Characteristics Of Human Body Research Articles

Human action recognition using a depth sequence key-frames based on discriminative collaborative representation classifier for healthcare analytics

Using deep map sequence to recognize human action is an important research field in computer vision. The traditional deep map-based methods have a lot of redundant information. Therefore, this paper proposes a new deep map sequence feature expression method based on discriminative collaborative representation classifier, which highlights the time sequence of human action features. In this paper, the energy field is established according to the shape and action characteristics of human body to obtain the energy information of human body. Then the energy information is projected onto three orthogonal axes to obtain deep spatialtemporal energy map. Meanwhile, in order to solve the problem of high misclassification probability of similar samples by collaborative representation classifier (CRC), a discriminative CRC (DCRC) is proposed. The classifier takes into account the influence of all training samples and each kind of samples on the collaborative representation coefficient, it obtains the highly discriminative collaborative representation coefficient, and improves the discriminability of similar samples. Experimental results on MSR Action3D data set show that the redundancy of key-frame algorithm is reduced, and the operation efficiency of each algorithm is improved by 20%-30%. The proposed algorithm in this paper reduces the redundant information in deep map sequence and improves the extraction rate of feature map. It not only preserves the spatial information of human action through the energy field, but also records the temporal information of human action in a complete way. What?s more, it still maintains a high recognition accuracy in the action data with temporal information.

Application of Functional Training in Sports Dance Training.

With the rapid development of the economy, people's various needs for daily life are also increasing. Because this sport combines sports and dance, which makes the sport have the effect of physical fitness, as well as the emotional edification and visual beauty that dance brings to dancers and audiences. With the increase in people's needs, at the same time, corresponding to the relatively insufficient functional training of the project, this problem makes sports dance, the international standard ballroom dance, always at a low level of development, and at the same time, makes it lack of competitiveness. Also, the nature of dance is now more of a viewing experience. In view of the lack of functional training in international standard ballroom dancing, this article will study the role of functional training in this. By adopting the technology of human body feature recognition and functional action screening, it implements the steps of human action recognition detection technology, human action tracking technology, human body posture shape recognition method, and functional action screening. And the final experimental results show that functional training can screen out 57.3% of female dancers, with ligament damage, and 52.1% of male dancers. The screen outs were more than half of the total numbers, which shows that functional training effectively improves international standard ballroom dance that can provide good assistance for dancers' training.

Occluded Person Re-Identification Method Based on Multiscale Features and Human Feature Reconstruction

Occluded Re-ID task is proposed mainly because people are often occluded by various obstacles in the real world, which greatly affects the accuracy of model matching.In view of the challenge of occluded Re-ID, the main work of this paper is as follows :(i) Aiming at the incompleteness of human body under occlusion, an occluded Re-ID method based on multi-scale features is proposed. A partial human body locator is constructed by using the target detection algorithm to automatically recognize and cut partial human body in this method. Then this method designs a horizontal pyramid pooling strategy to extract multi-scale features and enhance the robustness of the model under the occlusion problem. Experiments show that, this method has better matching accuracy in the occluded Re-ID task. (ii) Aiming at the problem that it is difficult to align the local features between different people images under occlusion, an occluded Re-ID method based on human feature reconstruction is proposed. This method is an unaligned method, which uses sparse representation to reconstruct human body features. Difficult sample triplet loss function was improved by using human feature reconstruction distance and the proportion of similar parts to matching correlation was increased. Experiments show that this method can effectively improve the occlusion resistance of the model.

A Novel Multiple Person Pose Estimation Optimization Model Utilizing Genetic Algorithm

For traditional human pose estimation models rely on a large amount of human body feature information, this paper proposes an optimization model using genetic algorithm to solve the problem of multiple person body part assembly. Different from other human body parts assembly method. The method proposed in this paper depends on the joints position information, namely the sum of the connection distances between the joints as the objective function, and finds the optimal value to obtain the best human pose assembly information. The simulation results show that compared with the traditional OpenPose model, the model proposed in this paper can obtain the same human skeleton using less position information.

