Solvent-regulated self-assembled carbon nitride for photocatalytic reduction of U(VI) in water.

Manufacturing high-performance and reusable materials from radioactive uranium-containing wastewater remains a significant challenge. Herein, a supramolecular self-assembly strategy was proposed, using melamine and cyanuric acid as precursors and using intermolecular hydrogen bond force to form carbon nitride (CN-D) in different solvents through a single thermal polymerization strategy. Supramolecular self-assembly method is a promising strategy to synthesize a novel carbon nitride with molecular regulatory properties. In addition, 98% of U(VI) in wastewater can be removed by using CN-D for 60 min under visible light. After five cycles of recycling, more than 95% of U(VI) can still be reduced, indicating that it has good recyclability and reusability. This study not only provides an efficient photocatalytic method of uranium reduction, but also provides a new method for self-assembly synthesis.

A hand motion capture method based on infrared thermography for measuring fine motor skills in biomedicine.

Many biomedical applications require fine motor skill assessments; however, real-time and contactless fine motor skill assessments are not typically implemented. In this study, we followed the 2D-to-3D pipeline principle and proposed a transformer-based spatial-temporal network to accurately regress 3D hand joint locations by inputting infrared thermal video for eliminating need of multiple cameras or RGB-D devices. We also developed a dataset composed of infrared thermal videos and ground truth annotations for training. The label represents a set of 3D joint locations from infrared optical trackers, which is considered the gold standard for clinical applications. To demonstrate their potential, the proposed method was used to measure the finger motion angle, and we investigated its accuracy by comparing the proposal with the Azure Kinect system and Leap Motion system. On the proposed dataset, the proposed method achieved a 3D hand pose mean error of less than 14 mm and outperforms the other deep learning methods. When the error thresholds were larger than approximately 35 mm, our method first to achieved excellent performance (>80%) in terms of the fraction of good frames. For the finger motion angle calculation task, the proposed and commercial systems had comparable inter-system reliability (ICC2,1 ranging from 0.81 to 0.83) and excellent validity (Pearson's r-values ranging from 0.82 to 0.86). We believe that the proposed approaches can capture hand motion and measure finger motion angles and can be used in different biomedicine scenarios as an effective evaluation tool for fine motor skills.

MIL-100(Fe)/g-C3N4 composites with enhanced photocatalytic activity for UO22+ reduction under visible light.

As a clean energy source, nuclear energy can gradually replace traditional fossil energy sources, and is an important means to achieve the "double carbon goal". Uranium-containing wastewater is inevitable in the development of nuclear energy. The composites MIL/CNx of MOF material MIL-100(Fe) and carbon nitride (CN) were obtained by a simple solvo-thermal method using iron nitrate, homophthalic acid and CN. The material MIL-100(Fe) with high specific surface area was compounded with CN to increase the in-plane adsorption sites, which could adsorb 30% of uranium in solution during the dark reaction. The close interfacial contact of the two materials effectively inhibited the complexation of photo-generated electrons and holes and promotes electron migration. These two synergistic effects improved their overall photocatalytic reduction capacity, which could reduce 97% of UO22+ in solution in 20 min. The UO22+ removal efficiency of MIL/CN0.1 was 2.3 and 1.6 times higher than that of CN and MIL-100(Fe), respectively. In addition, MIL/CN0.1 was stable in reducing uranium during the five cycles of the experiment.

Dual-channel cascade pose estimation network trained on infrared thermal image and groundtruth annotation for real-time gait measurement.

