Hypertension is a leading risk factor for cardiovascular diseases. Traditional blood pressure monitoring methods are cumbersome and inadequate for continuous tracking, prompting the development of PPG-based cuffless blood pressure monitoring wearables. This study leverages deep learning models, including ResNet and Transformer, to analyze wrist PPG data collected with a smartwatch for efficient hypertension risk screening, eliminating the need for handcrafted PPG features. Using the Home Blood Pressure Monitoring (HBPM) longitudinal dataset of 448 subjects and five-fold cross-validation, our model was trained on over 68k spot-check instances from 358 subjects and tested on real-world continuous recordings of 90 subjects. The compact ResNet model with 0.124M parameters performed significantly better than traditional machine learning methods, demonstrating its effectiveness in distinguishing between healthy and abnormal cases in real-world scenarios.

The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneously. Key challenges lie in the high dimensionality and sparsity of scATAC-seq data, as well as the lack of a standardized schema for representing open chromatin regions (OCRs). Here, we present ChromFound, a foundation model tailored for scATAC-seq. ChromFound utilizes a hybrid architecture and genome-aware tokenization to effectively capture genome-wide long contexts and regulatory signals from dynamic chromatin landscapes. Pretrained on 1.97 million cells from 30 tissues and 6 disease conditions, ChromFound demonstrates broad applicability across 6 diverse tasks. Notably, it achieves robust zero-shot performance in generating universal cell representations and exhibits excellent transferability in cell type annotation and cross-omics prediction. By uncovering enhancer-gene links undetected by existing computational methods, ChromFound offers a promising framework for understanding disease risk variants in the noncoding genome. The implementation of ChromFound is available via https://github.com/JohnsonKlose/ChromFound.

Cinemagraph creates captivating video experience by combining elements of still photography and subtle motion. However, most existing cinemagraph video generation lacks depth information, being restricted within 2-dimensional (2D) image space. We advance cinemagraph from 2D image space to 3-dimensional (3D) space with high quality by proposing LoopGaussian. It is based on 3D Gaussian modeling, taking advantage of the 3D Gaussian Splatting (3D-GS) technique that has significantly improved the field of novel view synthesis. Here is a brief overview of our new approach: It employs 3D-GS to reconstruct 3D Gaussian point clouds from multi-view images of static scenes, where shape regularization is used to prevent blurring or artifacts caused by object deformation. To maintain local continuity between scenes, it then clusters the 3D Gaussian points by the proposed SuperGaussian algorithm using features acquired by an autoencoder tailored for 3D Gaussian. Similarities between clusters are used to derive an Eulerian motion field for describing velocities across the entire scene. The estimated Eulerian motion field drives the movement of the 3D Gaussian points, based on which a 3D Cinemagraph is generated through bidirectional animation. The resulting 3D Cinemagraph exhibits natural and seamlessly loopable dynamics. Experiment results validate the effectiveness of the proposed approach, demonstrating high-quality and visually appealing video generation.

Wearable devices have become indispensable gadgets in people’s daily lives nowadays; especially wireless earphones have experienced unprecedented growth in recent years, which lead to increasing interest and explorations of user authentication techniques. Conventional user authentication methods embedded in wireless earphones that use microphones or other modalities are vulnerable to environmental factors, such as loud noises or occlusions. To address this limitation, we introduce EarPPG, a new biometric modality that takes advantage of the unique in-ear photoplethysmography (PPG) signals, altered by a user’s unique speaking behaviors. When the user is speaking, muscle movements cause changes in the blood vessel geometry, inducing unique PPG signal variations. As speaking behaviors and PPG signals are unique, the EarPPG combines both biometric traits and presents a secure and obscure authentication solution. The system first detects and segments EarPPG signals and proceeds to extract effective features to construct a user authentication model with the 1D ReGRU network. We conducted comprehensive real-world evaluations with 25 human participants and achieved 94.84% accuracy, 0.95 precision, recall, and f1-score, respectively. Moreover, considering the practical implications, we conducted several extensive in-the-wild experiments, including body motions, occlusions, lighting, and permanence. Overall outcomes of this study possess the potential to be embedded in future smart earable devices.