Design and implementation of fake two-piece fully formed process for blouse

PurposeFully formed knitting technology is a cutting-edge technology in the design and production of knitted apparel. Using this technology and its supporting design system, a new development mode of fully formed knitted apparel with double-layer structure and fake two-piece knitwear is proposed.Design/methodology/approachBased on the upper body structure feature points of human body characteristics and single-layer knitted garment prototype, a double-layer structure knitted garment pattern was established by pattern expansion method. The model was introduced into SDS-APPEX3 design system for process design, including three aspects consists: the inner vest, the outer blouse and double-layer joint part, analysis of the process and forming principle. Weaving on four-needle bed computerized flat knitting machine of MACH-2XS, through the setting of the machine parameters. Finally, a full-shaped fake two-piece knitted blouse was formed.FindingsOn the basis of single-layer knitted garment pattern, a double-layer garment pattern is constructed, and the design and weaving are completed on the four-needle bed computerized knitting machine of MACH-2XS and its supporting SDS-APPEX3 design system through the fake two-piece double-layer garment style design. The double-layer joint model is an effective reference for the construction of this kind of fake two-piece fully formed knitted clothing.Originality/valueIn this paper, a design and knitting method of fully formed double-layer structure fake two-piece knitted garment is proposed. The integrated knitting of fully formed double-layer structure sweater is realized for the first time, which provides ideas for the development of fully formed double-layer structure knitted clothing style and enriches the fully formed clothing style.

Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm

The low frequency vibration of the vehicle in motion has a great influence on the ride comfort of occupants. The research on the vibration response characteristics of human body plays a great role in analyzing and improving ride comfort. The purpose of this study was to investigate the parameter identification of seated human body dynamic model. A seven-degree-of-freedom (DOF) lumped parameter model was established to describe the vibration response characteristics of human body. The derivation processes of apparent mass (AM) and seat to head transmissibility (STHT) were performed. After the theoretical calculation of the human body vibration characteristics, we used several different evolutionary algorithms to identify the 23 parameters of the model, including the mass, stiffness and damping parameters. By comparing the results of the five optimization algorithms and comprehensively analyzing the convergence and distribution of the non-dominated solution set, we found that the reference vector guided evolutionary algorithm (RVEA) shows good competitiveness in solving many-objective optimization problem (MaOP), that is, parameter identification of seated human body model in this paper. The AM and STHT calculated by model identification were compared with their measured by experiment. The result shows that the selected seven-DOF model can well describe the vertical vibration characteristics of seated human body and the identification method used in this paper can accurately identify the parameters of lumped parameter model, which provides convenience for the establishment of a complete “road-vehicle-seat-human body” system dynamic model.

A Study On Graph Theory Properties In Human Blood Circulatory System

Network science and graph theory applications have spread widely to help in understanding how human body features are linked to graph network structure, thus providing a conceptual framework that can help in reducing the analytical circulatory complexity and underlining how network topology can be used to characterize circulatory diseases [1].

Flood–pedestrian simulator for modelling human response dynamics during flood-induced evacuation: Hillsborough stadium case study

Abstract. The flood–pedestrian simulator uses a parallel approach to couple a hydrodynamic model to a pedestrian model in a single agent-based modelling (ABM) framework on graphics processing units (GPU), allowing dynamic exchange and processing of multiple-agent information across the two models. The simulator is enhanced with more realistic human body characteristics and in-model behavioural rules. The new features are implemented in the pedestrian model to factor in age- and gender-related walking speeds for the pedestrians in dry zones around the floodwater and to include a maximum excitement condition. It is also adapted to use age-related moving speeds for pedestrians inside the floodwater, with either a walking condition or a running condition. The walking and running conditions are applicable without and with an existing two-way interaction condition that considers the effects of pedestrian congestion on the floodwater spreading. A new autonomous change of direction condition is proposed to make pedestrian agents autonomous in wayfinding decisions driven by their individual perceptions of the flood risk or the dominant choice made by the others. The relevance of the newly added characteristics and rules is demonstrated by applying the augmented simulator to reproduce a synthetic test case of a flood evacuation in a shopping centre, to then contrast its outcomes against the version of the simulator that does not consider age and gender in the agent characteristics. The enhanced simulator is demonstrated for a real-world case study of a mass evacuation from the Hillsborough football stadium, showing usefulness for flood emergency evacuation planning in outdoor spaces where destination choice and individual risk perception have great influence on the simulation outcomes.

