Photoacoustic effect is a fundamental physics concept which is essentially the generation of sound due to the absorption of intensity modulated light or more generally EM waves by a certain material. We took this fundamental physics concept to build a mobile sensing system that can characterize the quality or nutritional characteristics of liquid food. The long-term vision of this work is to democratize food characterization using such a low cost, easy to use, mobile system which could enable consumers to test food before purchase and to put an indirect pressure on the food industry and government regulators to ensure quality. This work made the following contributions: (1) Proving the fundamental concept of the theory of photoacoustic effect with step-by-step experimentation. (2) Design and Implementation of a low-cost mobile photoacoustic sensing system. (3) Implementation of the signal processing and machine learning algorithm for liquid food characterization. (4) Evaluation of Nutrilyzer for milk protein concentration, milk adulterants, and alcohol concentration characterization.

DoppleSleep is a contactless sleep sensing system that continuously and unobtrusively tracks sleep quality using commercial off-the-shelf radar modules. DoppleSleep provides a single sensor solution to track sleep- related physical and physiological variables including coarse body movements and subtle and fine-grained chest, heart movements due to breathing and heartbeat. By integrating vital signals and body movement sensing, DoppleSleep achieves 89.6% recall with Sleep vs. Wake classification and 80.2% recall with REM vs. Non-REM classification compared to EEG-based sleep sensing. Lastly, it provides several objective sleep quality measurements including sleep onset latency, number of awakenings, and sleep efficiency. The contactless nature of DoppleSleep obviates the need to instrument the user’s body with sensors. Lastly, DoppleSleep is implemented on an ARM microcontroller and a smart- phone application that are benchmarked in terms of power and resource usage.

Researchers have devised different interventions to help users regulate their emotions. However, many of the current interventions require a lot of attention and effort from the users, which may affect their concentration during ongoing tasks and even increase their stress. Therefore, a crucial question that arises is: How to design mobile interventions that can help users to regulate their emotions in real-time, without compromising their behavior or cognition? In this project we argue that it is possible to do that by developing mobile interventions that focus on implicit emotion regulation, in which users are able to regulate their emotions without the need for conscious supervision or explicit intentions. In particular, previous studies show that the way we perceive our bodily signals can directly influence our emotional experience. Inspired by these previous studies we designed and built EmotionCheck, which is a watch-like device that can help users to regulate their anxiety by changing their perception of their own heart rate in a subtle way.

Motivated by the need to support those managing chronic pain, we report on the iterative design, development, and evaluation of Keppi, a novel pressure-based tangible user interface (TUI) for the self-report of pain intensity. In-lab studies with 28 participants found individuals were able to use Keppi to reliably report low, medium, and high pain as well as map squeeze pressure to pain level. Based on insights from these evaluations, we ultimately created a wearable version of Keppi with multiple form factors, including a necklace, bracelet, and keychain. Interviews indicated high receptivity to the wearable design, which satisfied additional user-identified needs (e.g., discreet and convenient) and highlighted key directions for the continued refinement of tangible devices for pain assessment.

Food and branding research groups have leveraged illusions to alter individuals perception of serving size, resulting in the individuals serving themselves smaller food portions. One method is using smaller plates or serving utensils, which causes individuals to serve smaller portions of food. This is due to a perception bias that causes individuals to perceive that there is more food on the plate due to the amount of surface area covered or that they have served more. By leveraging one of these illusions we built a persuasive technology that create the illusion that there is more food on a plate than there actually is, which could influence how much food users serve themselves.

BodyBeat is a novel mobile sensing system for capturing and recognizing a diverse range of non-speech body sounds in real-life scenarios. Non-speech body sounds, such as sounds of food intake, breath, laughter, and cough contain invaluable information about our dietary behavior, respiratory physiology, and affect. The BodyBeat mobile sensing system consists of a custom-built piezoelectric microphone and a distributed computational framework that utilizes an ARM microcontroller and an Android smartphone. The custom-built microphone is designed to capture subtle body vibrations directly from the body surface without being perturbed by external sounds. The microphone is attached to a 3D printed neckpiece with a suspension mechanism. The ARM embedded system and the Android smartphone process the acoustic signal from the microphone and identify non-speech body sounds. We have extensively evaluated the BodyBeat mobile sensing system. Our results show that BodyBeat outperforms other existing solutions in capturing and recognizing different types of important non-speech body sounds.