arXiv subjects

Justin Chan

Publications and source records attributed to Justin Chan.

At least 19 recordsLinked to original sources

arXiv2026

WeeCare: Towards Handheld Bladder Fullness Sensing with a Conformable Pad

Patients with bladder dysfunction often lose the sensation of bladder fullness and cannot void naturally, forcing reliance on fixed-schedule catheterization that is uncomfortable and risks complications. We present WeeCare, a handheld conformable pad with fabric electrodes for on-demand bladder fullness sensing using electrical impedance tomography (EIT). The central challenge is that repeated removal and reattachment can introduce variation in electrode position and contact quality. We assess WeeCare along three axes: in-silico simulations characterizing electrode layout and noise robustness, in-vitro phantom experiments across urine salinities and filling levels, and an in-vivo study tracking voiding dynamics and fullness sensing across 8 participants, with filling dynamics characterized in a single participant. Our results provide an early assessment of WeeCare's feasibility under controlled conditions.

cs.HC↗

arXiv2026

GlintMarkers: Spatial Perception on XR Eyewear using Corneal Reflections

We present GlintMarkers, the first system to perform gaze-driven spatial perception using the inward-facing cameras on XR eyewear. Our key observation is that the cornea acts as a mirror that encodes both gaze direction and visual information about the environment in a small, low-contrast reflection. To extract spatial and semantic information from this reflection despite the camera's limited pixel budget, we present a passive retroreflective marker design that concentrates reflected near-infrared light onto the cornea, producing bright glint patterns. We develop a custom Perspective-n-Point (PnP) estimation framework adapted to corneal imaging and perform orientation and distance estimation of tagged objects, as well as unique object identification.

cs.HC↗

arXiv2026

Active noise cancellation on open-ear smart glasses

Active noise cancellation (ANC) is widely deployed on consumer headphones and earbuds to suppress environmental noise. However, existing ANC systems require an error microphone at the user's ear canal to measure residual sound, preventing deployment on emerging open-ear wearable devices such as smart glasses and VR headsets, which leave the ear unoccluded. Here we present an ANC system for open-ear wearables that suppresses environmental noise using only microphones and miniaturized open-ear speakers embedded within the frame of the wearables, removing the need for an in-ear error microphone. Our low-latency computational pipeline uses a neural network to estimate the noise at the ear from an array of eight microphones distributed around the wearable's frame and generates an anti-noise signal in real-time. This mapping generalizes to unseen users and acoustic environments without prior acoustic measurement. We develop a custom glasses prototype and evaluate across eleven unseen users and eight unseen environments under mobility in the 100 to 1000 Hz frequency range, where environmental noise is concentrated. We achieve a mean noise reduction of 9.6 dB without any calibration, and 11.2 dB with a brief user-specific calibration. Further, we demonstrate that our approach extends to the broader class of open-ear wearables including VR headsets and headbands.

eess.AS↗

arXiv2025

Contactless Monitoring of Muscle Vibrations During Exercise with a Chaos-Inspired Radar

In this paper, our goal is to enable quantitative feedback on muscle fatigue during exercise to optimize exercise effectiveness while minimizing injury risk. We seek to capture fatigue by monitoring surface vibrations that muscle exertion induces. Muscle vibrations are unique as they arise from the asynchronous firing of motor units, producing surface micro-displacements that are broadband, nonlinear, and seemingly stochastic. Accurately sensing these noise-like signals requires new algorithmic strategies that can uncover their underlying structure. We present GigaFlex the first contactless system that measures muscle vibrations using mmWave radar to infer muscle force and detect fatigue. GigaFlex draws on algorithmic foundations from Chaos theory to model the deterministic patterns of muscle vibrations and extend them to the radar domain. Specifically, we design a radar processing architecture that systematically infuses principles from Chaos theory and nonlinear dynamics throughout the sensing pipeline, spanning localization, segmentation, and learning, to estimate muscle forces during static and dynamic weight-bearing exercises. Across a 23-participant study, GigaFlex estimates maximum voluntary isometric contraction (MVIC) root mean square error (RMSE) of 5.9\%, and detects one to three Repetitions in Reserve (RIR), a key quantitative muscle fatigue metric, with an AUC of 0.83 to 0.86, performing comparably to a contact-based IMU baseline. Our system can enable timely feedback that can help prevent fatigue-induced injury, and opens new opportunities for physiological sensing of complex, non-periodic biosignals.