An adaptive recognition method for take-off action images of back-style high jump based on feature extraction

In the process of human movement, the behavioral characteristics of human body are complex and diverse. Thus, it is difficult to use accurate shape to represent the uncertainty of human behavior, which brings difficulties for 3D modeling in the state of motion. To solve this problem, an adaptive image recognition method based on feature extraction is proposed in this paper. Firstly, the image noise threshold was obtained by wavelet coefficient conversion. Then the wavelet coefficient matrix was constructed on the basis of decomposed image multi-resolution, and the image noise was removed according to the threshold. Based on this, Gaussian function in Canny method is introduced to obtain the edge coordinates of moving targets by convolution operation. After that, the image rotation and translation matrices were resolved according to the Murkowski distance to obtain the motion feature vectors. Finally, the hidden Markov model was constructed to segment the background and the target according to the feature sequence and the target edge coordinates, so as to realize the adaptive recognition of the take-off action images of the back-style high jump. Experimental results showed that this method had the advantages of high denoising effect, accurate feature extraction, error free edge division and accurate recognition result.

Motion recognition technology of badminton players in sports video images

Intelligent identification technology has made great progress in transportation, and its application in sports has attracted widespread attention. This research mainly discusses the research of human motion recognition technology in sports dance video images. In geometric algebraic space, using instance templates, a new cfrdF method, using css-based similarity of human body features as features, to construct an angle-adaptive and continuous-scale space template matching algorithm to calculate the similarity between horizontal plates and detected images value, set a certain threshold, so as to match the area where the human body is located. Based on the actual video analysis and display, using the self-similar structure of the color of the human object as the basic feature, it is described by the geometric algebra method, and the human body in the video image is extracted using template convolution objects and design adaptive template functions to extract human movements from sports dance video images. By loading each test video in turn, the posture of the characters in the video is checked every 30 s, and the approximate positions of the head and feet are marked and output to a txt file. Because lying on the ground during sports dance is a way of matching by height correction, the accuracy is less disturbed, but it can still reach 90.9%. The research results show that the template matching robot model proposed in this paper can accurately and robustly extract human objects in video.

Intelligent Sports Training System Based on Artificial Intelligence and Big Data

All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.

A Real-Time Photogrammetric System for Acquisition and Monitoring of Three-Dimensional Human Body Kinematics

Real-time acquisition and analysis of three-dimensional (3D) human body kinematics are essential in many applications. In this paper, we present a real-time photogrammetric system consisting of a stereo pair of red-green-blue (RGB) cameras. The system incorporates a multi-threaded and graphics processing unit (GPU)-accelerated solution for real-time extraction of 3D human kinematics. A deep learning approach is adopted to automatically extract two-dimensional (2D) human body features, which are then converted to 3D features based on photogrammetric processing, including dense image matching and triangulation. The multi-threading scheme and GPU-acceleration enable real-time acquisition and monitoring of 3D human body kinematics. Experimental analysis verified that the system processing rate reached ∼18 frames per second. The effective detection distance reached 15 m, with a geometric accuracy of better than 1% of the distance within a range of 12 m. The real-time measurement accuracy for human body kinematics ranged from 0.8% to 7.5%. The results suggest that the proposed system is capable of real-time acquisition and monitoring of 3D human kinematics with favorable performance, showing great potential for various applications.

