Smart homes that detect sneeze, cough, and face touching

Miller, Elishiah; Banerjee, Nilanjan; Zhu, Ting

doi:10.1016/j.smhl.2020.100170

Cited by 10 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Chiu

Wang

et al. 2023

Healthcare

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…An interesting method for monitoring hand hygiene activity among healthcare workers, using head-mounted cameras, has been proposed in [ 13 ]. The review of the hand hygiene compliance monitoring methods is published in [ 14 ], whereas in [ 15 ], the authors propose a smart home system based on radars, which is able to detect such activities as coughing, sneezing, and face touching in order to prevent the spreading of disease.…”

Section: Introductionmentioning

confidence: 99%

Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases

Pawlicki

Fotyga

Rewieński

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper proposes an idea of Wireless Body Area Networks (WBANs) based on Bluetooth Low-Energy (BLE) standards to recognize and alarm a gesture of touching the face, and in effect, to prevent self-inoculation of respiratory viral diseases, such as COVID-19 or influenza A, B, or C. The proposed network comprises wireless modules placed in bracelets and a necklace. It relies on the received signal strength indicator (RSSI) measurements between the bracelet and necklace modules. The measured signal is cleared of noise using the exponential moving average (EMA). Next, we use a classification algorithm based on a Least-Squares Support Vector Machine (LSSVM) in order to detect facial touches. When the results of the classification indicate that the hand is moving toward the face, an alarm is sent through the neck module and the vibrator embedded in the wrist module is switched on. Based on the performed tests, it can be concluded that the proposed solution is characterized by high accuracy and reliability. It should be useful, especially for individuals who are regularly exposed to the risk of respiratory infections.

show abstract

Smart homes that detect sneeze, cough, and face touching

Cited by 10 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

Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases

Contact Info

Product

Resources

About