physics.med-ph↗

arXiv2025

L-Band Milliwatt Room-Temperature Solid-State Maser

Molecular room temperature masers have emerged as promising sources of coherent microwaves, but systematic comparisons of organic gain media under uniform conditions remain limited. This paper presents a characterization of two systems, pentacene doped para terphenyl (Pc:PTP, 1.45 GHz) and 6,13 diazapentacene doped para terphenyl (DAP:PTP, 1.478 GHz), examined at four concentrations, including a new 0.05 percent DAP:PTP sample. By evaluating L band masing media under identical conditions, optimal doping levels and gain materials for high power operation are identified. The optimized system produced room temperature continuous wave masing with a peak output of 2.34 mW (+3.69 dBm), marking the first milliwatt level emission from an organic maser. Spectral coherence times of 465 ns and coherence lengths up to 150 m were obtained. Coupling to a high Q cavity mode enables collective spin photon interactions, with Rabi oscillations revealing coherent ensemble dynamics. Frequency domain analysis shows normal mode splitting of 1.37 MHz for Pc:PTP and 2.14 MHz for DAP:PTP, confirming strong coupling. Cavity QED analysis yields cooperativities C* = 304-803 for Pc:PTP and 405-1071 for DAP:PTP, among the highest for organic systems. Quantitative metrics of signal to noise ratio, spectral coherence distance, and throughput demonstrate the potential of these masers for radar, secure communication, and quantum interface technologies.

physics.app-ph↗

arXiv2025

Feasibility of Free-Space Transmission using L-Band Maser Signals in Organic Gain Media

Atmospheric conditions such as fog, humidity, and scattering by foliage routinely degrade optical free-space (FS) links, motivating alternatives that are robust in adverse conditions. Coherent microwave sources offer a compelling alternative for quantum-secure communication, yet their propagation outside enclosed resonators has remained untested. Here, we demonstrate room-temperature FS transmission of maser signals generated using organic L-band (1-2 GHz) gain media, pentacene doped p-terphenyl (Pc: PTP), and Diazapentacene-doped p-terphenyl (DAP:PTP). Spectral and temporal coherence is preserved over distances up to 25 cm, approximately one wavelength at the masing frequency. Tests included polarisation misalignment, high-humidity conditions, and partial occlusion by foliage to emulate realistic FS reception scenarios. Strong spin-photon coupling was maintained, as confirmed by persistent rabi oscillations and normal-mode splitting. An instantaneous peak output of 4.29 mW (+6.32 dBm) from a 0.01% DAP:PTP gain medium. with a pulse duration of ~4 {\mu}s, marks a performance benchmark for directly coupled masers. These findings demonstrate a proof-of-concept for masers as viable platforms for short-range, interference-resilient coherent microwave links, with future relevance to quantum sensing and secure communication technologies.

physics.app-ph↗

arXiv2025

DropleX: Liquid sensing on tablet touchscreens

We present DropleX, the first system that enables liquid sensing using the capacitive touchscreen of commodity tablets. DropleX detects microliter-scale liquid samples, and performs non-invasive, through-container measurements for liquid analysis. These capabilities are made possible by a physics-informed mechanism that disables the touchscreen's built-in adaptive filters, originally designed to reject the effects of liquid drops such as rain, without any hardware modifications. We model the touchscreen's sensing capabilities, limits, and non-idealities to inform the design of a signal processing and learning-based pipeline for liquid sensing. Under controlled laboratory conditions, our system achieves 89-99% accuracy in detecting microliter-scale adulteration in soda, wine, and milk, 94-96% accuracy in threshold detection of trace chemical concentrations, and 86-96% accuracy in through-container adulterant detection. These exploratory results demonstrate the potential of repurposing commodity touchscreens as a liquid characterization platform for laboratory settings, food and beverage testing, and chemical analysis applications.

cs.HC↗

arXiv2025

Measuring multi-site pulse transit time with an AI-enabled mmWave radar

Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relationship to diastolic blood pressure (DBP). We present the first AI-enabled mmWave system for contactless multi-site PTT measurement using a single radar. By leveraging radar beamforming and deep learning algorithms our system simultaneously measures PTT and estimates diastolic blood pressure at multiple sites. The system was evaluated across three physiological pathways - heart-to-radial artery, heart-to-carotid artery, and mastoid area-to-radial artery -- achieving correlation coefficients of 0.73-0.89 compared to contact-based reference sensors for measuring PTT. Furthermore, the system demonstrated correlation coefficients of 0.90-0.92 for estimating DBP, and achieved a mean error of -1.00-0.62 mmHg and standard deviation of 4.97-5.70 mmHg, meeting the FDA's AAMI guidelines for non-invasive blood pressure monitors. These results suggest that our proposed system has the potential to provide a non-invasive measure of cardiovascular health across multiple regions of the body.

