Skeleton Model Research Articles

Biomechanism and exercise effect of fitness walking using twin walking sticks

In Japan, walking poles with pairs of sticks developed exclusively for fitness walking have been designed. A new concept of walking style (WS) has been conceived using these walking sticks to “effectively” walk around the city, comprehensive sports parks, or at rehabilitation hospitals. Stick manufacturers are promoting its health benefits; however, evidence supporting these claims is lacking. Hence, this study aimed to measure the influence of walking sticks and evaluate the exercise effect based on functional physical fitness related to WS characteristics. The participants were 12 WS instructors. They engaged in WS at a comfortable speed after walking normally at the same speed (WN) for ∼5 m (seven times), followed by WS again. The walking speed, step length, stride width, walk ratio, one-leg support time, and trajectory of the center of gravity (CG) (in the horizontal and vertical directions of one walking cycle) calculated from the whole-body skeleton model were analyzed. The gait of WS increased the step length, step width, and walking ratio as compared with that of WN (p<0.05). WS likely reduce cadence and one-leg support time (p<0.05). The CG locus in the left-right direction showed no significant differences between WS and WN. The maximum value of the CG locus in the vertical direction was high in WS (p<0.05). WS can be used as a navigation training tool that improves a walker's exercise efficiency and left-right leg coordination, thereby improving walking posture. This may help reduce the anxiety due to injuries and pain that may occur while walking.

Real-Time Identification from Gait Features Using Cascade Voting Method

Abstract There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric methods. Recently, methods using behavioural biometric features have gained popularity. Identification made using gait pattern is also one of these methods. The present study proposes a machine learning based system performing identification in real time via gait features using a Kinect device. The data set is composed of 23 individuals’ skeleton model data obtained by the authors. From these data, 147 handcrafted features have been extracted. Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting (GB), XG-Boost (XGB) and K-Nearest Neighbour (KNN) classifiers have been trained with these features. Furthermore, the output of these five machine learning models has been combined with a voting approach. The highest classification has been obtained with 97.5 % accuracy via a voting approach. The classification accuracies of the RF, DNN, XGB, GB and KNN classifiers are 95 %, 87.5 %, 85 %, 80 % and 65 %, respectively. The classification accuracy obtained via a voting approach is higher than in the previous studies. The developed system successfully performs real-time identification.

Research on aerobics training posture motion capture based on mathematical similarity matching statistical analysis

Abstract Aiming at the freely editable characteristics of human motion posture, a human skeleton model is extracted, and a human motion posture model library is established. The application of motion capture system in dance training is analysed, and a method based on similarity matching between feature planes is proposed to calculate each model. Parameters of motion data between parts are obtained. After verification, the method has high accuracy and robustness for the analysis of human poses so that dancers can accurately compare the differences with standard dance movements and provide theoretical support for dancers to perform scientific dance training.

Research on motion capture of dance training pose based on statistical analysis of mathematical similarity matching

Abstract In order to verify the effectiveness and feasibility of the combination of motion capture technology and teaching, based on dance teaching, this paper proposes a dance posture analysis method based on feature vector matching and applies it to dance teaching.. The main research work includes the following: (1) according to the characteristics of human motion poses-free editing, extracting human skeleton models, establishing a human motion model database, analysing the application of motion capture systems in dance training, and proposing a method of feature plane similarity matching to calculate model components and motion parameters. After verification, the method has high accuracy and robustness for the analysis of human posture, so that dancers can accurately compare the differences with standard dance movements, and provide theoretical support for scientific dance training. (2) Aiming at the complexity of learning dance, a dance teaching method based on motion capture technology is proposed. Using motion capture technology, a whole complex dance movement is decomposed into many small segments to make a teaching animation, which guides students to learn based on small dance movement. Imitation makes the abstract theory vivid, intuitive and easy to understand, which is conducive for the innovation of education and teaching methods.

Research on Action Recognition Method of Dance Video Image Based on Human-Computer Interaction

Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.

Toroidal skeleton model of a high-porosity cellular material for modeling a supersonic flow around a cylinder with a gas-permeable frontal insert at an angle of attack

Toroidal skeleton model of a high-porosity cellular material for modeling a supersonic flow around a cylinder with a gas-permeable frontal insert at an angle of attack

Aircraft System State Recognition and Fault Prediction Based on a Test Diagnostic Model

The existing testability models for fault prognosis of aircraft systems limit the implementation of prognosis and health management systems. This paper develops a test diagnosis modeling method and relevant algorithms to support dynamic testing and to evaluate fault prognostic ability during aircraft system design. According to the system principles and the complex function structure of aircraft systems, a test diagnostic model is established by integrating testing and prognostic information with a test diagnostic skeleton model using multi-signal flow. New test indexes are identified to assess the testability and prognostic ability of aircraft systems. Relevant state recognition and fault prediction algorithms are established by fusing the improved particle swarm optimization algorithm and Hidden Semi-Markov Model. The feasibility and validity of the test diagnostic modeling method and relevant algorithms are verified in an aircraft’s engine bleed air system. Training and test show that the model can support analysis and estimation, and the algorithms can ensure accurate results after training the HSMM using improved PSO algorithm.

