Search for: All records

The stethoscope is one of the most important diagnostic tools used by healthcare professionals, through a process called auscultation, to screen patients for abnormalities of the heart and lungs. While there are digital stethoscopes on the market which ease this process, it still takes years of training to properly use these devices to listen for abnormal sounds within the body. We present ARSteth, an intelligent stethoscope platform that improves the accessibility of stethoscopes for the general population, allowing anyone to perform auscultation in the comfort of their own homes. Our platform utilizes a combination of augmented reality (AR), acoustic intelligence, and human-machine interaction to dynamically guide users on where to place the stethoscope on different parts of the body (auscultation points), through visual and audio cues. Through user studies, we show that ARSteth, on average, can guide users within 13.2 mm from optimal auscultation points marked by licensed physicians in 13.09 seconds for each auscultation point. By guiding users towards more effective auscultation points, make preventative health screening more accessible and effective for everyone we are able to achieve higher confidence on classifying heart murmurs. 

Cardiopulmonary ailments are a major cause of mortality. Stethoscopes are one of the most important tools that healthcare professionals use to screen patients for a variety of ailments, especially those related to the heart and lungs. Despite the growth of digital stethoscopes on the market, it takes years of training to properly use stethoscopes to listen for abnormal sounds within the body. In this demonstration, we present an intelligent stethoscope platform that makes stethoscopes more accessible to the general population. Our platform utilizes augmented reality (AR) to provide real-time guidance on where to properly place the stethoscope on the body, enabling the general population to screen themselves for ailments. 

With the global spread of the COVID-19 pandemic, ventilation indoors is becoming increasingly important in preventing the spread of airborne viruses. However, while sensors exist to measure wind speed and airflow gradients, they must be manually held by a human or an autonomous vehicle, robot, or drone that moves around the space to build an airflow map of the environment. In this demonstration, we present DAE, a novel drone-based system that can automatically navigate and estimate air flow in a space without the need of additional sensors attached onto the drone. DAE directly utilizes the flight controller data that all drones use to self-stabilize in the air to estimate airflow. DAE estimates airflow gradients in a room based on how the flight controller adjusts the motors on the drone to compensate external perturbations and air currents, without the need for attaching additional wind or airflow sensors. 

Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeterias. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts a 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy. 

We present SoFIT, an easily-deployed and privacy-preserving camera network system for occupant tracking. Unlike traditional camera network-based systems, SoFIT does not require a person to calibrate the network or provide real-world references. This enables anyone, including non-professionals, to install SoFIT. Once installed, SoFIT automatically localizes cameras within the network and generates the floor map leveraging movements of people using the space in daily life, before using the floor map and camera locations to track occupants throughout the environment. We demonstrate through a series of deployments that SoFIT can localize cameras with less than 4.8cm error, generate floor maps with 85% similarity to actual floor maps, and track occupants with less than 7.8cm error. 

With the recent societal impact of COVID-19, companies and government agencies alike have turned to thermal camera based skin temperature sensing technology to help screen for fever. However, the cost and deployment restrictions limit the wide use of these thermal sensing technologies. In this work, we present SIFTER, a low-cost system based on a RGB-thermal camera for continuous fever screening of multiple people. This system detects and tracks heads in the RGB and thermal domains and constructs thermal heat map models for each tracked person, and classifies people as having or not having fever. SIFTER can obtain key temperature features of heads in-situ at a distance and produce fever screening predictions in real-time, significantly improving screening through-put while minimizing disruption to normal activities. In our clinic deployment, SIFTER measurement error is within 0.4°F at 2 meters and around 0.6°F at 3.5 meters. In comparison, most infrared thermal scanners on the market costing several thousand dollars have around 1°F measurement error measured within 0.5 meters. SIFTER can achieve 100% true positive rate with 22.5% false positive rate without requiring any human interaction, greatly outperforming our baseline [1], which sees a false positive rate of 78.5%. 

Homes are becoming more intelligent due to the growth of smart sensors and devices found in typical homes. However, most of these sensors and devices function independently from one another, limiting the amount of utility and services a truly "smart" home can provide. In this demonstration, we introduce two key ideas towards more intelligent homes. First, we explore the usage of mobile drones in the home environment. Second, we propose DIA, a system that seamlessly connects to the home environment and automatically discovers and jointly utilizes smart sensors and actuators around the home to provide services that are otherwise not possible. We demonstrate three services that DIA enables. 

In this poster abstract, we present a thermal comfort estimation system using low-cost thermal camera based sensor nodes. This system extracts perspective invariant, non-intrusive thermal measurements, is easily deployable and low-cost, and can incorporate individual thermal feedback for more personalized thermal comfort estimates. In comparison with baseline methods, our system is able to improve thermal comfort estimates on the ASHRAE 7-point thermal sensation scale by up to 64% over baseline methods. 

In commercial buildings, occupant thermal comfort is a key factor that must be optimized to provide a comfortable and productive work environment. However, current methods largely estimate thermal comfort based on preset models which do not incorporate real-time measurements or individual thermal preferences. In this work, we present a scalable system for estimating personalized thermal comfort using low-cost thermal camera based sensor nodes. This system extracts non-intrusive thermal measurements, is robust to different perspectives and environments, is easily deployable and low-cost, and can incorporate individual thermal feedback for more personalized thermal comfort estimates. In comparison with baseline methods, our system is able to improve thermal comfort estimates on the ASHRAE 7-point thermal sensation scale by 64% over baseline methods.