A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-cost Motion Capture Technique.

Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) "skeleton" joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.

A Novel Helmet Fitness Evaluation Device Based on the Flexible Pressure Sensor Matrix.

Helmet comfort has always been important for the evaluation of infantry equipment accessories and has for decades not been well addressed. To evaluate the stability and comfort of the helmet, this paper proposes a novel type of helmet comfort measuring device. Conventional pressure measuring devices can measure the pressure of flat surfaces well, but they cannot accurately measure the pressure of curved structures with large curvatures. In this paper, a strain-resistive flexible sensor with a slice structure was used to form a matrix network containing more than a 100 sensors that fit the curved surface of the head well. Raw data were collected by the lower computer, and the original resistance value of the pressure was converted from analog to digital by the A/D conversion circuit that converts an analog signal into a digital signal. Then, the data were output to the data analysis and image display module of the upper computer. The complex curved surface of the head poses a challenge for the appropriate layout design of a head pressure measuring device. This study is expected to allow this intuitive and efficient technology to fit other wearable products, such as goggles, glasses, earphones and neck braces.

Delay-Aware Energy-Efficient Routing towards a Path-Fixed Mobile Sink in Industrial Wireless Sensor Networks.

Wireless sensor networks (WSNs) involve more mobile elements with their widespread development in industries. Exploiting mobility present in WSNs for data collection can effectively improve the network performance. However, when the sink (i.e., data collector) path is fixed and the movement is uncontrollable, existing schemes fail to guarantee delay requirements while achieving high energy efficiency. This paper proposes a delay-aware energy-efficient routing algorithm for WSNs with a path-fixed mobile sink, named DERM, which can strike a desirable balance between the delivery latency and energy conservation. We characterize the object of DERM as realizing the energy-optimal anycast to time-varying destination regions, and introduce a location-based forwarding technique tailored for this problem. To reduce the control overhead, a lightweight sink location calibration method is devised, which cooperates with the rough estimation based on the mobility pattern to determine the sink location. We also design a fault-tolerant mechanism called track routing to tackle location errors for ensuring reliable and on-time data delivery. We comprehensively evaluate DERM by comparing it with two canonical routing schemes and a baseline solution presented in this work. Extensive evaluation results demonstrate that DERM can provide considerable energy savings while meeting the delay constraint and maintaining a high delivery ratio.

Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks.

Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy forwarding. However, most of aggregation schemes reduce information accuracy and prolong end-to-end delay when eliminating transmission overhead. In this paper, we propose a data aggregation scheme based on overlapping rate of sensing area, namely AggOR, aiming for energy-efficient data collection in wireless sensor networks with high information accuracy. According to aggregation rules, gathering nodes are selected from candidate parent nodes and appropriate neighbor nodes considering a preset threshold of overlapping rate of sensing area. Therefore, the collected data in a gathering area are highly correlated, and a large amount of redundant data could be cleaned. Meanwhile, AggOR keeps the original entropy by only deleting the duplicated data. Experiment results show that compared with others, AggOR has a high data accuracy and a short end-to-end delay with a similar network lifetime.

Estimation of Human Body Volume (BV) from Anthropometric Measurements Based on Three-Dimensional (3D) Scan Technique.

BACKGROUND: This study aimed to develop estimation formulae for the total human body volume (BV) of adult males using anthropometric measurements based on a three-dimensional (3D) scanning technique. Noninvasive and reliable methods to predict the total BV from anthropometric measurements based on a 3D scan technique were addressed in detail. METHODS: A regression analysis of BV based on four key measurements was conducted for approximately 160 adult male subjects. Eight total models of human BV show that the predicted results fitted by the regression models were highly correlated with the actual BV (p < 0.001). RESULTS: Two metrics, the mean value of the absolute difference between the actual and predicted BV (V error) and the mean value of the ratio between V error and actual BV (RV error), were calculated. The linear model based on human weight was recommended as the most optimal due to its simplicity and high efficiency. CONCLUSIONS: The proposed estimation formulae are valuable for estimating total body volume in circumstances in which traditional underwater weighing or air displacement plethysmography is not applicable or accessible. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Secure and Efficient Two-Factor User Authentication Scheme with User Anonymity for Network Based E-Health Care Applications.

