Indoor Contact Awareness on Spatiotemporal Analytics with Smartphone-Based Pedestrian Dead Reckoning

Due to the prevalence of COVID-19, providing safe environments and avoiding exposure to the virus play a pivotable role in our daily lives. As a well-established measurement, contact tracing is widely applied to suppress its spread. Most of the digital contact tracing systems merely detect direct face-to-face contact based on estimated proximity and do not quantify the exposed virus concentration. Indirect environmental exposure due to virus survival time in the air and constant airborne transmission is rarely considered quantitatively. In this work, to provide accurate awareness of the virus quanta concentration in different origins at various times, we propose iSTCA, a self-containing contact awareness approach with spatiotemporal information considered explicitly. Smartphone-based PDR is employed to precisely achieve the location and trajectories for distance estimation and time induction without extra infrastructure involved, in which the accumulative error is calibrated by recognized landmarks in space. A custom deep learning model composed of CNN and LSTM for both the local correlation and long-term dependency extraction is utilized to identify landmarks. By the integration of spatial distance and time difference, the virus quanta concentration of the entire indoor environment is quantitatively calculated at any time with all contributed virus particles. We conduct an experiment based on practical scenario to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.8 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment. © 2022 ACM.

WiHead: WiFi-Based Head-Pose Estimation

Due to the impact of Covid-19, people have started to conduct online courses or meetings. However, this makes it difficult to communicate with each other effectively because of the lack of non-verbal communication. Although webcams are available for online courses, etc., people often do not want to turn them on for privacy reasons. Thus, there is a need to develop privacy preserving way to enable non-verbal communication in online learning and work environments. WiFi as a sensor can be used to detect non-verbal gestures such as head poses, and has been increasingly valued due to its advantages of avoiding the effects of light, non-line of sight monitoring, privacy protection, etc. In this paper, we proposed an approach, which uses WiFi CSI data to estimate head pose. Our approach not only use the amplitude and phase data of raw CSI data, but also use the information in frequency domain. Our experiment with proposed approach confirmed the feasibility of head pose estimation based on WiFi CSI data. This has important implications for device-free sensing detection. Especially in today’s world where web conferences and online courses are widely used, WiFi-based head recognition can give feedback to the other party while protecting privacy, which helps to improve the quality and comfort of communication. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

UNDERSTANDING STUDENT SLIDE READING PATTERNS DURING THE PANDEMIC

The COVID-19 pandemic has resulted in school closures all across the world, and lots of students have shifted from conventional classrooms to online learning. With the help of ICT technologies nowadays, learning online can be more effective in a number of ways. However, most of the online learning environments without instructors' attention may result in different learning patterns compared to the traditional face-to-face classroom. In this paper, we aimed at detecting the slide reading behaviors of the students by analyzing operational event logs from a digital textbook reader for a lecture offered in our university. We compared reading patterns between traditional face-to-face lectures and hybrid online lectures, our results show that online lectures lead to more off-task behaviors. Our analysis provides a rich understanding of e-book reading and informs design implications for online learning during the pandemic. The findings can also be used to improve the instruction designs and learning strategies. © 2021 Virtual Simulation Innovation Workshop, SIW 2021. All rights reserved.