Continuous and precise bladder volume monitoring is essential for patients with lower urinary tract dysfunction (LUTD) to support timely voiding and effective rehabilitation management. However, existing devices lack skin conformity and cannot support reliable "stick-once, long-term daily use" in real-world scenarios. We present BladderSense, a skin-conforming wireless wearable system featuring an X-shaped flexible phased-array ultrasound probe. Its development faces three key challenges: preserving beam focusing under skin deformation, maintaining accurate volume estimation despite bladder position shifts, and enabling low-power wireless transmission despite large raw data volume. To address these obstacles, we employ a suitable-frequency deep-focus design to stabilize beam quality, introduce a dual-orthogonal array with a shared geometric anchor and develop a coordinate-encoded deep learning (DL) model, together enabling bladder tracking and end-to-end volume estimation. An envelope-extraction-based compression scheme further enables Bluetooth Low Energy (BLE) transmission, supporting continuous monitoring with intermittent (1-minute) sensing. Experiments with 10 participants show that BladderSense provides accurate, robust bladder volume estimation across bladder changes, posture transitions, and dynamic daily activities, realizing dependable "stick-once, long-term monitoring" for LUTD patients.

Existing AI-based meeting summarization tools have enabled rapid generation of meeting notes, yet their reliability and user controllability remain limited. This paper explores human-AI collaboration for mobile meeting summarization and presents MeetSumAid, a multifunctional system that integrates summarization algorithms with an interactive user interface. The system is designed to support users in understanding, validating, and refining AI-generated summaries through natural interactions and flexible control mechanisms. By enabling real-time inspection, editing, and feedback, MeetSumAid facilitates reliable collaboration between humans and AI in dynamic meeting scenarios. A user study with 20 participants shows that MeetSumAid significantly improves summary quality, generation efficiency, and user-perceived reliability compared with baseline AI summarizers, while reducing cognitive load. Further analysis reveals how different interface components enhance users' engagement and confidence during collaboration. This work provides a practical step toward reliable and user-centered human-AI collaboration in mobile meeting summarization and offers actionable design implications for future intelligent collaborative systems.

Estimating the soluble solids content (SSC) in fruits is essential for meeting consumer expectations, ensuring the quality of processed products, and minimizing food waste. Current methods often rely on destructive sampling, expensive equipment, and complex procedures, which limit their practicality. This paper presents VNiScan-Fruit, a non-invasive, low-cost, and easy-to-use optical sensing system that utilizes visible-near-infrared (Vis-NIR) spectroscopy to estimate SSC by analyzing the interaction of light with fruit tissues to generate characteristic absorption spectra. Our approach diverges from traditional high-precision spectrometers, employing commercial LEDs and photodetectors (PD) to construct the optical sensing unit. We design and implement innovative ring-shaped rubber enclosure, interference elimination algorithms, and reconstruction strategy to extract low-dimensional reflectance spectral features from various fruits. These features are then processed using a specially designed nonlinear regression model, incorporating sucrose values obtained from a commercial refractometer to estimate SSC accurately. We tested VNiScan-Fruit on a range of fruits with diverse peel characteristics, demonstrating its ability to penetrate exocarps and effectively analyze fruits with rough surfaces and delicate tissues. The system achieved normalized mean absolute errors (NMAE) of 8%, imperceptible to consumers in sweetness difference, and remained stable under varying lighting and temperature conditions. Our findings highlight the potential of VNiScan-Fruit as a practical tool for non-destructive fruit quality assessment.

The dental plaque disclosing agent helps guide plaque removal by revealing plaque on the teeth. However, existing toothbrushes cannot provide visualization of the stained plaque beneath the toothpaste foam during brushing. To fill this gap, this paper proposes PIT and uses smartphone to provide real-time plaque visualization during brushing. PIT introduces novel hardware, including a micro camera, four green LEDs, and a mechanical structure, offering a stable camera view of bristle position and bristle rotation. To address the challenge of toothpaste foam obstruction, we derived an optical channel model that guided the design of the light source to enhance plaque visibility. Furthermore, we designed a deep neural network specifically for plaque segmentation under thick foam. Finally, the trained distilled student model was run on a smartphone and evaluated on both denture models and human subjects. The results show that PIT achieved an IoU (Intersection over Union) of 75.22% and a latency of 29 ms, with robust performance across various conditions. Evaluation by 10 participants revealed that PIT helped reduce plaque coverage to 5.6% within two minutes of brushing, significantly outperforming existing advanced toothbrushes.

Incorrect brushing methods normally lead to poor oral hygiene, and result in severe oral diseases and complications. While effective brushing can address this issue, individuals often struggle with incorrect brushing, like aggressive brushing, insufficient brushing, and missing brushing. To break this stalemate, in this paper, we proposed LiT, a toothbrushing monitoring system to assess the brushing status on 16 surfaces using the Bass technique. LiT utilizes commercial LED toothbrushes’ blue LEDs as transmitters, and incorporates only two low-cost photodetectors as receivers on the toothbrush head. It is challenging to determine optimal deployment positions and minimize photodetectors number to establish the light transmission channel in oral cavity. To address these challenges, we established mathematical models within the oral cavity based on the two photodetectors’ deployment to theoretically validate the feasibility and prove robustness. Furthermore, we designed a comprehensive framework to fight against the implementation challenges including brushing action separation, light interference on the outer surfaces of front teeth, toothpaste diversity, user variations, brushing hand variability, and incorrect brushings. Experimental results demonstrate that LiT achieves a highly accurate surface recognition rate of 95.3%, an estimated error for brushing duration of 6.1%, and incorrect brushing detection accuracy of 96.9%. Furthermore, LiT retains stable capability under a variety of circumstances, such as various lighting conditions, user movement, toothpaste diversity, and left and right-handed users.

