Social distancing and contact/exposure tracing are accepted to be critical strategies in the fight against the COVID-19 epidemic. They are both closely connected to the ability to reliably establish the degree of proximity between people in real-world environments. We proposed, implemented, and evaluated a wearable proximity sensing system based on an oscillating magnetic field that overcomes many of the weaknesses of the current state of the art Bluetooth based proximity detection. In this paper, we first described the underlying physical principle, proposed a protocol for the identification and coordination of the transmitter (which is compatible with the current smartphone-based exposure tracing protocols). Subsequently, the system architecture and implementation were described, finally an elaborate characterization and evaluation of the performance (both in systematic lab experiments and in real-world settings) were performed. Our work demonstrated that the proposed system is much more reliable than the widely-used Bluetooth-based approach, particularly when it comes to distinguishing between distances above and below the 2.0 m threshold due to the magnetic field’s physical properties.
We present a wearable, oscillating magnetic field-based proximity sensing system to monitor social distancing as suggested to prevent COVID 19 spread (being between 1.5 and 2.0m) apart. We evaluate the system both in controlled lab experiments and in a real life large hardware store setting. We demonstrate that, due physical properties of the magnetic field, the system is much more robust than current BT based sensing, in particular being nearly 100% correct when it comes to distinguishing between distances above and below the 2.0m threshold. CCS CONCEPTS • Human-centered computing → Ubiquitous computing.
& THE NEW CORONAVIRUS pandemic has promoted the new development of mobile and wearable computing in unprecedented ways. We discuss how on-body devices can help to fight the pandemic and may stay as a toolset to effectively deal with infectious diseases in the future. WHY WEARABLES?Researchers and health policy managers turned to smartphones and on-body devices mostly for their ubiquity, i.e., to offer health and safety-related information to a large share of the population or gather patient responses. Yet, smartphones and wearables enable fast data and information flow, which is particularly relevant for the rapid infectious character of SARS-CoV-2. We observe that continuous sensor and behavior data of smartphones and on-body devices are important as virus testing is associated with effort, cost, and provides only one-time information, and global immunization is still far away.We focus here on already existing and newly created wearable devices and smartphone apps for everyday use, but we exclude clinical and laboratory measurement systems, e.g., for heart and respiratory assessment. For researchers and public health authorities, symptom screening and tracking based on continuous sensor data from wearables and smartphones can
Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.
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