Bradford Campbell’s research while affiliated with University of Virginia and other places

As micromobility devices such as e-scooters gain global popularity, emergency departments around the world have observed a rising trend in related injuries. However, the majority of current research on e-scooter safety relies heavily on surveys, news reports, and data from vendors, with a noticeable scarcity of naturalistic studies examining the effects of riders' behaviors and physiological responses. Therefore, this paper aims to study the responses of e-scooter users under different infrastructures and scenarios through naturalistic riding experiments. The findings indicate that different speed profiles, infrastructural elements, and traffic scenarios significantly influence riding dynamics. The experimental results also reveal that e-scooters face amplified safety challenges when navigating through areas with speed variations and without dedicated riding spaces. The study underscores the importance of considering infrastructure design and its influence on e-scooter safety, providing insights that could inform future urban planning and policy-making to enhance the safety of these increasingly popular vehicles.

The advancement of radar has enabled more accurate object detection and semantic segmentation by leveraging the measurements of the distance, direction, and velocity of an object, showing great potential for radar systems to be adopted in various scenarios, such as human detection for smart vacuum cleaners or semantic segmentation for self-driving cars. However, the lack of available large-scale annotated radar datasets and the significant human effort needed to annotate radar points are making it difficult to adapt radar-based sensing applications. Although it is difficult to annotate radar point clouds, it is easier to collect synchronous radar and camera data, and there are already pre-trained models for camera-based semantic segmentation. Inspired by this, we propose RadarContrast, a self-supervised camera-to-radar knowledge distillation approach to reduce the annotation burden on raw radar data by leveraging existing vision-based pre-trained models. RadarContrast works by pre-training the radar-based model with existing camera-based models using a large amount of non-annotated data, and later only requires using a small portion of annotated data to fine-tune the radar model. To be more specific, we build the distillation based on regions that most likely belong to the same object. We apply image segmentation algorithms to separate the image into objects, and instead of doing pixel-wise or point-wise contrasting, we group the pixels and radar point clouds into superpixels and superpoints, respectively. Then, we use a biased pooling strategy to transfer the knowledge from 2D cameras to 3D radar point clouds. We evaluate RadarContrastusing the nuScenes dataset for autonomous driving and demonstrate that our method can achieve similar performance for semantic segmentation while using 5x-10x less annotated data.

Occupancy detection is vital in optimizing smart building applications , such as automatic heating/cooling, lighting systems, and energy management. Conventional occupancy sensing approaches have limitations, such as inadequate performance with stationary occupants, susceptibility to environmental factors, and limited adaptability to various indoor environments. To address these limitations , we propose FTM-Sense, a real-time adaptable sensor-free occupancy detection system by employing the WiFi fine time measurement (FTM) protocol. Our proposed system leverages the variations in FTM measurements caused by human body presence for occupancy detection purposes. FTM-Sense has the advantage of accurately detecting static occupants, and demonstrating robustness and adaptability to various indoor environments. Experimental results show that FTM-Sense achieves a 97.72% overall accuracy, with 97.68% accuracy in detecting static occupants. Furthermore, the system dynamically adapts to room configuration changes within 80 seconds without compromising detection performance. FTM-Sense is tested in both residential and office buildings, collecting over 17 hours of data in seven different rooms with varying sizes and characteristics while also evaluating three different materials for blockage in non-line-of-sight (NLOS) scenarios. This research presents a promising solution for reliable and adaptable sensor-free occupancy detection, contributing to energy management and building cost reduction. CCS CONCEPTS • Computer systems organization → Embedded systems, Sensor networks.

Individually, wall-powered Internet of Things devices are small: in form factor, in complexity, in function, and in power draw. However, at scale, and certainly at the scale optimistic forecasters project, these small devices add up to be a big energy problem. Just adding a single two watt sensor to each US building would add to more annual energy consumption than some small countries. Wall-powered IoT devices are also easier to create than their energy-constrained (i.e. battery-powered) counterparts, and marketed as more convenient (no hub required!), leading to their continued growth. Yet, unlike other energy consuming devices, there are no Energy Star (or equivalent) standards for smart devices. Despite having very infrequent active times, they draw power for functions like AC-DC conversion, wireless communication, and wakeup word detection continuously. Further, the discrete nature of devices and siloed nature of IoT ecosystems leads to significant redundancy in IoT devices. We posit that new techniques are needed to reverse this trend. This includes new techniques for auditing devices, systems that leverage existing devices rather than requiring new ones, and architectures that have less reliance on the cloud (and the energy overhead of network usage and cloud compute). The IoT is pitched to improve energy efficiency and reduce users' carbon footprints, but we need a new research agenda to ensure the devices themselves are not the next problem.

Chronic kidney disease (CKD) is a life-threatening and prevalent disease. CKD patients, especially end-stage kidney disease (ESKD) patients on hemodial-ysis, suffer from kidney failures and are unable to remove excessive fluid, causing fluid overload and multiple morbidities including death. Current solutions for fluid overtake monitoring such as ultra-sonography and biomarkers assessment are cumbersome , discontinuous, and can only be performed in the clinic. In this paper, we propose SRDA, a latent graph learning powered fluid overload detection system based on Sensor Relation Dual Autoencoder to detect excessive fluid consumption of EKSD patients based on passively collected bio-behavioral data from smartwatch sensors. Experiments using real-world mobile sensing data indicate that SRDA outper-forms the state-of-the-art baselines in both F1 score and recall, and demonstrate the potential of ubiquitous sensing for ESKD fluid intake management.