Biochemical information sensing has always been one of the challenges in ubiquitous sensing research for mobile computing. Microorganisms will cause undetectable deterioration in drink production, such as wine and beverage, and microbial contamination is highly susceptible during storage like some liquors can be bottled for sometimes over ten years. Microbial culture methods are common for quality monitoring but unsuitable for real-time beverage quality monitoring. As far as we know, we are the first to use ubiquitous sensing for real-time microbial contamination detection. We designed a lightweight monitoring system called Microbe-Radar, which uses light signals to monitor real-time beverage quality. Microbe-Radar uses eight LEDs and a photodiode to detect fine-grained surface tension and absorption spectrum changes caused by microbial metabolites and growth during deterioration. Characteristic offset degree measurement and absorption spectrum dimension expansion are two critical technologies. Moreover, we implemented countermeasures against ambient light noise and sloshing interference. Microbe-Radar's surface tension and absorption spectrum measurement errors are only 0.89 mN/m and 2.4%, respectively, making identifying the contamination duration, microorganism content, and microorganism composition worthwhile. Experiments showed Microbe-Radar could determine potential issues with liquor quality when the liquid becomes health-threatening or even just contaminated, with an accuracy of 97.5%. Microbe-Radar can also be extended to beverage deterioration warning, with deterioration prediction accuracy of more than 90.6% for five beverages (milk, apple juice, etc.).

Neglecting proper oral hygiene has proven to potentially lead to severe oral disease, resulting in complications over time. Careful brushing can mitigate the problem, but it is common for individuals to dedicate insufficient time to the various areas of their teeth. We propose LiT to monitor the brushing situation of 16 Bass technique surfaces in real-time. LiT relies on commercial toothbrushes with blue LEDs as a transmitter and requires only 2 low-cost photosensors as receivers on the toothbrush head. However, the transmission channel of light in the oral cavity is unclear. Finding the optimal deployment positions and minimizing the number of photosensors is challenging. To tackle these obstacles, we design the positioning of the 2 photosensors and create a transmission model within the oral cavity to verify the feasibility theoretically. Additionally, obstacles in implementation include separating brushing action accurately, interference of light on the outer surfaces of front teeth, and individual variability. To overcome these challenges, we develop corresponding technologies and a comprehensive framework. Experiments with 16 users show that LiT achieves a highly accurate recognition rate of 95.3% with an error estimate for brushing duration of 6.1%. Furthermore, LiT also proves resilient under user motion and environmental interference.

Traditional liquid identification instruments are often unavailable to the general public. This paper shows the feasibility of identifying unknown liquids with commercial lightweight devices, such as a smartphone. The wisdom arises from the fact that different liquid molecules have various viscosity coefficients, so they need to overcome dissimilitude energy barriers during relative motion. With this intuition in mind, we introduce a novel model that measures liquids’ viscosity based on active vibration. The idea sounds straightforward, yet, it is challenging to build up a robust system utilizing the built-in accelerometer in smartphones. Practical issues include under-sampling, self-interference, and volume change impact. Instead of using machine learning techniques, we tackle these issues through multiple signal processing stages to reconstruct the original signals and cancel out the interference. Our approach achieved the liquid viscosity estimates with a mean relative error of 2.3% and distinguish 30 kinds of liquid with an average accuracy of 97.33%.

In industrialized wine production, brewing and aging are two key steps. These two processes require the liquors to be bottled for a long time, sometimes more than ten years. The liquor is vulnerable and highly susceptible to microbial contamination during storage, causing undetectable deterioration. During the production process, wineries control the indoor temperature and carbon dioxide concentration to slow down other microorganisms' reproduction speed. These methods, however, do not prevent pathogenic microorganism growth. Currently, microbial culture methods are not suitable for real-time liquor quality monitoring in wineries. Therefore, we have designed a lightweight monitoring system called Lili, which uses light signals to monitor real-time liquor quality changes. Lili detects the changes in surface tension and absorption spectrum caused by microbial metabolites and growth during deterioration. Lili employs eight LEDs and one photodiode to achieve fine-grained surface tension and absorption spectrum measurements. By analyzing these changes, Lili realizes real-time quality monitoring. In this paper, the characteristic offset degree measurement and the absorption spectrum dimension expansion are two critical technologies. In addition, we implemented countermeasures against ambient light noise and sloshing interference. Lili's surface tension and absorption spectrum measurement errors are only 0.89 mN/m and 2.4%, respectively, making it useful to identify the contamination duration, microorganism content and microorganism composition. These two data points can be used to determine potential issues with liquor quality when the liquor becomes health-threatening or even just contaminated, with an accuracy of 97.5%.

Traditional liquid identification instruments are often unavailable to the general public. This paper shows the feasibility of identifying unknown liquids with commercial lightweight devices, such as a smartphone. The wisdom arises from the fact that different liquid molecules have various viscosity coefficients, so they need to overcome dissimilitude energy barriers during relative motion. With this intuition in mind, we introduce a novel model that measures liquids' viscosity based on active vibration. Yet, it is challenging to build up a robust system utilizing the built-in accelerometer in smartphones. Practical issues include under-sampling, self-interference, and volume change impact. Instead of machine learning, we tackle these issues through multiple signal processing stages to reconstruct the original signals and cancel out the interference. Our approach could achieve the liquid viscosity estimates with a mean relative error of 2.9% and distinguish 30 kinds of liquid with an average accuracy of 95.47%.