Research on the Application of Computer Ergonomics in Industrial Product Design

Computer ergonomics is to design and optimize industrial products according to the structure and skill characteristics of human body, to realize the effective combination of products and human activities in the use process, and to accelerate the effective promotion of product experience and interaction. Based on this, this paper first analyses the standards and principles of industrial product design based on computer ergonomics, and then studies the utilization of computer ergonomics in industrial product design, including the influence of human factors, environment and social factors on industrial product design and specific utilization strategies.

Lower limb rehabilitation exoskeleton robot: A review

Lower limb rehabilitation exoskeleton robots (LLRERs) play a positive role in lower limb rehabilitation and assistance for patients with lower limb disorders, and they are helpful to improve patients’ physical status. More and more experiments pay more attention to the kinematic and dynamic data characteristics of different patient groups. However, it is not clear whether these devices have broad adaptability and their clinical significance, so it is necessary to summarize and analyze these research results. This paper summarizes the LLRERs prototype and product in recent years, also compares the advantages and disadvantages of the theory and technology used in these research, and compares the functional characteristics of the devices, finally summarizes the aspects of the LLRERs to be improved. These devices apply advanced theories, techniques or structures, as well as human kinematics and dynamics data. However, due to the complexity of human body characteristics and movement rules, the theory or technology applied in the study design of LLRERs remains to be further studied, which can be improved in many aspects, such as improve the human-computer cooperation of equipment or carry out clinical trials. This paper can provide reference for researchers and designers in the future study, as well as understanding and selecting LLRERs for all kinds of therapist and patients.

Automatic Segmentation and 3D Printing of A-shaped Manikins using a Bounding Box and Body-feature Points

A novel algorithm for 3D-printing technology was proposed to generate large-scale objects, especially A-shaped manikins or 3D human body scan data. Most of the conventional 3D printers have a finite printing volume, and it is the users’ work to convert the target object into a printable size. In this study, an automatic three-step segmentation strategy was applied to the raw manikin mesh data until the final pieces had a smaller size than the 3D printer’s maximum printing volume, which is generally called “beam length”. Human body feature point information was adopted for fashion and textile researchers to easily specify the desired cutting positions. A simple bounding box, especially orienting bounding box, and modified Boolean operator were proposed to extract the specified segments with computational stability. The proposed method was applied to graphically synthesized manikin data, and 1/8, 1/4, and 1/2 scale manikins were successfully printed, minimizing the amount of support structure.

Structural Design and Electrical Characteristics of Wearable and Flexible Stainless Steel/Polyester Fibre Mixture

The metallic fibres-based composite yarns have been increasingly used to make flexible, comfortable and wearable fabric sensors, which have advantages in monitoring human physical and physiological body characteristics. In our previous study, flexible and conductive blended yarns made from stainless steel (SS) fibres were fabricated into highly sensitive knitted fabric sensors. However, as the SS weight ratio increased (particularly the pure SS fibres-based yarn), yarns were easily broken in knitting process. Here we made adjustments to the new series of composite yarns with similar absolute fineness (same diameters) rather than relative fineness (linear density). Not only the problem of yarn breakage has been solved, it also retains the well-conductive (less than 70 Ω/cm) and flexible and wearable properties. A more important result is that the varying probe span length makes little impact on these SS fibre mixtures’ values of percolation threshold (0.178 based on weight ratio and 0.015 on volume fraction, respectively) and on critical exponents (about 1.3, according with the universal conductivity exponent in 2D conductive polymer composite system). The main reason may be on account of these composite yarns having similar diameters (absolute fineness) and so aspect ratio.