Sign language is a primary language used by deaf and hard-of-hearing (DHH) communities. However, existing sign language translation solutions primarily focus on recognizing manual markers. The non-manual markers, such as negative head shaking, question markers, and mouthing, are critical grammatical and semantic components of sign language for better usability and generalizability. Considering the significant role of non-manual markers, we propose the TransASL, a real-time, end-to-end system for sign language recognition and translation. TransASL extracts feature from both manual markers and non-manual markers via a customized eyeglasses-style wearable device with two parallel sensing modalities. Manual marker information is collected by two pairs of outward-facing microphones and speakers mounted to the legs of the eyeglasses. In contrast, non-manual marker information is acquired from a pair of inward-facing microphones and speakers connected to the eyeglasses. Both manual and non-manual marker features undergo a multi-modal, multi-channel fusion network and are eventually recognized as comprehensible ASL content. We evaluate the recognition performance of various sign language expressions at both the word and sentence levels. Given 80 frequently used ASL words and 40 meaningful sentences consisting of manual and non-manual markers, TransASL can achieve the WER of 8.3% and 7.1%, respectively. Our proposed work reveals a great potential for convenient ASL recognition in daily communications between ASL signers and hearing people.

With the popularity of non-invasive vital signs detection, remote photoplethysmography (rPPG) is drawing attention in the community. Remote PPG or rPPG signals are extracted in a contactless manner that is more prone to artifacts than PPG signals collected by wearable sensors. To develop a robust and accurate pipeline to estimate heart rate (HR) from rPPG signals, we propose a novel real-time dynamic ROI tracking algorithm that applies to slight motions and light changes. Furthermore, we develop and include a signal quality index (SQI) to improve the HR estimation accuracy. Studies have explored optimal SQIs for PPG signals, but not for remote PPG signals. In this paper, we select and test six SQIs: Perfusion, Kurtosis, Skewness, Zero-crossing, Entropy, and signal-to-noise ratio (SNR) on 124 rPPG sessions from 30 participants wearing masks. Based on the mean absolute error (MAE) of HR estimation, the optimal SQI is selected and validated by Mann–Whitney U test (MWU). Lastly, we show that the HR estimation accuracy is improved by 29% after removing outliers decided by the optimal SQI, and the best result achieves the MAE of 2.308 bpm.

Catadioptric panoramic vision systems have been widely used in many fields because of their advantages such as a wide field of view, integrated imaging, and rotational symmetry. They also play a very important role in the monitoring of unmanned platforms. However, the resolution of catadioptric panoramic vision systems is not very high, usually less than 5 million pixels at present. Even if the resolution is greater than 5 million pixels, the unwrapping and rectification of these images is carried out off-line. Further, the systems are also deployed in a stationary or near-stationary state. This paper proposes an unwrapping and rectification method for a high-resolution catadioptric panoramic vision system that can be used while moving. It can segment dynamic circular mark regions from panoramic videos accurately, determine the center coordinates of circular images in real time, eliminate the processing of unwanted video regions, and shorten the image processing time. In addition, the center coordinates and radius of the circular mark regions are obtained simultaneously so that the image distortion caused by inaccurate center coordinates is reduced. The method uses radial and decentering distortion parameters and a correction factor for fitting a polynomial to rectify the panoramic video without distortion.

It attracts many researchers' attention to find a stereo matching algorithm both accurate and fast. Yoon and Kweon's Adaptive Support-Weight(ASW) method is supposed to be a very successful algorithm in both accuracy and speed. However, it is very time consuming for ASW to take a large number of pixels into consideration for computing a disparity. In this paper, we present a stereo matching algorithm based on the ASW method to solve this problem. We introduce a time efficient technique, partial sum, to reduce the information of pixels in a large window into a small one so that disparities can be computed using the large window of pixels with low time complexity. Our method can obtain better representation of local properties of a pixel than the ASW method. Experimental results show that our method produces more accurate disparity maps than ASW method.