Interpersonal Distance Tracking with mmWave Radar and IMUs

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. © 2023 Owner/Author.

mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave

There is a growing trend for people to perform work-outs at home due to the global pandemic of COVID-19 and the stay-at-home policy of many countries. Since a self-designed fitness plan often lacks professional guidance to achieve ideal outcomes, it is important to have an in-home fitness monitoring system that can track the exercise process of users. Traditional camera-based fitness monitoring may raise serious privacy concerns, while sensor-based methods require users to wear dedicated devices. Recently, researchers propose to utilize RF signals to enable non-intrusive fitness monitoring, but these approaches all require huge training efforts from users to achieve a satisfactory performance, especially when the system is used by multiple users (e.g., family members). In this work, we design and implement a fitness monitoring system using a single COTS mm Wave device. The proposed system integrates workout recognition, user identification, multi-user monitoring, and training effort reduction modules and makes them work together in a single system. In particular, we develop a domain adaptation framework to reduce the amount of training data collected from different domains via mitigating impacts caused by domain characteristics embedded in mm Wave signals. We also develop a GAN-assisted method to achieve better user identification and workout recognition when only limited training data from the same domain is available. We propose a unique spatialtemporal heatmap feature to achieve personalized workout recognition and develop a clustering-based method for concurrent workout monitoring. Extensive experiments with 14 typical workouts involving 11 participants demonstrate that our system can achieve 97% average workout recognition accuracy and 91% user identification accuracy. © 2022 IEEE.

Network-side 5G mmWave Channel Signatures for Pandemic Contact Tracing

Contact tracing is a key mechanism to help contain the COVID-19 pandemic and other pandemics in the future. In this work, we propose using 5G channel signatures - specifically, mm-Wave channel signatures - to perform contact tracing and infer early sources of the infection. Our network-side approach is motivated by the density of mm-Wave base stations, coupled with the large amount of data about mobile device signals already being collected by cellular operators. We model the contact tracing problem as a graph mining problem, and develop machine learning models to estimate contacts between UEs based on 5G channel signatures such as received power. These contacts are also used to infer the original sources of the infection. Simulations of our proposed method using the ns-3 5G mmWave module suggest that contact can be inferred with a recall of 85% and specificity of 94%. Our infection sources estimation method can accurately rank the most likely infection sources, with the true infection sources lying in the top 25% of the ranked list on average. These methods represent a first step towards network-based contact tracing, and can complement other contact tracing methods to help reduce the spread of disease. © 2022 IEEE.

Remote radar-camera vital sign monitoring system using a graph-based extraction algorithm

Mm-wave radar and video-based technologies have shown potentials for contactless detection of vital signs. However, state-of-the-art signal processing based vital sign extraction methods are prone to disruptions, such as motion corruption. In this work, we propose a novel graph-based segmentation algorithm for improved accuracy and robustness. We also developed a combined radar-camera system to integrate with the proposed algorithm. The test results on both mmWave radar and camera systems were found to be of high correlations (0.95-0.97) with the golden standard. Our system provides a viable and robust approach for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19. © 2021 IEEE