Real-time spatiotemporal parameter measurement for gait analysis is challenging. Previous techniques for 3D motion analysis, such as inertial measurement units, marker based motion analysis or the use of depth cameras, require expensive equipment, highly skilled staff and limits feasibility for sustainable applications. In this paper a dual-channel cascaded network to perform contactless real-time 3D human pose estimation using a single infrared thermal video as an input is proposed. An algorithm to calculate gait spatiotemporal parameters is presented by tracking estimated joint locations. Additionally, a training dataset composed of infrared thermal images and groundtruth annotations has been developed. The annotation represents a set of 3D joint locations from infrared optical trackers, which is considered to be the gold standard in clinical applications. On the proposed dataset, our pose estimation framework achieved a 3D human pose mean error of below 21 mm and outperforms state-of-the-art methods. The results reveal that the proposed system achieves competitive skeleton tracking performance on par with the other motion capture devices and exhibited good agreement with a marker-based three-dimensional motion analysis system (3DMA) over a range of spatiotemporal parameters. Moreover, the process is shown to distinguish differences in over-ground gait parameters of older adults with and without Hemiplegia's disease. We believe that the proposed approaches can measure selected spatiotemporal gait parameters and could be effectively used in clinical or home settings.

A contactless method to measure real-time finger motion using depth-based pose estimation.

BACKGROUND: Finger mobility plays a crucial role in everyday living and is a leading indicator during hand rehabilitation and assistance tasks. Depth-based hand pose estimation is a potentially low-cost solution for the clinical and home-based measurement of symptoms of limited human finger motion. OBJECTIVE: The purpose of this study was to achieve the contactless measurement of finger motion based on depth-based hand pose estimation using Azure Kinect depth cameras and transfer learning, and to evaluate the accuracy in comparison with a three-dimensional motion analysis (3DMA) system. METHODS: Thirty participants performed a series of tasks during which their hand motions were measured concurrently using the Azure Kinect and 3DMA systems. We propose a simple and effective approach to achieving real-time hand pose estimations from single depth images using ensemble convolutional neural networks trained by a transfer learning strategy. Algorithms to calculate the finger joint motion angles are presented by tracking the locations of the 24 hand joints. To demonstrate their potential, the Azure-Kinect-based 3D finger motion measurement system and algorithms are experimentally verified through comparison with a camera-based 3DMA system, which is the gold standard. RESULTS: Our results revealed that the Azure-Kinect-based hand pose estimation system produced highly correlated measurements of hand joint coordinates. Our method achieved excellent performance in terms of the fraction of good frames ( >80%) when the error thresholds were larger than approximately 2 cm, and the range of mean error distance was 0.23--1.05 cm. For joint angles, the Azure Kinect and 3DMA systems had comparable inter-trial reliability (ICC2,1 ranging from 0.89 to 0.97) and excellent concurrent validity, with Pearsons r-values >0.90 for most measurements (range: 0.88--0.97). The 95% BlandAltman limits of agreement were narrow enough for the Azure Kinect to be considered a valid tool for the measurement of all reported joint angles of the index finger and thumb in pinching. Moreover, our method runs in real time at over 45 fps. CONCLUSION: The results of this study suggest that the proposed method has the capacity to measure the performance of fine motor skills.

Extraction and selection of gait recognition features using skeleton point detection and improved fuzzy decision.

It is of great importance to effectively measure gait features and recognize the signature gait patterns for gait rehabilitation. In this work, we used a skeleton point detection to extract gait features and proposed an improved fuzzy decision to select the most significant features for classifying gait patterns. Thirteen gait recognition features were extracted from the obtained skeleton points data. Taking the extracted features as an input, our improved fuzzy similarity priority decision method has obtained important sequences of all features based on the relatively important scores. Then, the ranked features were delivered in different classifiers by a sequential forward selection strategy to select the optimal feature subset. There were significant differences between groups in each of the thirteen gait recognition features (p < 0.005), indicating that all extracted features are potential influence factors for classifying gait patterns. We also found that the highest classification accuracy of 100% for gait feature subsets included the stride frequency, maximum flexion angle of knee, and toe-out angle, on the all classifiers. The results suggest that the proposed approaches are very useful in searching for the optimal feature subset in present dataset.

Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction.

Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. First, we train a Random Forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the Random Forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.

Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras.

