Food perception is a multisensory experience shaped by environmental cues such as ambient lighting. Previous studies have demonstrated that lighting can impact how satisfied we feel while dining. However, many of these studies were conducted in controlled laboratory settings with standardized meals, overlooking how lighting interacts with personal dietary choices and diverse dining contexts. This paper introduces LumiBite, a portable lighting system integrated into a lunchbox, designed for dining environments to enable personalized lighting adjustments during meals. Through a seven-day in-the-wild study with six participants, where they freely chose when, what, and where to eat, we explored the feasibility of deploying LumiBite and investigated how user agency in customizing lighting settings impacts dining satisfaction, sensory perception and dietary behaviors. Our findings demonstrate that LumiBite not only enhances food aesthetics but also shifts users from passive consumers to active meal curators. The study highlights key challenges, including cultural dining practices and ambient light interference, and offers actionable design principles for creating context-aware, culturally sensitive dining technologies.

Laser speckle imaging is a powerful but underutilized optical technique, capable of capturing a range of physiological biomarkers, including heart rate, respiration, skin perfusion, hydration, and subtle structural changes in skin such as piloerection. Despite its proven efficacy in controlled settings, Laser speckle imaging is rarely found in wearable or point-ofcare devices, which typically monitor only basic vital signs. To address this gap, we introduce SkinSpex: a compact, affordable device based on a Raspberry Pi Zero 2, integrating a multiwavelength (560,750, and 930 nm) laser system for flexible, non-contact speckle imaging. SkinSpex enables mapping of both hemodynamic and topographical skin features from a distance of just 10 cm, opening the door to multi-biomarker monitoring in everyday environments. Such comprehensive monitoring is especially relevant for detecting acute physiological states, including the sudden onset of opioid withdrawal, where changes in heart rate, breathing, and skin structure may occur simultaneously but are often missed by conventional wearables. By directly capturing both surface and subsurface skin dynamics, SkinSpex can provide a more complete view of physiological state, enabling earlier and more reliable detection of significant events. Our results show the unique capabilities of this compact prototype and suggest that SkinSpex could enable a new generation of wearable platforms for continuous, comprehensive health assessment across a variety of clinical and real-world settings.

Bench-top performance evaluation of novel continuous fetal heart monitoring sensors that utilizes surface mechanical vibrations is hampered by the absence of a physical phantom that is able to simulate fetal heartbeat realistically in the frequency and amplitude domains. In this work, we present the design of$\phi$-Fetus, a phantom in-utero fetus built with a medical gel and interchangeable actuators. The medical gel was molded for a shape and size that match the maternal abdominal dimensions. Under the square wave excitation of the speaker actuator at$\mathbf{2}-2.8 \text{Hz}$, the phantom delivers accurate and consistent surface vibration responses, as recorded by an accelerometer, validating its precision in frequency control. By pumping the balloon with specific initial volume and pumping volume that correlates to respective cardiac volume and stroke volume of the fetus from 20 to 40 weeks of gestation, the phantom delivers peak accelerations that matches actual data points through interpolation, thus validating its realistic simulation of fetal heartbeat. These results demonstrate that the phantom is able to provide a tunable, repeatable, and physiologically relevant platform for testing and benchmarking of novel fetal heart monitoring sensors, and enables quantitative validation of wearable sensors applicable to future prenatal sensing research.

Accurate respiratory tracking is essential for targeting dynamic organs during oncology procedures, yet current solutions struggle in constrictive clinical environments like MRI and radiotherapy suites. We propose a novel, low-profile respiratory tracking device using only a nasal cannula and remote flow sensing to overcome these limitations. The system was evaluated in two experiments: a flow distance study confirming signal fidelity across 20 meters of tubing (Pearson correlation$=0.9938 \pm 0.0021$), and a flow mapping study showing that the cannula captured 1% of total respiratory flow and, when scaled, could show the full respiratory cycle. The device was also tested in the MRI suite. These results demonstrate the feasibility of a simple, non-invasive solution for accurate respiratory monitoring in constrained clinical settings.

Wearable health devices have transformed the land-scape of vital sign monitoring by enabling continuous, unob-trusive data collection. These compact and lightweight devices bypass the need for large, specialized instruments, facilitating frequent and comprehensive health monitoring essential for di-agnosing various medical conditions. Researchers are leveraging innovative techniques to sense bodily functions through external signals, such as using acoustic signals for joint health and repurposing low-cost sensors like IMUs, temperature sensors, and microphones as biosensors. These advancements aim to create more affordable and widespread health monitoring systems than traditional, costly biosensors. In this work, we present HealthHub, a versatile wearable health prototyping toolkit designed to expedite the development and testing of wearable health devices. HealthHub's modularity and flexibility are demonstrated by its array of onboard sensors and its support for custom snap-on boards that enhance sensing capabilities via the onboard ADC. Our evaluation of HealthHub included testing its power consumption and performance in measuring respiration, where it functioned as a pendant. The system operated for three days on a single coin cell battery, recording data at high sample rates and fidelity. HealthHub proves to be a lightweight, compact, and highly adaptable platform for developing wearable health devices. Its robust performance and extendable design make it an invaluable tool for researchers and developers in wearable health technol-ogy, facilitating the rapid conversion of innovative ideas into functional prototypes.

