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Remote monitoring and evaluation of pulmonary diseases via telemedicine are important to disease diagnosis and management, but current telemedicine solutions have limited capability of objectively examining the airway's internal physiological conditions that are crucial to pulmonary disease evaluation. Existing solutions based on smartphone sensing are also limited to externally monitoring breath rates, respiratory events, or lung function. In this paper, we present PTEase, a new system design that addresses these limitations and uses commodity smartphones to examine the airway's internal physiological conditions. PTEase uses active acoustic sensing to measure the internal changes of lower airway caliber, and then leverages machine learning to analyze the sensory data for pulmonary disease evaluation. We implemented PTEase as a smartphone app, and verified its measurement error in lab-controlled settings as <10%. Clinical studies further showed that PTEase reaches 75% accuracy on disease prediction and 11%-15% errors in estimating lung function indices. Given that such accuracy is comparable with that in clinical practice using spirometry, PTEase can be reliably used as an assistive telemedicine tool for disease evaluation and monitoring.

Due to the widespread applications of high-dimensional representations in many fields, the three-dimension (3D) display technique is increasingly being used for commercial purpose in a holographic-like and immersive demonstration. However, the visual discomfort and fatigue of 3D head mounts demonstrate the limits of usage in the sphere of marketing. The compressive light field (CLF) display is capable of providing binocular and motion parallaxes by stacking multiple liquid crystal screens without any extra accessories. It leverages optical viewpoint fusion to bring an immersive and visual-pleasing experience for viewers. Unfortunately, its practical application has been limited by processing complexity and reconstruction performance. In this paper, we propose a dual-guided learning-based factorization on polarization-based CLF display with depth-assisted calibration (DAC). This substantially improves the visual performance of factorization in real-time processing. In detail, we first take advantage of a dual-guided network structure under the constraints of reconstructed and viewing images. Additionally, by utilizing the proposed DAC, we distribute each pixel on displayed screens following the real depth. Furthermore, the subjective performance is increased by using a Gauss-distribution-based weighting (GDBW) toward the concentration of the observer’s angular position. Experimental results illustrate the improved performance in qualitative and quantitative aspects over other competitive methods. Amore »CLF prototype is assembled to verify the practicality of our factorization.

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A soft smart bandage is capable of multiplexed metabolic monitoring, antimicrobial treatment, and electrical stimulation.

Pulmonary diseases, such as asthma and Chronic Obstructive Pulmonary Disease (COPD), constitute a major public health challenge. The disease symptoms, including airway obstruction and inflammation, usually result in changes in airway mechanical properties, such as the caliber and impedance of the airway. To measure such airway properties for disease evaluation and diagnosis purposes, pulmonary function tests (PFT) has been widely adopted. However, most existing PFT systems require expensive and cumbersome hardware that are impossible to be used out of clinic. To allow out-clinic continuous pulmonary disease evaluation, in this paper we present AWARE, a new sensing and AI system that supports accurate and reliable PFT using commodity smartphones. AWARE uses a smartphone to transmit acoustic signals and reconstructs the profile of human airway based on the analysis of reflected acoustic waves captured from the smartphone's microphone. The subject's pulmonary condition is then evaluated by a multi-task learning model that integrates both the airway measurements and the subject's lung function records as the ground truth. Evaluations on 75 human subjects demonstrate that AWARE has the capability to achieve 80% accuracy on distinguishing between humans with healthy pulmonary function and with asthma symptoms.