physics.med-ph↗

arXiv2025

LubDubDecoder: Bringing Micro-Mechanical Cardiac Monitoring to Hearables

We present LubDubDecoder, a system that enables fine-grained monitoring of micro-cardiac vibrations associated with the opening and closing of heart valves across a range of hearables. Our system transforms the built-in speaker, the only transducer common to all hearables, into an acoustic sensor that captures the coarse "lub-dub" heart sounds, leverages their shared temporal and spectral structure to reconstruct the subtle seismocardiography (SCG) and gyrocardiography (GCG) waveforms, and extract the timing of key micro-cardiac events. In an IRB-approved feasibility study with 25 users, our system achieves correlations of 0.88-0.95 compared to chest-mounted reference measurements in within-user and cross-user evaluations, and generalizes to unseen hearables using a zero-effort adaptation scheme with a correlation of 0.91. Our system is robust across remounting sessions and music playback.

cs.HC↗

arXiv2025

VergeIO: Depth-Aware Eye Interaction on Glasses

There is growing industry interest in unobtrusive designs for electrooculography (EOG) sensing of eye gestures on glasses (e.g. JINS MEME and Apple eyewear). We present VergeIO, an EOG-based glasses system that enables depth-aware eye interaction by sensing vergence with a glasses-compatible electrode layout and smart glass prototype. It can distinguish between four depth-based eye gestures with 97% accuracy on unseen users without any calibration in a user study across 20 users and 1,520 gesture instances. To reduce false detections, we incorporate a motion artifact detection pipeline and a preamble-based activation scheme. The system uses dry sensors without any adhesives or gel and operates in real time with 3 mW power consumption by the analog sensing front-end.

cs.HC↗

arXiv2025

SonicSieve: Bringing Directional Speech Extraction to Smartphones Using Acoustic Microstructures

Imagine placing your smartphone on a table in a noisy restaurant and clearly capturing the voices of friends seated around you, or recording a lecturer's voice with clarity in a reverberant auditorium. We introduce SonicSieve, the first intelligent directional speech extraction system for smartphones using a bio-inspired acoustic microstructure. Our passive design embeds directional cues onto incoming speech without any additional electronics. It attaches to the in-line mic of low-cost wired earphones which can be attached to smartphones. We present an end-to-end neural network that processes the raw audio mixtures in real-time on mobile devices. Our results show that SonicSieve achieves a signal quality improvement of 5.0 dB when focusing on a 30{\deg} angular region. Additionally, the performance of our system based on only two microphones exceeds that of conventional 5-microphone arrays.

cs.SD↗

arXiv2025

Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems

AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data. However, the reliability (a.k.a. trust) of these systems is fundamentally challenged by the potential for LLMs to produce errors, limiting their adoption in critical domains. To help address this reliability bottleneck, we introduce semantic integrity constraints (SICs) -- a declarative abstraction for specifying and enforcing correctness conditions over LLM outputs in semantic queries. SICs generalize traditional database integrity constraints to semantic settings, supporting common types of constraints, such as grounding, soundness, and exclusion, with both reactive and proactive enforcement strategies. We argue that SICs provide a foundation for building reliable and auditable AI-augmented data systems. Specifically, we present a system design for integrating SICs into query planning and runtime execution and discuss its realization in AI-augmented DPSs. To guide and evaluate our vision, we outline several design goals -- covering criteria around expressiveness, runtime semantics, integration, performance, and enterprise-scale applicability -- and discuss how our framework addresses each, along with open research challenges.

cs.DB↗

arXiv2024

Implementations of Cooperative Games Under Non-Cooperative Solution Concepts

Cooperative games can be distinguished as non-cooperative games in which players can freely sign binding agreements to form coalitions. These coalitions inherit a joint strategy set and seek to maximize collective payoffs. When the payoffs to each coalition under some non-cooperative solution concept coincide with their value in the cooperative game, the cooperative game is said to be implementable and the non-cooperative game its implementation. This paper proves that all strictly superadditive partition function form games are implementable under Nash equilibrium and rationalizability; that all weakly superadditive characteristic function form games are implementable under Nash equilibrium; and that all weakly superadditive partition function form games are implementable under trembling hand perfect equilibrium. Discussion then proceeds on the appropriate choice of non-cooperative solution concept for the implementation.