Point-of-care (POC) technologies have the potential to greatly improve the clinical lab testing and diagnostics process since they significantly reduce the wait time before getting the results. Microscope, as a very important instrument widely used in clinical testing, is a key component to be adapted to POC scenarios, and there have been various attempts to do so by replacing conventional microscopes with smartphone-attached systems. However, these attempts all utilized components that are either expensive or bulky, which greatly limits the widespread adaptation and utilization of these devices, especially in areas with limited resources. Thus we propose a design and prototype of$\mu$-Phone, an accessible microscope attachment for smartphones using cheap and easily accessible components to minimize the obstacles to resource availability. The proposed system has a depth of field of 5.25 micrometers, a pixel resolution of 26.3 pixels per micrometer and a spatial resolution of 2.19 micrometers, which makes it powerful enough to capture microscopic images for identification and analysis at a cellular level. With some future developments, this system has great potential to be deployed worldwide for quick and accurate POC analysis of physiological samples.

Wearable devices have become efficient tools for sports performance analysis. Professional systems heavily rely on the high-tech setup, which are expensive and privacy-invasive for amateur players. This paper addresses the gap between advanced professional systems and limited consumer options by proposing a low-cost, privacy-preserving approach for basketball shot detection and outcome prediction. We leverage accelerome-ter data from wrist-worn smartwatches, combined with audio recordings, to develop a system capable of identifying shot movements and predicting shot outcomes. The shot detection was achieved by a ID CNN model through accelerometer data and outcome classification was achieved by an audio classification model. We evaluated the system on 6 participants, and the macro F1 score for shot outcome classification in data streams are 81.53% and 78.07% on dominant hand and non-dominant hand, respectively. Our system opens up explorations in other domains, including medical or industrial activity recognition, where similar approaches can be applied.

The proliferation of e-cigarettes and portable vaporizers presents new opportunities for accurately and unobtrusively tracking e-cigarette use. PuffPacket is a hardware and soft-ware 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. Puff-Packet is generalizable to a wide variety of electronic nicotine,THC, and other drug delivery devices currently on the mar-ket. The hardware and software for PuffPacket is open-source so it can be expanded upon and leveraged for mobile health tracking research.

Motivated by the need to support those self-managing chronic pain, we report on the development and evaluation of a novel pressure-based tangible user interface (TUI) for the self-report of scalar values representing pain intensity. Our TUI consists of a conductive foam-based, force-sensitive resistor (FSR) covered in a soft rubber with embedded signal conditioning, an ARM Cortex-M0 microprocessor, and Bluetooth Low Energy (BLE). In-lab usability and feasibility studies with 28 participants found that individuals were able to use the device to make reliable reports with four degrees of freedom as well map squeeze pressure to pain level and visual feedback. Building on insights from these studies, we further redesigned the FSR into a wearable device with multiple form factors, including a necklace, bracelet, and keychain. A usability study with an additional 7 participants from our target population, elderly individuals with chronic pain, found high receptivity to the wearable design, which offered a number of participant-valued characteristics (e.g., discreetness) along with other design implications that serve to inform the continued refinement of tangible devices that support pain self-assessment.

In this paper we demonstrate that it is possible to help individuals regulate their emotions with mobile interventions that leverage the way we naturally react to our bodily signals. Previous studies demonstrate that the awareness of our bodily signals, such as our heart rate, directly influences the way we feel. By leveraging these findings we designed a wearable device to regulate user's anxiety by providing a false feedback of a slow heart rate. The results of an experiment with 67 participants show that the device kept the anxiety of the individuals in low levels when compared to the control group and the other conditions. We discuss the implications of our findings and present some promising directions for designing and developing this type of intervention for emotion regulation.

In this paper, we propose Nutrilyzer, a novel mobile sensing system for characterizing the nutrients and detecting adulterants in liquid food with the photoacoustic effect. By listening to the sound of the intensity modulated light or electromagnetic wave with different wavelengths, our mobile photoacoustic sensing system captures unique spectra produced by the transmitted and scattered light while passing through various liquid food. As different liquid foods with different chemical compositions yield uniquely different spectral signatures, Nutrilyzer's signal processing and machine learning algorithm learn to map the photoacoustic signature to various liquid food characteristics including nutrients and adulterants. We evaluated Nutrilyzer for milk nutrient prediction (i.e., milk protein) and milk adulterant detection. We have also explored Nutrilyzer for alcohol concentration prediction. The Nutrilyzer mobile system consists of an array of 16 LEDs in ultraviolet, visible and near-infrared region, two piezoelectric sensors and an ARM microcontroller unit, which are designed and fabricated in a printed circuit board and a 3D printed photoacoustic housing.

Persuasive technologies aim to influence user's behaviors. In order to be effective, many of the persuasive technologies de-veloped so far relies on user's motivation and ability, which is highly variable and often the reason behind the failure of such technology. In this paper, we present the concept of Mindless Computing, which is a new approach to persuasive technology design. Mindless Computing leverages theories and concepts from psychology and behavioral economics into the design of technologies for behavior change. We show through a systematic review that most of the current persuasive technologies do not utilize the fast and automatic mental processes for behavioral change and there is an opportunity for persuasive technology designers to develop systems that are less reliant on user's motivation and ability. We describe two examples of mindless technologies and present pilot studies with encouraging results. Finally, we discuss design guidelines and considerations for developing this type of persuasive technology.

In this paper, we present DoppleSleep -- 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 smartphone application that are benchmarked in terms of power and resource usage.

In digital music production, the phrase "in the box" refers to the increasing replacement of extraneous hardware devices with compatible software components. As controls move from hard to soft, we have seen an increase in usability issues for musicians and sound engineers dealing with a large number of temporal inputs and both continuous and discrete controls. We present the SonicExplorer application, which we developed to give users a new interface for exploring and manipulating audio. SonicExplorer leverages users' spatial and color perception to enhance exploration by visualizing the parameter space and providing implicit memory cues. The application also leverages bimanual input to aid in fluid exploration of multidimensional audio parameter spaces, and to minimize the need for switching between parameters.

In this paper, we propose BodyBeat, 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.