Benefited from the development of network and communication technologies, E-health care systems and telemedicine have got the fast development. By using the E-health care systems, patient can enjoy the remote medical service provided by the medical server. Medical data are important privacy information for patient, so it is an important issue to ensure the secure of transmitted medical data through public network. Authentication scheme can thwart unauthorized users from accessing services via insecure network environments, so user authentication with privacy protection is an important mechanism for the security of E-health care systems. Recently, based on three factors (password, biometric and smart card), an user authentication scheme for E-health care systems was been proposed by Amin et al., and they claimed that their scheme can withstand most of common attacks. Unfortunate, we find that their scheme cannot achieve the untraceability feature of the patient. Besides, their scheme lacks a password check mechanism such that it is inefficient to find the unauthorized login by the mistake of input a wrong password. Due to the same reason, their scheme is vulnerable to Denial of Service (DoS) attack if the patient updates the password mistakenly by using a wrong password. In order improve the security level of authentication scheme for E-health care application, a robust user authentication scheme with privacy protection is proposed for E-health care systems. Then, security prove of our scheme are analysed. Security and performance analyses show that our scheme is more powerful and secure for E-health care systems when compared with other related schemes.

A novel complex networks clustering algorithm based on the core influence of nodes.

In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property. Network clustering methods are thus significant for the study of complex networks. Currently, many typical clustering algorithms have some weakness like inaccuracy and slow convergence. In this paper, we propose a clustering algorithm by calculating the core influence of nodes. The clustering process is a simulation of the process of cluster formation in sociology. The algorithm detects the nodes with core influence through their betweenness centrality, and builds the cluster's core structure by discriminant functions. Next, the algorithm gets the final cluster structure after clustering the rest of the nodes in the network by optimizing method. Experiments on different datasets show that the clustering accuracy of this algorithm is superior to the classical clustering algorithm (Fast-Newman algorithm). It clusters faster and plays a positive role in revealing the real cluster structure of complex networks precisely.

Textual emotion classification using MPNet and cascading broad learning.

As one of the most important tasks of natural language processing, textual emotion classification (TEC) aims to recognize and detect all emotions contained in texts. However, most existing methods are implemented using deep learning approaches, which may suffer from long training time and low convergence. Motivated by these challenges, in this paper, we provide a new solution for TEC by using cascading broad learning (CBL) and sentence embedding using a masked and permuted pre-trained language model (MPNet), named CBLMP. Texts are input into MPNet to generate sentence embedding containing emotional semantic information. CBL is adopted to improve the ability of feature extraction in texts and to enhance model performance for general broad learning, by cascading feature nodes and cascading enhancement nodes, respectively. The L-curve model is adopted to ensure the balance between under-regularization and over-regularization for regularization parameter optimization. Extensive experiments have been carried out on datasets of SMP2020-EWECT and SemEval-2019 Task 3, and the results show that CBLMP outperforms the baseline methods in TEC.

Is low-cost motion capture with artificial intelligence applicable for human working posture risk assessment during manual material handling? A pilot study.

BACKGROUND: Assessing working posture risks is important for occupational safety and health. However, low-cost assessment techniques for human motion injuries in the logistics delivery industry have rarely been reported. OBJECTIVE: To propose a novel approach for posture risk assessment using low-cost motion capture with artificial intelligence. METHODS: A Kinect was adopted to obtain red-green-blue (RGB) and depth images of the subject with 24 postures, and the human joints were extracted using artificial intelligence. The images were registered to obtain the actual three-dimensional (3D) human joint angle. RESULTS: The root mean square error (RMSE) significantly decreased. Finally, two common methods for evaluating human working posture injuries-the Rapid Upper Limb Assessment and Ovako Working Posture Analysis System-were investigated. CONCLUSIONS: The outputs of the proposed method are consistent with those of the commercial ergonomic evaluation software.

DGANet: A Dual Global Attention Neural Network for Breast Lesion Detection in Ultrasound Images.

Deep learning-based breast lesion detection in ultrasound images has demonstrated great potential to provide objective suggestions for radiologists and improve their accuracy in diagnosing breast diseases. However, the lack of an effective feature enhancement approach limits the performance of deep learning models. Therefore, in this study, we propose a novel dual global attention neural network (DGANet) to improve the accuracy of breast lesion detection in ultrasound images. Specifically, we designed a bilateral spatial attention module and a global channel attention module to enhance features in spatial and channel dimensions, respectively. The bilateral spatial attention module enhances features by capturing supporting information in regions neighboring breast lesions and reducing integration of noise signal. The global channel attention module enhances features of important channels by weighted calculation, where the weights are decided by the learned interdependencies among all channels. To verify the performance of the DGANet, we conduct breast lesion detection experiments on our collected data set of 7040 ultrasound images and a public data set of breast ultrasound images. YOLOv3, RetinaNet, Faster R-CNN, YOLOv5, and YOLOX are used as comparison models. The results indicate that DGANet outperforms the comparison methods by 0.2%-5.9% in total mean average precision.

