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Patients with motor impairments (e.g., stroke, bone fracture, Parkinson's) are sensitive to the wearing experience of rehabilitation devices, and they often have difficulty accurately positioning them at an accurate position. While solutions involving optical systems or IMUs could potentially help alleviate the issue, they often introduce other challenges such as privacy concerns or discomforting experiences. With the emergence of wearable soft sensors during the last few decades, researchers widely apply soft sensors in rehabilitation to improve the wearing experience. However, these approaches have primarily focused on analyzing the sensor readings to improve accuracy rather than addressing the needs of patients and healthcare providers, and there is a lack of comprehensive design investigation and need assessment based on soft sensor-based rehabilitation systems for motor-impaired patients and their doctors. In this study, we developed an application, RehabFAB, utilizing fabric sensors for rehabilitation purposes. Besides, we evaluated our application and device and investigated the needs of patients and doctors for potential home rehabilitation applications. The investigation was conducted through thematic analysis, correlation analysis and System Usability Scale. The experimental results validated the efficacy, reliability and usability of our approach, with a SUS score of 81.75. In addition, the RehabFAB meets the expectations of motor-impaired patients and medical professionals as a home rehabilitation tool. Our core contributions lie in a thorough evaluation of the needs of motor-impaired patients in order to design a stable and reliable motion-tracking device based on soft sensors for their recovery. [ABSTRACT FROM AUTHOR]

Despite progress in the past decades, 3D shape acquisition techniques are still a threshold for various 3D face-based applications and have therefore attracted extensive research. Moreover, advanced 2D data generation models based on deep networks may not be directly applicable to 3D objects because of the different dimensionality of 2D and 3D data. In this work, we propose two novel sampling methods to represent 3D faces as matrix-like structured data that can better fit deep networks, namely (1) a geometric sampling method for the structured representation of 3D faces based on the intersection of iso-geodesic curves and radial curves, and (2) a depth-like map sampling method using the average depth of grid cells on the front surface. The above sampling methods can bridge the gap between unstructured 3D face models and powerful deep networks for an unsupervised generative 3D face model. In particular, the above approaches can obtain the structured representation of 3D faces, which enables us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation to obtain better 3D faces with different expressions. We demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different expressions using the two novel down-sampling methods mentioned above. [ABSTRACT FROM AUTHOR]

Extracting gait biometrics from videos has been receiving rocketing attention given its applications, such as person re-identification. Although deep learning arises as a promising solution to improve the accuracy of most gait recognition algorithms, the lack of enough training data becomes a bottleneck. One of the solutions to address data deficiency is to generate synthetic data. However, gait data synthesis is particularly challenging as the inter-subject and intra-subject variations of walking style need to be carefully balanced. In this paper, we propose a complete 3D framework to synthesize unlimited, realistic, and diverse motion data. In addition to walking speed and lighting conditions, we emphasize two key factors: 3D gait motion style and character appearance. Benefiting from its 3D nature, our system can provide various gait-related data, such as accelerometer data and depth map, not limited to silhouettes. We conducted various experiments using the off-the-shelf gait recognition algorithm and draw the following conclusions: (1) the real-to-virtual gap can be closed when adding a small portion of real-world data to a synthetically trained recognizer; (2) the amount of real training data needed to train competitive gait recognition systems can be reduced significantly; (3) the rich variations in gait data are helpful for investigating algorithm performance under different conditions. The synthetic data generator, as well as all experiments, will be made publicly available. [ABSTRACT FROM AUTHOR]

Color design for 3D indoor scenes is a challenging problem due to many factors that need to be balanced. Although learning from images is a commonly adopted strategy, this strategy may be more suitable for natural scenes in which objects tend to have relatively fixed colors. For interior scenes consisting mostly of man-made objects, creative yet reasonable color assignments are expected. We propose $C^{3}$ C 3 Assignment, a system providing diverse suggestions for interior color design while satisfying general global and local rules including color compatibility, color mood, contrast, and user preference. We extend these constraints from the image domain to $\mathbb {R}^3$ R 3 , and formulate 3D interior color design as an optimization problem. The design is accomplished in an omnidirectional manner to ensure a comfortable experience when the inhabitant observes the interior scene from possible positions and directions. We design a surrogate-assisted evolutionary algorithm to efficiently solve the highly nonlinear optimization problem for interactive applications, and investigate the system performance concerning problem complexity, solver convergence, and suggestion diversity. Preliminary user studies have been conducted to validate the rule extension from 2D to 3D and to verify system usability. [ABSTRACT FROM AUTHOR]

Analyzing human muscle states has attracted extensive attention. EMG (electromyography) pattern recognition methods based on these works have been proposed for many years. However, uncomfortable wearing and high prices make it inconvenient for motion tracking and muscle analysis by using robotic arms and inertial sensors in daily life. In this study, we propose to use smart clothes integrated with flexible sensors to collect arm motion data, estimate the kinematic information of continuous arm motion, and predict the EMG signal of each arm muscle. Firstly, the neural network regression model integrated with the LSTM (long short-term memory) module is used to continuously estimate the sensor resistances collected by the smart clothes and the angles collected by Kinect. Then, six types of shoulder and elbow movements' angles and the corresponding EMG signals of 5 subjects are preprocessed and aligned. The stacked regression model based on extremely randomized trees (extra trees) is used for regression. Our experimental results show that the average estimation absolute error from the sensor resistances to the joint angle is 3.45 degrees, and the absolute percentage error from the joint angle to the EMG signal is only 1.82%. [ABSTRACT FROM AUTHOR]

