Projects
OptoBeat
OptoBeat enable’s the acquisition of blood oxygen saturation monitoring by augmenting the smartphone’s camera system, focusing the light source, and leveraging extant computing capacity. With this system, we can use a skin tone measurement to adjust the ratios of the two source frequencies transmitted through the skin to calibrate the measurement of blood oxygen for a more accurate reading.
Specifically, we present the following contributions:
• An experiment and quantitative analysis of how skin tone affects traditional pulse oximeters and a demonstration of how this can be remedied.
• The design of the OptoBeat optical sensing system and an experiment to validate the theory behind our design.
• The design, execution, and results from an ex-vivo experiment that validates the accuracy of pulse oximetry from healthy and critical SpO2 levels against the gold standard and the results of a human subject experiment to validate the efficacy of the device.
PuffPacket
The proliferation of e-cigarettes and portable vaporizers presents new opportunities for accurately and unobtrusively tracking e-cigarette use. PuffPacket is a hardware and software research platform that leverages the technology built into vaporizers, e-cigarettes and other electronic drug delivery devices to ubiquitously track their usage. The system piggybacks on the signals these devices use to directly measure and track the nicotine consumed by users. PuffPacket augments e-cigarettes with Bluetooth to calculate the frequency, intensity, and duration of each inhalation. This information is augmented with smartphone-based location and activity information to help identify potential contextual triggers. PuffPacket is generalizable to a wide variety of electronic nicotine, THC, and other drug delivery devices currently on the market. The hardware and software for PuffPacket is open-source (https://github.com/PuffPacket/PuffPacket) so it can be expanded upon and leveraged for mobile health tracking research.
Nutrilyzer
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
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.
EmotionCheck
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.
Keppi
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.
MindlessPlate
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
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.