Fast 3D Modeling of Prosthetic Robotic Hands Based on a Multi-Layer Deformable Design.

Current research of designing prosthetic robotic hands mainly focuses on improving their functionality by devising new mechanical structures and actuation systems. Most of existing work relies on a single structure/system (e.g., bone-only or tissue-only) and ignores the fact that the human hand is composed of multiple functional structures (e.g., skin, bones, muscles, and tendons). This may increase the difficulty of the design process and lower the flexibility of the fabricated hand. To tackle this problem, this paper proposes a three-dimensional (3D) printable multi-layer design that models the hand with the layers of skin, tissues, and bones. The proposed design first obtains the 3D surface model of a target hand via 3D scanning, and then generates the 3D bone models from the surface model based on a fast template matching method. To overcome the disadvantage of the rigid bone layer in deformation, the tissue layer is introduced and represented by a concentric tube-based structure, of which the deformability can be explicitly controlled by a parameter. The experimental results show that the proposed design outperforms previous designs remarkably. With the proposed design, prosthetic robotic hands can be produced quickly with low cost and be customizable and deformable.

PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning.

This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

A history of crowd simulation: the past, evolution, and new perspectives.

This paper aims to discuss the past, evolution, and new perspectives in crowd simulation. Many work have been produced and published in this area that was launched approximately 30 years ago. In this paper, we re-visited the main aspects of the area, presenting the periods and evolution we had in the past. In addition, we also discuss the present and possible trends for the future.

Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia.

Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.

Automated Verbal and Non-verbal Speech Analysis of Interviews of Individuals with Schizophrenia and Depression.

Schizophrenia and depression are the two most common mental disorders associated with negative symptoms that contribute to poor functioning and quality of life for millions of patients globally. This study is part of a larger research project. The overall aim of the project is to develop an automated objective pipeline that aids clinical diagnosis and provides more insights into symptoms of mental illnesses. In our previous work, we have analyzed non-verbal cues and linguistic cues of individuals with schizophrenia. In this study, we extend our work to include participants with depression. Powered by natural language processing techniques, we extract verbal features, both dictionary-based and vector-based, from participants' interviews that were automatically transcribed. We also extracted conversational, phonatory, articulatory and prosodic features from the interviews to understand the conversational and acoustic characteristics of schizophrenia and depression. Combining these features, we applied ensemble learning with leave-one-out cross-validation to classify healthy controls, schizophrenic and depressive patients, achieving an accuracy of 69%-75% in paired classification. From those same features, we also predict the subjective Negative Symptoms Assessment 16 scores of patients with schizophrenia or depression, yielding an accuracy of 90.5% for NSA2 but lower accuracy for other NSA indices. Our analysis also revealed significant linguistic and non-verbal differences that are potentially symptomatic of schizophrenia and depression respectively.

Hough Forest With Optimized Leaves for Global Hand Pose Estimation With Arbitrary Postures.

Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel algorithm to optimize the leaf weights in a Hough forest to assist global hand pose estimation with a single depth camera. Different from traditional Hough forest, we propose to learn the vote weights stored at the leaf nodes of a forest in a principled way to minimize average pose prediction error, so that ambiguous votes are largely suppressed during prediction fusion. Experiments show that the proposed method largely improves pose estimation accuracy with optimized leaf weights on both synthesis and real datasets and performs favorably compared to state-of-the-art convolutional neural network-based methods. On real-world depth videos, the proposed method demonstrates improved robustness compared to several other recent hand tracking systems from both industry and academy. Moreover, we utilize the proposed method to build virtual/augmented reality applications to allow users to manipulate and examine virtual objects with bare hands.

Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks.

In this paper, we present a novel method for real-time 3D hand pose estimation from single depth images using 3D Convolutional Neural Networks (CNNs). Image-based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information. Our proposed 3D CNN-based method, taking a 3D volumetric representation of the hand depth image as input and extracting 3D features from the volumetric input, can capture the 3D spatial structure of the hand and accurately regress full 3D hand pose in a single pass. In order to make the 3D CNN robust to variations in hand sizes and global orientations, we perform 3D data augmentation on the training data. To further improve the estimation accuracy, we propose applying the 3D deep network architectures and leveraging the complete hand surface as intermediate supervision for learning 3D hand pose from depth images. Extensive experiments on three challenging datasets demonstrate that our proposed approach outperforms baselines and state-of-the-art methods. A cross-dataset experiment also shows that our method has good generalization ability. Furthermore, our method is fast as our implementation runs at over 91 frames per second on a standard computer with a single GPU.

Evaluating Quality of Screen Content Images Via Structural Variation Analysis.