Do sleep and psychological factors influence musculoskeletal pain among nurses?

BACKGROUND: The physical factors associated with musculoskeletal pain in nursing personnel have been largely investigated, although the role of sleep and psychological factors resulting in musculoskeletal pain has not been addressed thoroughly. OBJECTIVE: This study aimed to explore the prevalence of musculoskeletal pain and investigate how sleep and psychological factors influence musculoskeletal pain in a nursing group. METHODS: Nordic standard questionnaires were distributed to 230 female nurses. Chi-square tests were performed to assess the associations between sleep problems, psychological problems, and musculoskeletal pain symptoms. Binary logistic regression analysis was also conducted to identify the primary factors influencing the prevalence of musculoskeletal pain. RESULTS: The highest prevalence of pain was observed in the lower back, neck, and shoulders, whereas the lowest prevalence of pain was observed in the ankles, feet, elbows, and hips/buttocks. Chi-square analysis and binary logistic regression showed that sleep duration, sleep onset time, and sleep quality all significantly contributed to the development of neck and upper back pain. With regard to the psychological factors, only occupational pride and stress had a significant effect on pain; in contrast, family support did not show any significant influence. CONCLUSION: Compared with other body regions, musculoskeletal pain in the lower back, neck, and shoulders requires more attention and preventive interventions. Special efforts should be made to shift the workday system of the nursing group because of the strong correlation between sleep problems and pain. Incentives other than penalty mechanisms should be considered seriously in nursing to boost occupational pride and relieve job stress.

Lung Segmentation Reconstruction Based Data Augmentation Approach for Abnormal Chest X-ray Images Diagnosis.

Experienced radiologists can accurately diagnose relevant diseases by observing the cardiopulmonary region in chest X-ray images. Advances in deep learning techniques enable the prediction of lesions in chest X-ray images. However, deep learning-based algorithms usually require a large amount of training data, and it lacks an effective method for data generation and augmentation. In this paper, we propose a Lung Segmentation Reconstruction (LSR) module. A healthy chest X-ray image is generated based on the abnormal image as a reference. With the generated healthy reference, we propose a novel way of data augmentation for chest X-ray images. The whole images, lung regions and abnormal regions are stacked together and fed into a classification model to make a credible diagnosis. Extensive experiments have been conducted on the public dataset CheXpert. Experimental results show that our proposed abnormality enhancement can help the baseline models achieve better performance on consolidation and pleural effusion. These results highlight the potential value of the large number of healthy chest X-ray images in the dataset and the combination of different regions of chest X-ray images for prediction.

Machine Learning-Based Evaluation on Craniodentofacial Morphological Harmony of Patients After Orthodontic Treatment.

Objectives: Machine learning is increasingly being used in the medical field. Based on machine learning models, the present study aims to improve the prediction performance of craniodentofacial morphological harmony judgment after orthodontic treatment and to determine the most significant factors. Methods: A dataset of 180 subjects was randomly selected from a large sample of 3,706 finished orthodontic cases from six top orthodontic treatment centers around China. Thirteen algorithms were used to predict the value of the cephalometric morphological harmony score of each subject and to search for the optimal model. Based on the feature importance ranking and by removing features, the regression models of machine learning (including the Adaboost, ExtraTree, XGBoost, and linear regression models) were used to predict and compare the score of harmony for each subject from the dataset with cross validations. By analyzing the prediction values, the most optimal model and the most significant cephalometric characteristics were determined. Results: When nine features were included, the performance of the XGBoost regression model was MAE = 0.267, RMSE = 0.341, and Pearson correlation coefficient = 0.683, which indicated that the XGBoost regression model exhibited the best fitting and predicting performance for craniodentofacial morphological harmony judgment. Nine cephalometric features including L1/NB (inclination of the lower central incisors), ANB (sagittal position between the maxilla and mandible), LL-EP (distance from the point of the prominence of the lower lip to the aesthetic plane), SN/OP (inclination of the occlusal plane), SNB (sagittal position of the mandible in relation to the cranial base), U1/SN (inclination of the upper incisors to the cranial base), L1-NB (protrusion of the lower central incisors), Ns-Prn-Pos (nasal protrusion), and U1/L1 (relationship between the protrusions of the upper and lower central incisors) were revealed to significantly influence the judgment. Conclusion: The application of the XGBoost regression model enhanced the predictive ability regarding the craniodentofacial morphological harmony evaluation by experts after orthodontic treatment. Teeth position, teeth alignment, jaw position, and soft tissue morphology would be the most significant factors influencing the judgment. The methodology also provided guidance for the application of machine learning models to resolve medical problems characterized by limited sample size.

