Search for: All records

Domain-specific sensor deployments are critical to enabling various IoT applications. Existing solutions for quickly deploying sensing systems require significant amount of work and time, even for experienced engineers. We propose LegoSENSE, a low-cost open-source and modular platform, built on top of the widely popular Raspberry Pi single-board computer, that makes it simple for anyone to rapidly set up and deploy a customized sensing solution for application specific IoT deployments. In addition, the ‘plug and play’ and ‘mix and match’ functionality of LegoSENSE makes the sensor modules reusable, and allows them to be mixed and matched to serve a variety of needs. We show, through a series of user studies, that LegoSENSE enables users without engineering background to deploy a wide range of applications up to 9 × faster than experienced engineers without the use of LegoSENSE. We open-source the hardware and software designs to foster an ever-evolving community, enabling IoT applications for enthusiasts, students, scientists, and researchers across various application domains with or without prior experiences with embedded platforms or coding. 

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. 

The growth of smart devices is making typical homes more intelligent. In this work, in collaboration with therapists, we introduce a home-based AI therapist that takes advantage of the smart home environment to screen the day-to-day functioning and infer mental wellness of an occupant. Unlike existing “chatbot” works that identify the mental status of users through conversation, our AI therapist additionally leverages smart devices and sensors throughout the home to infer mental well-being and assesses a user's daily functioning. We propose a series of 37 dimensions of daily functioning, that our system observes through conversing with the user and detecting daily activity events using sensors and smart sensors throughout the home. Our system utilizes these 37 dimensions in conjunction with novel natural language processing architectures to detect abnormalities in mental status (e.g., angry or depressed), well-being, and daily functioning and generate responses to console users when abnormalities are detected. Through a series of user studies, we demonstrate that our system can converse with a user naturally, accurately detect abnormalities in well-being, and provide appropriate responses consoling users. 

Breath monitoring is important for monitoring illnesses, such as sleep apnea, for people of all ages. One cause of concern for parents is sudden infant death syndrome (SIDS), where an infant suddenly passes away during sleep, usually due to complications in breathing. There are a variety of works and products on the market for monitoring breathing, especially for children and infants. Many of these are wearables that require you to attach an accessory onto the child or person, which can be uncomfortable. Other solutions utilize a camera, which can be privacy-intrusive and function poorly during the night, when lighting is poor. In this work, we introduce BuMA, an audio-based, non-intrusive, and contactless, breathing monitoring system. BuMA utilizes a microphone array, beamforming, and audio filtering to enhance the sounds of breathing by filtering out several common noises in or near home environments, such as construction, speech, and music, that could make detection difficult. We show that BuMA improves breathing detection accuracy by up to 12%, within 30cm from a person, over existing audio filtering algorithms or platforms that do not leverage filtering. 

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. 

Audio is valuable in many mobile, embedded, and cyber-physical systems. We propose AvA, an acoustic adaptive filtering architecture, configurable to a wide range of applications and systems. By incorporating AvA into their own systems, developers can select which sounds to enhance or filter out depending on their application needs. AvA accomplishes this by using a novel adaptive beamforming algorithm called content-informed adaptive beam-forming (CIBF), that directly uses detectors and sound models that developers have created for their own applications to enhance or filter out sounds. CIBF uses a novel three step approach to prop-agate gradients from a wide range of different model types and signal feature representations to learn filter coefficients. We apply AvA to four scenarios and demonstrate that AvA enhances their respective performances by up to 11.1%. We also integrate AvA into two different mobile/embedded platforms with widely different resource constraints and target sounds/noises to show the boosts in performance and robustness these applications can see using AvA. 

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. 

Sound detection and classification are critical in many acoustic-based applications. Existing works generally focus on discovering new features and classifiers to improve detection. However, in many scenarios the presence of other sounds may hinder the performance of these sound classifiers. In this work, we take a sound filtering and enhancement approach to improve sound detection for mobile and embedded applications, regardless of the type of detector used.