Iris pattern recognition as an infallible mean of identity establishment in forensic casework

Biometrics is a mean of forensic recognition based on me as ureme nts an d calculat ions o f human bo dy or biological features. Human body features. Biometric identification systems may make use of iris, retina, fingerprints, face, ear, gait parameters, facial physiognomy, photogrammetric or geometry features, etc. for recognition and hence identification pursuits. The high security areas like airports, nuclear reactors, thermal or hydro-electric power plants, legislative buildings, defense establishments etc., require secured entry of authorized persons. Biometric tools of recognition like automatic face recognition system can play a crucial role in recognizing illegal intruders to such areas. Iris is an excellent trait to be used in biometric system for recognition of an individual due to its individualistic, unique, universal and inimitable features. In this article, the current national and international status of use of iris patterns forforensic recognition, its advantages and limitations, and future prospects have been presented.

Machine Learning Method for Continuous Noninvasive Blood Pressure Detection Based on Random Forest

In order to reduce the influence of differences in human characteristics on the blood pressure prediction model and further improve the accuracy of blood pressure prediction, this paper establishes support vector machine regression model and random forest regression model for accurate blood pressure measurement. First, the photoelectric method is used to obtain the photoelectric plethysmography signal (PPG) and ECG signals from people of different ages, and the blood pressure value is roughly estimated based on the high-quality physiological signals and the vascular elastic cavity model; then the human body characteristics are used as the input parameters of the blood pressure prediction model, and the model parameters are used to find the best parameter combination to improve the prediction performance of the model; finally, through a lot of training and learning, the best blood pressure prediction model is selected to achieve accurate measurement of blood pressure values. It has been verified by experiments that the average absolute error of diastolic and systolic blood pressure based on the random forest optimization model meets the standard of less than 5mmHg formulated by AAMI (American Medical Instrument Promotion Association), which is better consistent with the method of mercury sphygmomanometer, and has more excellent performance than support vector machine regression model under the same conditions.

Selecting Promising Junior Swimmers in Egypt Using Automated Biometric Algorithms of Image Processing and Fuzzy Concepts

Modern sports need a great physical and psychological effort from young athletes to reach high performance levels. Stature length, leg length, foot length, arm length, hand length, shoulder width, hip width, chest width and weight are the anthropometric characteristics that affect swimmers' performances. This paper introduces new techniques to select promising junior swimmers in Egypt. It develops two automated algorithms to select junior swimmer depending on their anthropometric measurement. The first technique uses Canny filter to develop the first algorithm, while the second one uses the Fuzzy concepts. The proposed algorithms make use of the image processing technique to handle the anthropometric measurements, by detecting the human body feature points automatically from the front and side images. The 101 feature points extract automatically from 36 human body measurements, while swimming games needs only 8 body dimensions from these 36 human body measurements. Therefore, the proposed system is not limited to swimming sports but can also be applied to other sports. Moreover, the experimental results and the corresponding statistical analysis show the high accuracy and advantages of the proposed algorithms. The first algorithm improved the results that could have obtained using the well-known fully vision-based automatic human body method by 22.23%, 15.9%,29.41%, and 27.5%, for stature length, arm length, leg length, and shoulder width, respectively. Also, it gave the best results for Leg Length, and Shoulder width, while the second one yielded the best result for Stature Length.

Tracking In-Cabin Astronauts Using Deep Learning and Head Motion Clues

A person-following robot is under development for astronaut assistance on the Chinese Space Station. Real-time astronaut detection and tracking are the most important prerequisites for in-cabin flying assistant robots so that they can follow a specific astronaut and offer him/her assistance. In the limited space in the space station cabin, astronauts stand close to each other when working collaboratively; thus, large regions of their bodies tend to overlap in the image. In addition, because astronauts wear the same clothes most of the time, it is difficult to distinguish an individual astronaut using human body features. In this paper, we distinguish the astronauts by tracking their heads in the image. A deep learning model trained using big data is proposed for effective head detection. In addition, a motion model based on spatial clues is combined with the head detection results to track astronauts in the scene. A complete pipeline of the algorithm has been implemented and run efficiently on the Tegra X2 embedded AI microprocessor. A set of experiments were carried out and successfully validated the effectiveness of the proposed tracking algorithm. This algorithm is a step toward the implementation of robot assistants, especially in resource-limited environments.