Focal U-Net: A Focal Self-attention based U-Net for Breast Lesion Segmentation in Ultrasound Images.

Accurate breast lesion segmentation in ultrasound images helps radiologists to make exact diagnoses and treatments, which is important to increase the survival rate of breast cancer patients. Recently, deep learning-based methods have demonstrated remarkable results in breast lesion segmentation. However, the blurry breast lesion boundaries and noise artifacts in ultrasound images still limit the performance of the deep learning-based methods. In this paper, we propose a novel segmentation network equipped with a focal self-attention block for improving the performance of breast lesion segmentation. The focal self-attention block can incorporate fine-grained local and coarse-grained global information. The fine-grained local information is useful to enhance features of breast lesion boundaries, while the coarse-grained global information effectively reduces noise interference. To verify the performance of our network, we implement breast lesion segmentation on our collected dataset of 9836 ultrasound images. The results demonstrate that the focal self-attention block enhances features of breast lesion boundaries and improves the accuracy of breast lesion segmentation.

The driver's instantaneous situation awareness when the alarm rings during the take-over of vehicle control in automated driving.

OBJECTIVE: The driver's instantaneous situation awareness in the process of take-over of vehicle control in automated driving has not yet been thoroughly investigated. The proposed research can provide a better understanding of the driver's perceived characteristics and identify the most urgent information requirements of the on-site scenario when the driver's eye sight returns from other distractors to the driving scene. METHODS: We conducted an experiment in simulated automated driving to study the participants' ability of instantaneous hazard perception and judgment. The scene pictures, which were displayed in millisecond time, were used to imitate the situations that drivers would see when the distracted drivers returned their gaze from the distractive sources to the road in the simulated automated driving. RESULTS: The results show that the driving state, scene representation time and hazard levels affect the instantaneous situation awareness of drivers. In addition, the scene perception accuracy of the group who played games during automated driving is much lower than that of the group that chatted with the copilot. The longer picture-showing duration decreases the accuracy of hazard identification, whereas the shorter picture-showing duration increases the accuracy of hazard perception and the hazard rating score. CONCLUSIONS: In conclusion, distraction reduces the accuracy of the instantaneous scene perception of drivers, and drivers behave more cautiously in decision making when the driving situations are more hazardous. This study provides a good theoretical basis for the design of hazard warning information for automated driving.

Mangrove restoration promotes the anti-scouribility of the sediments by modifying inherent microbial community and extracellular polymeric substance.

Coastal erosion will aggravate the loss of shorelines and threaten the safety of coastal engineering facilities. Mangrove is often considered as an efficient coastal guard; however the mechanisms involved in anti-scouribility ascribed to mangrove are still poorly understood. Thus, two artificial mangrove forests (including exotic Sonneratia apetala and native Kandelia obovata) and an unvegetated mudflat control were selected to explore the potential function of microbial extracellular polymeric substance (EPS) on the anti-scouribility of the sediments. A cohesive strength meter was used for the analysis of anti-scouribility, while a sequential extraction and 16S high-throughput sequencing were employed to evaluate the changes in EPS and microbial community driven by mangrove restoration. Principal component, redundancy, and two-matrix correlation heatmap analyses were performed for the analyses of the correlations among shear stress, EPS, microbes, and soil properties. The results showed an obvious enhancement of anti-scouribility after mangrove restoration. Compared to those of unvegetated mudflat, shear stress increased from 1.94 N/m2 to 3.26 and 4.93 N/m2 in the sediments of S. apetala and K. obovata stands, respectively. Mangrove restoration also promoted EPS content in the sediments, irrespective of EPS components and sub-fractions. Both extracellular protein and polysaccharide were found to be positively correlated with anti-scouribility. Coinciding with increased anti-scouribility and EPS, increased bacterial abundances were also detected in the sediments after mangrove restoration (especially K. obovata), whereas Proteobacteria and Bacteroides may be important and influential for EPS secretion and anti-scouribility promotion. Nevertheless, increased total organic carbon, total nitrogen and total phosphorus induced by mangrove restoration may also partially contribute to improvement of anti-scouribility. In conclusion, this is the first study to provide evidence for a link between mangrove restoration and increased EPS which improve resistance to scouring. The present study provides a novel perspective on the revealing of the function of mangrove on erosion mitigation.