Land use information is the basis of various geo‐spatial applications. Traditionally, land use patterns are predicted with agent‐based simulation, suffering from a long convergence process. Deep learning techniques have recently been used for land use classification but not prediction, due to the lack and difficulty of collecting enough training data. This paper proposes a novel paradigm for land use data generation with a randomized simulation strategy. We also design a tailored deep land use prediction model, LUPnet, to demonstrate the usage of the paradigm. Experimental results reveal the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such aggregation processes can lead to perturbations in the network inputs. This issue can significantly destabilize the feature embeddings and the predictions - rendering deep networks much less useful for the experts. This paper approaches this challenge by leveraging unit visualization techniques that enable the investigation of many-to-many relationships between dynamically varied multi-scalar aggregations of urban traffic data and neural network predictions. Through regular exchanges with a domain expert, we design and develop a visual analytics solution that integrates 1) a Bivariate Map equipped with an advanced bivariate colormap to simultaneously depict input traffic and prediction errors across space, 2) a Moran's I Scatterplot that provides local indicators of spatial association analysis, and 3) a Multi-scale Attribution View that arranges non-linear dot plots in a tree layout to promote model analysis and comparison across scales. We evaluate our approach through a series of case studies involving a real-world dataset of Shenzhen taxi trips, and through interviews with domain experts. We observe that geographical scale variations have important impact on prediction performances, and interactive visual exploration of dynamically varying inputs and outputs benefit experts in the development of deep traffic prediction models. [ABSTRACT FROM AUTHOR]

Several applications in shape modeling and exploration require identification and extraction of a 3D shape part matching a 2D sketch. We present CustomCut, an on-demand part extraction algorithm. Given a sketched query, CustomCut automatically retrieves partially matching shapes from a database, identifies the region optimally matching the query in each shape, and extracts this region to produce a customized part that can be used in various modeling applications. In contrast to earlier work on sketch-based retrieval of predefined parts, our approach can extract arbitrary parts from input shapes and does not rely on a prior segmentation into semantic components. The method is based on a novel data structure for fast retrieval of partial matches: the randomized compound k-NN graph built on multi-view shape projections. We also employ a coarse-to-fine strategy to progressively refine part boundaries down to the level of individual faces. Experimental results indicate that our approach provides an intuitive and easy means to extract customized parts from a shape database, and significantly expands the design space for the user. We demonstrate several applications of our method to shape design and exploration. [ABSTRACT FROM AUTHOR]

A multimodel based range query processing algorithm is proposed to solve the information collection task for the CPSs, which utilizes multiple probability models to depict the data distribution of a sensor node. The execution of the multimodel based algorithm consists of two phases, which are the preprocessing phase and the query processing phase. During the preprocessing phase, multiple models are constructed for each node according to their historical data. During the query processing phase, a suitable model is selected from the multiple models with the help of a sampling based algorithm, which is used to process the query. As the multimodel based algorithm needs to sample data from the network, it can waste energy more than that of the single model based algorithm in some cases, which does not sample data from the network. The cost of the multimodel based and single model based algorithm is analyzed. A cost model based algorithm is proposed to select a better algorithm to process a query from the two algorithms. Experimental results show that the cost model based algorithm can save 13.3% energy consumption more than that of the single model based algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Releasing sky lanterns is a popular way of celebrating festivals and ceremonies in the Asian countries. This paper presents a computer-aided approach to help novice users to design flyable sky lantern with desired shape. Given a closed up-right 3D model with a user-specified cutting on the bottom, our system optimizes the shape by regularizing the boundary, smoothing the geometry and improving the volume-to-area ratio to make it feasible for flying. The optimized shape is then approximated by a set of developable patches. Next, through a physical analysis step that tests the flying condition and determines the optimal size, the approximated shape is flattened into 2D patches, which can be printed out and glued together to form the airbag. Finally, the user can attach the airbag to a bamboo frame and assemble the fuel cell. We successfully apply our prototype system to design and construct real sky lanterns. [Copyright &y& Elsevier]

Character pose design is one of the most fundamental processes in computer graphics authoring. Although there are many research efforts in this field, most existing design tools consider only character body structure, rather than its interaction with the environment. This paper presents an intuitive sketching interface that allows the user to interactively place a 3D human character in a sitting position on a chair. Within our framework, the user sketches the target pose as a 2D stick figure and attaches the selected joints to the environment (e.g., the feet on the ground) with a pin tool. As reconstructing the 3D pose from a 2D stick figure is an ill-posed problem due to many possible solutions, the key idea in our paper is to reduce solution space by considering the interaction between the character and environment and adding physics constraints, such as balance and collision. Further, we formulated this reconstruction into a nonlinear optimization problem and solved it via the genetic algorithm (GA) and the quasi-Newton solver. With the GPU implementation, our system is able to generate the physically correct and visually pleasing pose at an interactive speed. The promising experimental results and user study demonstrates the efficacy of our method. [ABSTRACT FROM AUTHOR]