econ.TH↗

arXiv2023

Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables

Imagine being able to listen to the birds chirping in a park without hearing the chatter from other hikers, or being able to block out traffic noise on a busy street while still being able to hear emergency sirens and car honks. We introduce semantic hearing, a novel capability for hearable devices that enables them to, in real-time, focus on, or ignore, specific sounds from real-world environments, while also preserving the spatial cues. To achieve this, we make two technical contributions: 1) we present the first neural network that can achieve binaural target sound extraction in the presence of interfering sounds and background noise, and 2) we design a training methodology that allows our system to generalize to real-world use. Results show that our system can operate with 20 sound classes and that our transformer-based network has a runtime of 6.56 ms on a connected smartphone. In-the-wild evaluation with participants in previously unseen indoor and outdoor scenarios shows that our proof-of-concept system can extract the target sounds and generalize to preserve the spatial cues in its binaural output. Project page with code: https://semantichearing.cs.washington.edu

cs.SD↗

arXiv2023

Underwater 3D positioning on smart devices

The emergence of water-proof mobile and wearable devices (e.g., Garmin Descent and Apple Watch Ultra) designed for underwater activities like professional scuba diving, opens up opportunities for underwater networking and localization capabilities on these devices. Here, we present the first underwater acoustic positioning system for smart devices. Unlike conventional systems that use floating buoys as anchors at known locations, we design a system where a dive leader can compute the relative positions of all other divers, without any external infrastructure. Our intuition is that in a well-connected network of devices, if we compute the pairwise distances, we can determine the shape of the network topology. By incorporating orientation information about a single diver who is in the visual range of the leader device, we can then estimate the positions of all the remaining divers, even if they are not within sight. We address various practical problems including detecting erroneous distance estimates, addressing rotational and flipping ambiguities as well as designing a distributed timestamp protocol that scales linearly with the number of devices. Our evaluations show that our distributed system running on underwater deployments of 4-5 commodity smart devices can perform pairwise ranging and localization with median errors of 0.5-0.9 m and 0.9-1.6 m

cs.NI↗

arXiv2022

Wireless earbuds for low-cost hearing screening

We present the first wireless earbud hardware that can perform hearing screening by detecting otoacoustic emissions. The conventional wisdom has been that detecting otoacoustic emissions, which are the faint sounds generated by the cochlea, requires sensitive and expensive acoustic hardware. Thus, medical devices for hearing screening cost thousands of dollars and are inaccessible in low and middle income countries. We show that by designing wireless earbuds using low-cost acoustic hardware and combining them with wireless sensing algorithms, we can reliably identify otoacoustic emissions and perform hearing screening. Our algorithms combine frequency modulated chirps with wideband pulses emitted from a low-cost speaker to reliably separate otoacoustic emissions from in-ear reflections and echoes. We conducted a clinical study with 50 ears across two healthcare sites. Our study shows that the low-cost earbuds detect hearing loss with 100% sensitivity and 89.7% specificity, which is comparable to the performance of a $8000 medical device. By developing low-cost and open-source wearable technology, our work may help address global health inequities in hearing screening by democratizing these medical devices.

cs.CY↗

arXiv2022

Real-Time Target Sound Extraction

We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture with a stack of dilated causal convolution layers as the encoder, and a transformer decoder layer as the decoder. This hybrid architecture uses dilated causal convolutions for processing large receptive fields in a computationally efficient manner while also leveraging the generalization performance of transformer-based architectures. Our evaluations show as much as 2.2-3.3 dB improvement in SI-SNRi compared to the prior models for this task while having a 1.2-4x smaller model size and a 1.5-2x lower runtime. We provide code, dataset, and audio samples: https://waveformer.cs.washington.edu/.

cs.SD↗

arXiv2022

Underwater Acoustic Ranging Between Smartphones

We present a novel underwater system that can perform acoustic ranging between commodity smartphones. To achieve this, we design a real-time underwater ranging protocol that computes the time-of-flight between smartphones. To address the severe underwater multipath, we present a dual-microphone optimization algorithm that can more reliably identify the direct path. Our underwater evaluations show that our system has median errors of 0.48-0.86 m at distances upto 35 m. Further, our system can operate across smartphone model pairs and works in the presence of clock drifts. While existing underwater localization research is targeted for custom hydrophone hardware, we believe that our work breaks new ground by demonstrating a path to bringing underwater ranging capabilities to billions of existing smartphones, without additional hardware.

cs.NI↗