Hemiplegia is a form of paralysis that typically has the symptom of dysbasia. In current clinical rehabilitations, to measure the level of hemiplegia gaits, clinicians often conduct subject evaluations through observations, which is unreliable and inaccurate. The Microsoft Kinect sensor (MS Kinect) is a widely used, low-cost depth sensor that can be used to detect human behaviors in real time. The purpose of this study is to investigate the usage of the Kinect data for the classification and analysis of hemiplegia gait. We first acquire the gait data by using a MS Kinect and extract a set of gait features including the stride length, gait speed, left/right moving distances, and up/down moving distances. With the gait data of 60 subjects including 20 hemiplegia patients and 40 healthy subjects, we employ a random forest-based classification approach to analyze the importances of different gait features for hemiplegia classification. Thanks to the over-fitting avoidance nature of the random forest approach, we do not need to have a careful control over the percentage of patients in the training data. In our experiments, our approach obtained the averaged classification accuracy of 90.65% among all the combinations of the gait features, which substantially outperformed state-of-the-art methods. The best classification accuracy of our approach is 95.45%, which is superior than all existing methods. Additionally, our approach also correctly reveals the importance of different gait features for hemiplegia classification. Our random forest-based approach outperforms support vector machine-based method and the Bayesian-based method, and can effectively extract gait features of subjects with hemiplegia for the classification and analysis of hemiplegia. Graphical Abstract Random Forest based Classsification and Analysis of Hemiplegia Gait using Low-cost Depth Cameras. Left: Motion capture with MS Kinect; Top-right: Random Forest Classsification based on the extracted gait features; Bottom-right: Sensitivity and specificity evaluation of the proposed classification approach.

[Automatic recognition and analysis of hemiplegia gait].

In this paper, the research has been conducted by the Microsoft kinect for windows v2 for obtaining the walking trajectory data from hemiplegic patients, based on which we achieved automatic identification of the hemiplegic gait and sorted the significance of identified features. First of all, the experimental group and two control groups were set up in the study. The three groups of subjects respectively completed the prescribed standard movements according to the requirements. The walking track data of the subjects were obtained straightaway by Kinect, from which the gait identification features were extracted: the moving range of pace, stride and center of mass (up and down/left and right). Then, the bayesian classification algorithm was utilized to classify the sample set of these features so as to automatically recognize the hemiplegia gait. Finally, the random forest algorithm was used to identify the significance of each feature, providing references for the diagnose of disease by ranking the importance of each feature. This thesis states that the accuracy of classification approach based on bayesian algorithm reaches 96%; the sequence of significance based on the random forest algorithm is step speed, stride, left-right moving distance of the center of mass, and up-down moving distance of the center of mass. The combination of step speed and stride, and the combination of step speed and center of mass moving distance are important reference for analyzing and diagnosing of the hemiplegia gait. The results may provide creative mind and new references for the intelligent diagnosis of hemiplegia gait.

Coordination-driven synthesis of perfected π-conjugated graphitic carbon nitride with efficient charge transfer for oxygen activation and gas purification.

Efficient yield of reactive-oxygen species (ROS) is greatly important for environmental purification and engineering. In this study, the perfected π-conjugated g-C3N4 (PNa-g-C3N4) photocatalysts were constructed by coordination between 3p orbits of Na and N 2p lone electron at vacancy structure of tri-s-triazine polymer for ROS evolution and elimination of HCHO and NO. The perfected π-conjugated structure enhances the visible-light capturing capability, enriches active sites for O2 activation, and promotes the directional charge transfer from N 2p of C3-N to Na and C. Therefore, the superior activities including the evolution of O2- (35 µmol.L-1h-1), and H2O2 (517 µmol.L-1h-1) have been achieved over PNa-g-C3N4 photocatalyst. As a result, PNa-g-C3N4 photocatalysts demonstrate high performances removal efficiency of NO (53% for 6 min), and HCHO (almost 100% for 55 min) in the elimination process. The results may provide the promising strategy to construct efficient photocatalytic system to yield ROS for environmental purification.