liang

et al. 2021

IJGI

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Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute simple events, missing objects result in missing simple events, thus the number of detected complex events. As the main objective of this paper, an integrated cloud and edge computing architecture was designed and developed to reduce missing simple events. To achieve this goal, we deployed multiple smart cameras (i.e., cameras which connect to the Internet and are integrated with computerised systems such as the DL unit) in order to detect complex events from multiple views. Having more simple events from multiple cameras can reduce missing simple events and increase the number of detected complex events. To evaluate the accuracy of complex event detection, the F-score of risk behaviour regarding COVID-19 spread events in video streams was used. The experimental results demonstrate that this architecture delivered 1.73 times higher accuracy in event detection than that delivered by an edge-based architecture that uses one camera. The average event detection latency for the integrated cloud and edge architecture was 1.85 times higher than that of only one camera. However, this finding was insignificant with regard to the current case study. Moreover, the accuracy of the architecture for complex event matching with more spatial and temporal relationships showed significant improvement in comparison to the edge computing scenario. Finally, complex event detection accuracy considerably depended on object detection accuracy. Regression-based models, such as you only look once (YOLO), were able to provide better accuracy than region-based models.

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“…The COVID-19 pandemic was the trigger for another research project [23] that made use of radio frequency technology to capture special characteristics of human activities. Consequently, the gathered data were analyzed through a set of fuzzy logic IF-THEN rules that can recognize different subjects entering or leaving a room, coughing, sneezing, and face touching through walls.…”

Section: Face Touching Detectionmentioning

confidence: 99%

Preventing Keratoconus through Eye Rubbing Activity Detection: A Machine Learning Approach

Nokas

Kotsilieris

2023

Electronics

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Keratoconus is a non-inflammatory disease of the eyes diagnosed in more than 1/2000 people, making it significantly common. Among others, eye rubbing has been identified as a risk factor for the development of keratoconus. The severity of the disease strongly depends on the frequency and force of eye rubbing. Vast research efforts have focused on diagnosing keratoconus through the application of artificial intelligence techniques over optical coherence tomography images and corneal measurements. However, to the best of the authors’ knowledge, no studies have been conducted which provide an eye rubbing detection and alert mechanism for keratoconus prevention. This study intends to help close this research gap. An inertial measurement unit that is dedicated to collecting hand motion data and machine learning techniques are jointly employed for the early detection of potential problems and complications. Four conventional classification methods (support vector machines, decision trees, random forest, and XGBoost) were evaluated and compared. All methods attain high-quality accuracy results, with SVMs, RF, and XGBoost slightly outperforming DTs. As the results reveal, the performance of all methods is remarkable, allowing the integration of such a solution in wearable devices such as smartwatches to be considered for the early detection of eye rubbing and keratoconus prevention.

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Smart homes that detect sneeze, cough, and face touching

Cited by 12 publications

References 17 publications

Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation

Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Preventing Keratoconus through Eye Rubbing Activity Detection: A Machine Learning Approach

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