Semantic Communication for Capacity-aware Remote Collaboration

The global spread of coronavirus has sparked a considerable interest in technologies that facilitate seamless communication between users which are physically or spatially distant. Using current remote collaboration systems that utilize 3D sensing with LiDAR and depth cameras, point cloud streaming, and MR/VR devices, distant users can communicate with each other as if they did in person. However, these systems may violate users' privacy since they can share information of their entire personal space with other users. In addition, although various point cloud compression methods have been proposed, remote transmission of 3D scenes still requires significant bandwidth. This paper proposes a 3D spatial data sharing system based on the paradigm of 'semantic communication', i.e., controlling communication in the units of semantic objects. Our system understands the semantics of the scene and leverages point cloud streaming, thereby enabling users to assert fine-grained control over their privacy. Further, the system adaptively controls the size of the data frame based on network capacity and scene context. The experimental results show that the network delay can be reduced by 96%. We have also tested our system in a commercial 4G network, showing that 3-D spatial sharing with point clouds over severe networks is possible. © 2022 IEEE.

Cross-Subject Activity Detection for COVID-19 Infection Avoidance Based on Automatically Annotated IMU Data

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

Smartwatch-Based Face-Touch Prediction Using Deep Representational Learning

World Health Organization (WHO) reported that viruses, including COVID-19, can be transmitted by touching the face with contaminated hands and advised people to avoid touching their face, especially the mouth, nose, and eyes. However, according to recent studies, people touch their faces unconsciously in their daily lives, and it is difficult to avoid such activities. Although many activity recognition methods have been proposed over the years, none of them target the prediction of face-touch (rather than detection) with other daily life activities. To address to problem, we propose TouchAlert: a system that automatically predict the occurrence of face-touch activity and warn the user before its occurrence. Specifically, TouchAlert utilizes commodity wearable devices’ sensors to train a deep learning-based model for predicting the variable length face-touching of different users at an early stage of its occurrence. Our experimental results show high accuracy of F1-score of 0.98 and prediction accuracy of 97.9%. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

EVALUATION OF MENTAL STATE AND CLINICAL SYMPTOMS OF CORONAVIRUS PANDEMIC IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE

Background Coronavirus disease 2019 (COVID-19) has spread worldwide and the World Health Organization declared COVID-19 a pandemic on March 11th, 2020. In Japan, there have been more than 135,000 infected patients and 1950 deaths at late-November 2020. It remains unclear whether COVID-19 affects the clinical course of patients with chronic diseases, including inflammatory bowel disease (IBD). We previously reported that 75% in 1078 IBD patients believe that psychologic stress triggered an exacerbation of their disease, and that a worsened mental state corresponds with disease activity in IBD patients, especially in those who believe that their disease is exacerbated by psychologic stress (Araki M, et al. PLoS One, 2020). The aim of this study was to assess an association of psychologic stress /mental state and clinical symptoms of IBD patients during the COVID-19 pandemic. Methods IBD outpatients were recruited to obtain questionnaires about clinical symptoms, lifestyle habit, medication status, psychologic stress and mental state in Osaka University Hospital from May to June 2020, which was at the end of the first peak of COVID-19 pandemic in Osaka, Japan (pandemic period). As for mental state, the center for epidemiologic studies depression (CES-D) score of 7 points or higher was defined as depression, and the CES-D scores at the pandemic period were compared with those obtained in the previous survey performed from 2015 to 2017. The clinical activity indices [partial Mayo score for ulcerative colitis (UC) and Crohn’s disease activity index for Crohn’s disease (CD) ]at the pandemic period were compared with those from December 2019 to February 2020 (pre-pandemic period). Results A total of 99 adult IBD patients, comprising 38 patients with UC and 61 with CD, were recruited. The mean age (± SD) of the patients was 47.5 (± 14.7) years, and the age at diagnosis was 30.9 (± 13.9) years. No patients were diagnosed as COVID-19. Eighty-four percent of the patients reported as mentally stressed in the questionnaire. The average CES-D score was 5.89 (± 3.41) during the pandemic, which was significantly higher than the previous survey (2.27 ± 3.44, p <.001). The proportion of depressed patients was 40% (38/96), which was also significantly higher than the previous survey [13% (16/122), p <.001]. The mean values of partial Mayo score, Crohn’s disease activity index, and CRP during the pandemic period were 1.29 (± 1.78), 118 (± 85.0), and 0.23 (± 0.52) mg/dl, respectively, all of which were comparable with those in the pre-pandemic period [1.74 (± 2.16), 115 (± 76.8), 0.47 (± 1.18) mg/dl, respectively]. Conclusion During the COVID-19 pandemic, IBD patients were mentally stressed, although their clinical activities were not significantly changed during the short-term period.