A survey on incorporating domain knowledge into deep learning for medical image analysis.

Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To address this problem, researchers have started looking for external information beyond current available medical datasets. Traditional approaches generally leverage the information from natural images via transfer learning. More recent works utilize the domain knowledge from medical doctors, to create networks that resemble how medical doctors are trained, mimic their diagnostic patterns, or focus on the features or areas they pay particular attention to. In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection, lesion and organ segmentation. For each task, we systematically categorize different kinds of medical domain knowledge that have been utilized and their corresponding integrating methods. We also provide current challenges and directions for future research.

Domain Knowledge Powered Deep Learning for Breast Cancer Diagnosis Based on Contrast-Enhanced Ultrasound Videos.

In recent years, deep learning has been widely used in breast cancer diagnosis, and many high-performance models have emerged. However, most of the existing deep learning models are mainly based on static breast ultrasound (US) images. In actual diagnostic process, contrast-enhanced ultrasound (CEUS) is a commonly used technique by radiologists. Compared with static breast US images, CEUS videos can provide more detailed blood supply information of tumors, and therefore can help radiologists make a more accurate diagnosis. In this paper, we propose a novel diagnosis model based on CEUS videos. The backbone of the model is a 3D convolutional neural network. More specifically, we notice that radiologists generally follow two specific patterns when browsing CEUS videos. One pattern is that they focus on specific time slots, and the other is that they pay attention to the differences between the CEUS frames and the corresponding US images. To incorporate these two patterns into our deep learning model, we design a domain-knowledge-guided temporal attention module and a channel attention module. We validate our model on our Breast-CEUS dataset composed of 221 cases. The result shows that our model can achieve a sensitivity of 97.2% and an accuracy of 86.3%. In particular, the incorporation of domain knowledge leads to a 3.5% improvement in sensitivity and a 6.0% improvement in specificity. Finally, we also prove the validity of two domain knowledge modules in the 3D convolutional neural network (C3D) and the 3D ResNet (R3D).

Is music a mediator impacting car following when driver's personalities are considered.

Music can influence car following performance. However, it is not well resolved about its mediation effect on car following when the drivers' personalities are considered. We investigated how music style and tempo influence car following with different personalities. Twelve tracks were used in this study, four for each music tempo range, i.e., slow, medium, and fast tempo, and six for each music style, i.e., classical and pop one. The results showed introverts were more susceptible to music, and tend to listen to slow tempo music and classical one. In addition, pop music aroused the drivers more than classical and may induce closer headway distance. Furthermore, with the tempo speeding up, the drivers were more excited, less concentrated and performed less stablely. The medium music tempo was the most appropriate choice for keeping stable headway distance and taking actions to the changes of the leading vehicle. The present study shows personality can mediate the influence of music listening while driving, and music style and tempo can impact the mediation in a specific way. The study provides a guide on the music choice during driving and may bring benefits to the configuration of the music radio program and car music player.

The Influence of Target Layout and Clicking Method on Picking Time and Dragging Performance Based on Eye-Control Technique.

Eye-tracking has been a hot topic in human-computer interaction (HCI). Nevertheless, previous studies usually adopted eye-tracking as information output rather than input. The eye-control technique can achieve convenient and rapid real-time operation through the movement of the eyes and reduce unnecessary manual operations. Because the layout determines the location orientation, organizational complexity, cognitive consistency, and predictive ability of the information display, the interface layout design affects the user's perception of information intensity, complexity, and logic. Moreover, the method of target clicking by eye-control techniques, which include blink and dwell, also depends on the application and user's ability. The purpose of this study is to investigate the influence of target layout and target picking method on picking time and dragging performance based on eye-control technique. The results indicate that the target picking method, i.e., blink or dwell, had significant effects on the dragging time and dragging numbers. However, there was no significant effect of target layout on picking time and dragging performance (dragging time and numbers), which may be related to the setting of the experimental conditions (e.g., lighting level and screen resolution). Moreover, the target picking method and the target layout had no significant interaction effect on picking time and dragging performance. The findings are anticipated to provide helpful implications for future eye control technique design.