With the quick development and popularity of computers, computer-generated signals have drastically invaded into our daily lives. Screen content image is a typical example, since it also includes graphic and textual images as components as compared with natural scene images which have been deeply explored, and thus screen content image has posed novel challenges to current researches, such as compression, transmission, display, quality assessment, and more. In this paper, we focus our attention on evaluating the quality of screen content images based on the analysis of structural variation, which is caused by compression, transmission, and more. We classify structures into global and local structures, which correspond to basic and detailed perceptions of humans, respectively. The characteristics of graphic and textual images, e.g., limited color variations, and the human visual system are taken into consideration. Based on these concerns, we systematically combine the measurements of variations in the above-stated two types of structures to yield the final quality estimation of screen content images. Thorough experiments are conducted on three screen content image quality databases, in which the images are corrupted during capturing, compression, transmission, etc. Results demonstrate the superiority of our proposed quality model as compared with state-of-the-art relevant methods.

Robust 3D Hand Pose Estimation From Single Depth Images Using Multi-View CNNs.

Articulated hand pose estimation is one of core technologies in human-computer interaction. Despite the recent progress, most existing methods still cannot achieve satisfactory performance, partly due to the difficulty of the embedded high-dimensional nonlinear regression problem. Most existing data-driven methods directly regress 3D hand pose from 2D depth image, which cannot fully utilize the depth information. In this paper, we propose a novel multi-view convolutional neural network (CNN)-based approach for 3D hand pose estimation. To better exploit 3D information in the depth image, we project the point cloud generated from the query depth image onto multiple views of two projection settings and integrate them for more robust estimation. Multi-view CNNs are trained to learn the mapping from projected images to heat-maps, which reflect probability distributions of joints on each view. These multi-view heat-maps are then fused to estimate the optimal 3D hand pose with learned pose priors, and the unreliable information in multi-view heat-maps is suppressed using a view selection method. Experimental results show that the proposed method is superior to the state-of-the-art methods on two challenging data sets. Furthermore, a cross-data set experiment also validates that our proposed approach has good generalization ability.

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

New challenges have been brought out along with the emerging of 3D-related technologies, such as virtual reality, augmented reality (AR), and mixed reality. Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, and so on, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced-, and no-reference models.

Torso Crowds.

We present a novel dense crowd simulation method. In real crowds of high density, people manoeuvring the crowd need to twist their torso to pass between others. Our proposed method does not use the traditional disc-shaped agent, but instead employs capsule-shaped agents, which enables us to plan such torso orientations. Contrary to other crowd simulation systems, which often focus on the movement of the entire crowd, our method distinguishes between active agents that try to manoeuvre through the crowd, and passive agents that have no incentive to move. We introduce the concept of a focus point to influence crowd agent orientation. Recorded data from real human crowds are used for validation, which shows that our proposed model produces equivalent paths for 85 percent of the validation set. Furthermore, we present a character animation technique that uses the results from our crowd model to generate torso-twisting and side-stepping characters.

Design and development of a Virtual Dolphinarium for children with autism.

The recent proliferation of virtual reality (VR) technology applications in the autism therapy to promote learning and positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-assisted therapy has also become a topic of public and research interest for autism intervention and treatment. This paper will present an innovative design and development of a Virtual Dolphinarium for potential autism intervention. Instead of emulating the swimming with dolphins, our virtual dolphin interaction program will allow children with autism to act as dolphin trainers at the poolside and to learn (nonverbal) communication through hand gestures with the virtual dolphins. Immersive visualization and gesture-based interaction are implemented to engage children with autism within an immersive room equipped with a curved screen spanning a 320(°) and a high-end five-panel projection system. This paper will also report a pilot study to establish trial protocol of autism screening to explore the participants' readiness for the virtual dolphin interaction. This research will have two potential benefits in the sense of helping children with autism and protecting the endangered species.

Sensitivity of hip tissues contact evaluation to the methods used for estimating the hip joint center of rotation.

Computer-based simulations of human hip joints generally include investigating contacts happening among soft or hard tissues during hip movement. In many cases, hip movement is approximated as rotation about an estimated hip center. In this paper, we investigate the effect of different methods used for estimating hip joint center of rotation on the results acquired from hip simulation. For this reason, we use three dimensional models of hip tissues reconstructed from MRI datasets of 10 subjects, and estimate their center of rotation by applying five different methods (including both predictive and functional approaches). Then, we calculate the amount of angular and radial penetrations that happen among three dimensional meshes of cartilages, labrum, and femur bone, when hip is rotating about different estimated centers of rotation. The results indicate that hip simulation can be highly affected by the method used for estimating hip center of rotation. However, under some conditions (e.g. when Adduction or External Rotation are considered) we can expect to have a more robust simulation. In addition, it was observed that applying some methods (e.g. the predictive approach based on acetabulum) may result in less robust simulation, comparing to the other methods.

