ABSTRACT
The countermeasure for preventing COVID-19 should be further studied in order to make sure countries are prepared for the endemic phase. The biggest challenge of COVID-19 is its high infection rate and infection mortality rate. Robots offer a very good solution to this, hence, we developed a robot that can autonomously navigate a closed indoor room, sanitize it, and monitor social proximity practices. The quality of the hardware design, electronic system and software developments are conducted and experimental works to test the performance of the robot are performed. © 2022 IEEE.
ABSTRACT
The demand for safety-boosting systems is always increasing, especially to limit the rapid spread of COVID-19. Real-time social distance preserving is an essential application towards containing the pandemic outbreak. Few systems have been proposed which require infrastructure setup and high-end phones. Therefore, they have limited ubiquitous adoption. Cellular technology enjoys widespread availability and their support by commodity cellphones which suggest leveraging it for social distance tracking. However, users sharing the same environment may be connected to different teleco providers of different network configurations. Traditional cellular-based localization systems usually build a separate model for each provider, leading to a drop in social distance performance. In this paper, we propose CellTrace, a deep learning-based social distance preserving system. Specifically, CellTrace finds a cross-provider representation using a deep learning version of Canonical Correlation Analysis. Different providers’data are highly correlated in this representation and used to train a localization model for estimating the social distances. Additionally, CellTrace incorporates different modules that improve the deep model’s generalization against overtraining and noise. We have implemented and evaluated CellTrace in two different environments with a side-by-side comparison with the state-of-the-art cellular localization and contact tracing techniques. The results show that CellTrace can accurately localize users and estimate the contact occurrence, regardless of the connected providers, with a sub-meter median error and 97% accuracy, respectively. In addition, we show that CellTrace has robust performance in various challenging scenarios. Author
ABSTRACT
The World Health Organization reported that face touching is a primary source of infection transmission of viral diseases, including COVID-19, seasonal Influenza, Swine flu, Ebola virus, etc. Thus, people have been advised to avoid such activity to break the viral transmission chain. However, empirical studies showed that it is either impossible or difficult to avoid as it is unconsciously a human habit. This gives rise to the need to develop means enabling the automatic prediction of the occurrence of such activity. In this paper, we propose SafeSense, a cross-subject face-touch prediction system that combines the sensing capability of smartwatches and smartphones. The system includes innovative modules for automatically labeling the smartwatches’sensor measurements using smartphones’proximity sensors during normal phone use. Additionally, SafeSense uses a multi-task learning approach based on autoencoders for learning a subject-invariant representation without any assumptions about the target subjects. SafeSense also improves the deep model’s generalization ability and incorporates different modules to boost the per-subject system’s accuracy and robustness at run-time. We evaluated the proposed system on ten subjects using three different smartwatches and their connected phones. Results show that SafeSense can obtain as high as 97.9% prediction accuracy with a F1-score of 0.98. This outperforms the state-of-the-art techniques in all the considered scenarios without extra data collection overhead. These results highlight the feasibility of the proposed system for boosting public safety. IEEE