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Sleep is a vital physiological state that significantly impacts overall health. Continuous monitoring of sleep posture, heart rate, respiratory rate, and body movement is crucial for diagnosing and managing sleep disorders. Current monitoring solutions often disrupt natural sleep due to discomfort or raise privacy and instrumentation concerns. We introduce PillowSense, a fabric-based sleep monitoring system seamlessly integrated into a pillowcase. PillowSense utilizes a dual-layer fabric design. The top layer comprises conductive fabrics for sensing electrocardiogram (ECG) and surface electromyogram (sEMG), while the bottom layer features pressure-sensitive fabrics to monitor sleep location and movement. The system processes ECG and sEMG signals sequentially to infer multiple sleep variables and incorporates an adversarial neural network to enhance posture classification accuracy. We fabricate prototypes using off-the-shelf hardware and conduct both lab-based and in-the-wild longitudinal user studies to evaluate the system's effectiveness. Across 151 nights and 912.2 hours of real-world sleep data, the system achieves an F1 score of 88% for classifying seven sleep postures, and clinically-acceptable accuracy in vital sign monitoring. PillowSense's comfort, washability, and robustness in multi-user scenarios underscore its potential for unobtrusive, large-scale sleep monitoring. 

Foundation models (FM) have shown immense human-like capabilities for generating digital media. However, foundation models that can freely sense, interact, and actuate the physical domain is far from being realized. This is due to 1) requiring dense deployments of sensors to fully cover and analyze large spaces, while 2) events often being localized to small areas, making it difficult for FMs to pinpoint relevant areas of interest relevant to the current task. We propose FlexiFly, a platform that enables FMs to “zoom in” and analyze relevant areas with higher granularity to better understand the physical environment and carry out tasks. FlexiFly accomplishes by introducing 1) a novel image segmentation technique that aids in identifying relevant locations and 2) a modular and reconfigurable sensing and actuation drone platform that FMs can actuate to “zoom in” with relevant sensors and actuators. We demonstrate through real smart home deployments that FlexiFly enables FMs and LLMs to complete diverse tasks up to 85% more successfully. FlexiFly is critical step towards FMs and LLMs that can naturally interface with the physical world. 

Foundation models excel in tasks such as content generation, zero-shot classifications, and reasoning. However, they struggle with sensing, interacting, and actuating in the physical world due to their dependence on limited sensors and actuators in providing timely contextual information or physical interactions. This reliance restricts the system’s adaptability and coverage. To address these issues and create an embodied AI with foundation models (FMs), we introduce Embodied Reconfigurable Drone Agent (EmbodiedRDA). EmbodiedRDA features a custom drone platform that can autonomously swap payloads to reconfigure itself with a diverse list of sensors and actuators. We designed FM agents to instruct the drone to equip itself with appropriate physical modules, analyze sensor data, make decisions, and control the drone’s actions. This enables the system to perform a variety of tasks in dynamic physical environments, bridging the gap between the digital and physical worlds. 

Recent research has investigated the importance of both walkable urban design and social cohesion. Social cohesion has been shown to have broad social and health benefits, and scholars have hypothesized that walkable urban design can influence cohesion, though evidence remains limited. In this work, we leveraged a data-driven approach that broke down design factors related to walkable design and investigated their impact on cohesion. We used a US-wide open urban form dataset to characterize walkable urban design, and we used an open survey dataset that measured cohesion and demographics with a total sample size of 9670 in six US cities. We leveraged partial least squared structural equation modeling for statistical analysis. We found, controlling for demographics, that land use diversity had a significant positive impact on social cohesion. We also found that physical density, social density, and transit connectedness had significant negative impacts on cohesion, though this association is largely driven by the very dense neighborhoods in cities. These findings shed light on different theories of the built environment, offering insights for designers, engineers, and policymakers interested in the social effects of the built environment. 

We present the design and implementation of RECA, a novel human-centric recommender system for co-optimizing energy consumption, comfort and air quality in commercial buildings. Existing works generally optimize these objectives separately, or by only controlling energy consuming resources within the building without directly engaging occupants. We develop a deep reinforcement learning architecture based on multitask learning, demonstrate how it can be used to jointly learn energy savings, comfort and air quality improvements for different actions, and build a recommender system with humans-in-the-loop. Through real deployments in multiple commercial buildings, we found that RECA has the potential to further reduce energy consumption by up to in energy-focused optimization, improve all objectives by in joint optimization, and improve thermal comfort by up to in comfort and air quality focused optimization, over existing solutions. 

Mapping 3D airflow fields is important for many HVAC, industrial, medical, and home applications. However, current approaches are expensive and time-consuming. We present Anemoi, a sub-$100 drone-based system for autonomously mapping 3D airflow fields in indoor environments. Anemoi leverages the effects of airflow on motor control signals to estimate the magnitude and direction of wind at any given point in space. We introduce an exploration algorithm for selecting optimal waypoints that minimize overall airflow estimation uncertainty. We demonstrate through microbenchmarks and real deployments that Anemoi is able to estimate wind speed and direction with errors up to 0.41 m/s and 25.1° lower than the existing state of the art and map 3D airflow fields with an average RMS error of 0.73 m/s. 

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. 

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. 

Running with a consistent cadence (number of steps per minute) is important for runners to help reduce risk of injury, improve running form, and enhance overall bio-mechanical efficiency. We introduce CaNRun, a non-contact and acoustic-based system that uses sound captured from a mobile device placed on a treadmill to predict and report running cadence. CaNRun obviates the need for runners to utilize wearable devices or carry a mobile device on their body while running on a treadmill. CaNRun leverages a long short-term memory (LSTM) network to extract steps observed from the microphone to robustly estimate cadence. Through an 8-person study, we demonstrate that CaNRun achieves cadence detection accuracy without calibration for individual users, which is comparable to the accuracy of the Apple Watch despite being non-contact.