EGOFALLS: A visual-audio dataset and benchmark for fall detection using egocentric cameras

Falls are significant and often fatal for vulnerable populations such as the elderly. Previous works have addressed the detection of falls by relying on data capture by a single sensor, images or accelerometers. In this work, we rely on multimodal descriptors extracted from videos captured by egocentric cameras. Our proposed method includes a late decision fusion layer that builds on top of the extracted descriptors. Furthermore, we collect a new dataset on which we assess our proposed approach. We believe this is the first public dataset of its kind. The dataset comprises 10,948 video samples by 14 subjects. We conducted ablation experiments to assess the performance of individual feature extractors, fusion of visual information, and fusion of both visual and audio information. Moreover, we experimented with internal and external cross-validation. Our results demonstrate that the fusion of audio and visual information through late decision fusion improves detection performance, making it a promising tool for fall prevention and mitigation

Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformation

We study the problem of few-shot physically-aware articulated mesh generation. By observing an articulated object dataset containing only a few examples, we wish to learn a model that can generate diverse meshes with high visual fidelity and physical validity. Previous mesh generative models either have difficulties in depicting a diverse data space from only a few examples or fail to ensure physical validity of their samples. Regarding the above challenges, we propose two key innovations, including 1) a hierarchical mesh deformation-based generative model based upon the divide-and-conquer philosophy to alleviate the few-shot challenge by borrowing transferrable deformation patterns from large scale rigid meshes and 2) a physics-aware deformation correction scheme to encourage physically plausible generations. We conduct extensive experiments on 6 articulated categories to demonstrate the superiority of our method in generating articulated meshes with better diversity, higher visual fidelity, and better physical validity over previous methods in the few-shot setting. Further, we validate solid contributions of our two innovations in the ablation study. Project page with code is available at https://meowuu7.github.io/few-arti-obj-gen.Comment: ICCV 2023. Project Page: https://meowuu7.github.io/few-arti-obj-ge

Exploring RNA and protein 3D structures by geometric algorithms

Many problems in RNA and protein structures are related with their specific geometric properties. Geometric algorithms can be used to explore the possible solutions of these problems. This dissertation investigates the geometric properties of RNA and protein structures and explores three different ways that geometric algorithms can help to the study of the structures. Determine accurate structures. Accurate details in RNA structures are important for understanding RNA function, but the backbone conformation is difficult to determine and most existing RNA structures show serious steric clashes (greater than or equal to 0.4 A overlap). I developed a program called RNABC (RNA Backbone Correction) that searches for alternative clash-free conformations with acceptable geometry. It rebuilds a suite (unit from sugar to sugar) by anchoring phosphorus and base positions, which are clearest in crystallographic electron density, and reconstructing other atoms using forward kinematics and conjugate gradient methods. Two tests show that RNABC improves backbone conformations for most problem suites in S-motifs and for many of the worst problem suites identified by members of the Richardson lab. Display structure commonalities. Structure alignment commonly uses root mean squared distance (RMSD) to measure the structural similarity. I first extend RMSD to weighted RMSD (wRMSD) for multiple structures and show that using wRMSD with multiplicative weights implies the average is a consensus structure. Although I show that finding the optimal translations and rotations for minimizing wRMSD cannot be decoupled for multiple structures, I develop a near-linear iterative algorithm to converge to a local minimum of wRMSD. Finally I propose a heuristic algorithm to iteratively reassign weights to reduce the effect of outliers and find well-aligned positions that determine structurally conserved regions. Distinguish local structural features. Identifying common motifs (fragments of structures common to a group of molecules) is one way to further our understanding of the structure and function of molecules. I apply a graph database mining technique to identify RNA tertiary motifs. I abstract RNA molecules as labeled graphs, use a frequent subgraph mining algorithm to derive tertiary motifs, and present an iterative structure alignment algorithm to classify tertiary motifs and generate consensus motifs. Tests on ribosomal and transfer RNA families show that this method can identify most known RNA tertiary motifs in these families and suggest candidates for novel tertiary motifs

Elderly Fall Detection Systems: A Literature Survey

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial

Urban Communication Strategy Based on Short Video Platform: A Case Study of TikTok

The concept of urban communication bred in the new media era takes short video platforms as the vehicle for emerging Internet-famous cities. The internal relationship between the communication strategy and development orientation of urban communication and the image practice and performance space of short videos is worth studying. Taking Chongqing and Xi’an as the research objects, this paper explores the operation mode and communication concept of urban space in the new media era from the perspectives of communication origin, user participation, and in-depth communication. The research shows that the advent of mobile short-video platforms offers a new vehicle and channel for the communication and reconstruction of urban space and enriches the audience’s cognition of city images

High-resolution spectroscopic photoacoustic tomography for non-invasive functional imaging of small-animal brains in vivo

Based on the multiwavelength laser-based photoacoustic tomography, noninvasive imaging of cerebral blood oxygenation and blood volume in small-animal brains in vivo was realized. The high sensitivity of this technique is based on the spectroscopic differences between oxy- and deoxy-hemoglobins whereas its spatial resolution is diffraction-limited by the photoacoustic signals. The point-by-point distributions of hemoglobin oxygen saturation and total concentration of hemoglobin in the cerebral cortical venous vessels, altered by systemic physiological modulations including hyperoxia and hypoxia, were visualized successfully through the intact skin and skull. This technique can potentially accelerate the progress in neuroscience and provide important new insights into cerebrovascular physiology and brain function

Deep penetrating photoacoustic tomography in biological tissues

Photoacoustic tomography (PAT) in a circular scanning configuration was developed to image the deeply embedded optical heterogeneity in biological tissues. Based on the intrinsic contrast between blood and chicken breast muscle, an embedded blood object that was 5 cm deep in the tissue was detected using pulsed laser light at a wavelength of 1064 nm. Compared with detectors for flat active surfaces, cylindrically focused ultrasonic transducers can reduce the interference generated from the off-plane photoacoustic sources and make the image in the scanning plane clearer. While the optical penetration was optimized with near-infrared laser pulses of 800 nm in wavelength, the optical contrast was enhanced by indocyanine green (ICG) whose absorption peak matched the laser wavelength. This optimized PAT was able to image fine objects embedded at a depth of up to 5.2-cm, which is 6.2 times the 1/e optical penetration depth, in chicken breast muscle, at a resolution of < ~750 microns with a sensitivity of <7 pmol of ICG in blood. The resolution was found to deteriorate slowly with increasing imaging depth