Enhancing pilot performance with a SymBodic system.

Increased fatigue of pilots during long flights can place both humans and machine at high risk. In this paper, we describe our research on a SymBodic (SYMbiotic BODies) system designed to minimize pilot fatigue in a simulated 48 hour mission. The system detected the pilot's sleep breaks and used this information to plan future sleep breaks. When fatigue could not be prevented, the SymBodic system assisted the pilot by providing relevant flight information through a vibro-tactile vest. Experiments showed that it was difficult for the pilot to adapt to the suggested sleep schedule within the duration of the experiment, and fatigue was not avoided. However, during periods of severe sleep deprivation, the SymBodic system significantly improved piloting performance.

Penetration depth method--novel real-time strategy for evaluating femoroacetabular impingement.

We introduce a new method for computerized real-time evaluation of femoroacetabular impingement (FAI). In contrast to previously presented stress analyses, this method is based on two types of predictions of penetration depths for two rotating bodies: curvilinear and radial penetration depth. This intuitive method allows the analysis of both bony and soft tissue structures (such as cartilage and acetabular labrum) in real time. Characteristic penetration depth patterns were found for different subtypes of FAI, such as cam and pincer pathologies. In addition, correlation between the penetration depths (estimated by applying this method) and the existing contact stresses (estimated by applying the finite element method) of various hip morphologies were found. A strong correlation with predicted stress values existed, with a mean correlation coefficient of 0.91 for the curvilinear and 0.80 for the radial penetration method. The results show that the penetration depth method is a promising, fast, and accurate method for quantification and diagnosis of FAI.

YaQ: an architecture for real-time navigation and rendering of varied crowds.

The YaQ software platform is a complete system dedicated to real-time crowd simulation and rendering. Fitting multiple application domains, such as video games and VR, YaQ aims to provide efficient algorithms to generate crowds comprising up to thousands of varied virtual humans navigating in large-scale, global environments.

Fast collision detection methods for joint surfaces.

In the recent years medical diagnosis and surgery planning often require the precise evaluation of joint movements. This has led to exploit reconstructed three-dimensional models of the joint tissues obtained from CT or MR Images (for bones, cartilages, etc.). In such context, efficiently and precisely detecting collisions among the virtual tissues is critical for guaranteeing the quality of any further analysis. The common methods of collision detection are usually designed for general purpose applications in computer graphics or CAD-CAM. Hence they face worst case scenarios when handling the quasi-perfect concavity-convexity matching of the articular surfaces. In this paper, we present two fast collision detection methods that take advantage of the relative proximity and the nature of the movement to discard unnecessary calculations. The proposed approaches also accurately provide the penetration depths along two functional directions, without any approximation. They are compared with other collision detection methods and tested in different biomedical scenarios related to the human hip joint.

A fast method for finding range of motion in the human joints.

Finding the range of motion for the human joints is a popular method for diagnosing joint diseases. By current technology, it is more trustable and easier to find the range of motion by employing computer based models of the human tissues. In this paper we propose a novel method for finding range of motion for human joints without using any collision detection algorithm. This method is based on mesh classifying in a cylindrically segmented space. The method shows to be much faster than the traditional ones and provides the accurate results. This method is illustrated on the case of finding the range of motion in the human hip joint.

Efficient collision detection within deforming spherical sliding contact.

Handling the evolving permanent contact of deformable objects leads to a collision detection problem of high computing cost. Situations in which this type of contact happens are becoming more and more present with the increasing complexity of virtual human models, especially for the emerging medical applications. In this context, we propose a novel collision detection approach to deal with situations in which soft structures are in constant but dynamic contact, which is typical of 3D biological elements. Our method proceeds in two stages: First, in a preprocessing stage, a mesh is chosen under certain conditions as a reference mesh and is spherically sampled. In the collision detection stage, the resulting table is exploited for each vertex of the other mesh to obtain, in constant time, its signed distance to the fixed mesh. The two working hypotheses for this approach to succeed are typical of the deforming anatomical systems we target: First, the two meshes retain a layered configuration with respect to a central point and, second, the fixed mesh tangential deformation is bounded by the spherical sampling resolution. Within this context, the proposed approach can handle large relative displacements, reorientations, and deformations of the mobile mesh. We illustrate our method in comparison with other techniques on a biomechanical model of the human hip joint.