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The types of human activities occupants are engaged in within indoor spaces significantly contribute to the spread of airborne diseases through emitting aerosol particles. Today, ubiquitous computing technologies can inform users of common atmosphere pollutants for indoor air quality. However, they remain uninformed of the rate of aerosol generated directly from human respiratory activities, a fundamental parameter impacting the risk of airborne transmission. In this paper, we present AeroSense, a novel privacy-preserving approach using audio sensing to accurately predict the rate of aerosol generated from detecting the kinds of human respiratory activities and determining the loudness of these activities. Our system adopts a privacy-first as a key design choice; thus, it only extracts audio features that cannot be reconstructed into human audible signals using two omnidirectional microphone arrays. We employ a combination of binary classifiers using the Random Forest algorithm to detect simultaneous occurrences of activities with an average recall of 85%. It determines the level of all detected activities by estimating the distance between the microphone and the activity source. This level estimation technique yields an average of 7.74% error. Additionally, we developed a lightweight mask detection classifier to detect mask-wearing, which yields a recall score of 75%. These intermediary outputs are critical predictors needed for AeroSense to estimate the amounts of aerosol generated from an active human source. Our model to predict aerosol is a Random Forest regression model, which yields 2.34 MSE and 0.73 r2 value. We demonstrate the accuracy of AeroSense by validating our results in a cleanroom setup and using advanced microbiological technology. We present results on the efficacy of AeroSense in natural settings through controlled and in-the-wild experiments. The ability to estimate aerosol emissions from detected human activities is part of a more extensive indoor air system integration, which can capture the rate of aerosol dissipation and inform users of airborne transmission risks in real time. 

Environmentally-powered computer systems operate on renewable energy harvested from their environment, such as solar or wind, and stored in batteries. While harvesting environmental energy has long been necessary for small-scale embedded systems without access to external power sources, it is also increasingly important in designing sustainable larger-scale systems for edge applications. For sustained operations, such systems must consider not only the electrical energy but also the thermal energy available in the environment in their design and operation. Unfortunately, prior work generally ignores the impact of thermal effects, and instead implicitly assumes ideal temperatures. To address the problem, we develop a thermodynamic model that captures the interplay of elec- trical and thermal energy in environmentally-powered computer systems. The model captures the effect of environmental condi- tions, the system’s physical properties, and workload scheduling on performance. In evaluating our model, we distill the thermal effects that impact these systems using a small-scale prototype and a programmable incubator. We then leverage our model to show how considering these thermal effects in designing and operating environmentally-powered computer systems of varying scales can improve their energy-efficiency, performance, and availability. 

Recent advancements in semiconductor technologies have stimulated the growth of ultra-low power wearable devices. However, these devices often pose critical constraints in usability and functionality because of the on-device battery as the primary power source [1]. For example, periodic charging of wearable devices hampers the continuous monitoring of users' fitness or health conditions [2], and batteries and charging equipment have been identified as one of the most rapidly growing electronic waste streams [3]. To counteract the above-mentioned complications associated with the management of on-device batteries, wireless power transmission technologies capable of charging wearable devices in a completely unobtrusive and seamless manner have become an emerging topic of research over the past decade [4]. Researchers have instrumented daily objects or the surrounding environment with equipment that can wirelessly transfer energy from a variety of sources, such as Radio Frequency (RF) signals, laser, and electromagnetic fields [5]. However, these solutions require large and costly infrastructure and/or need to transmit a significant amount of power to support reasonable power harvesting at the wearable devices, which conflict with the vision of ubiquitously available and scalable charging support.