Experimental and Numerical Simulation of Neuro Fuzzy Based Cartesian Robot for Soft Material Cutting

Present technology used for soft material cutting robot are laser, water-jet, ultrasonic, plasma and oxy-gas cutting. The scope for designing and fabrication of soft material cutting robots are increasing due to its cost effectiveness and quality of cutting. Cartesian robots are mainly used in assembly and manufacturing applications. They also have a high degree of mechanical rigidity, accuracy, and repeatability. A previous work of more elementary kind was used as a skeleton model to start this work The purpose of the work is the maintenance and implementation of a 4 Degree of freedom robot and also to provide intelligence to the robot using Fuzzy logic and Neuro-Fuzzy for soft material cutting. The kinematic modeling of robot manipulator is done using Denavit-Hartenberg (D-H) parameterization method and Euler-Lagrange method is used for dynamic analysis to determine actuator torque for each joint. The image is acquired with the help of the digital camera which is fixed in a suitable position so that it can scan the entire workspace. Prewitt edge detection algorithm was used for image processing and analysis. The signal for the cutting is interfaced through the Arduino Uno r3 controller.

Influence of quick lime on pore characteristics of high-temperature zone in iron ore sinter based on XCT technology

The pore structure of the bed has an important influence on the heat transfer and permeability characteristics in the iron ore sintering process. In this study, liquid nitrogen was applied to quench the high-temperature zone sinter to reproduce the original porous structure to the maximum extent. The effect of quick lime on pore characteristics of the high-temperature zone in the iron ore sinter was studied. XCT technology was applied to scan the sinter, and the model of solid phase, pore, and closed pore was reconstructed. The equivalent spherical diameter of the pore and its distribution frequency, volume fraction, sphericity, and surface porosity were statistically analyzed. The results indicate that the addition of quick lime increases the permeability of the green bed and shortens the sintering time. Meanwhile, it promotes the flow of the molten liquid phase, resulting in the bubble aggregation more fully. With the increase of the addition of quick lime, the porosity of the sinter decreases gradually. Finally, the pore skeleton model was established, and the pore skeleton parameters were statistically analyzed. With the increase of quick lime, the total pore volume and total pore length, the number of pore segments, branching nodes and terminal nodes all decrease.

Stochastic parametric skeletal dosimetry model for humans: General approach and application to active marrow exposure from bone-seeking beta-particle emitters.

The objective of this study is to develop a skeleton model for assessing active marrow dose from bone-seeking beta-emitting radionuclides. This article explains the modeling methodology which accounts for individual variability of the macro- and microstructure of bone tissue. Bone sites with active hematopoiesis are assessed by dividing them into small segments described by simple geometric shapes. Spongiosa, which fills the segments, is modeled as an isotropic three-dimensional grid (framework) of rod-like trabeculae that "run through" the bone marrow. Randomized multiple framework deformations are simulated by changing the positions of the grid nodes and the thickness of the rods. Model grid parameters are selected in accordance with the parameters of spongiosa microstructures taken from the published papers. Stochastic modeling of radiation transport in heterogeneous media simulating the distribution of bone tissue and marrow in each of the segments is performed by Monte Carlo methods. Model output for the human femur at different ages is provided as an example. The uncertainty of dosimetric characteristics associated with individual variability of bone structure was evaluated. An advantage of this methodology for the calculation of doses absorbed in the marrow from bone-seeking radionuclides is that it does not require additional studies of autopsy material. The biokinetic model results will be used in the future to calculate individual doses to members of a cohort exposed to 89,90Sr from liquid radioactive waste discharged to the Techa River by the Mayak Production Association in 1949-1956. Further study of these unique cohorts provides an opportunity to gain more in-depth knowledge about the effects of chronic radiation on the hematopoietic system. In addition, the proposed model can be used to assess the doses to active marrow under any other scenarios of 90Sr and 89Sr intake to humans.

3D phytomer-based geometric modelling method for plants-the case of maize.

Geometric plant modelling is crucial in in silico plants. Existing geometric modelling methods have focused on the topological structure and basic organ profiles, simplifying the morphological features. However, the models cannot effectively differentiate cultivars, limiting FSPM application in crop breeding and management. This study proposes a 3D phytomer-based geometric modelling method with maize (Zea Mays) as the representative plant. Specifically, conversion methods between skeleton and mesh models of 3D phytomer are specified. This study describes the geometric modelling of maize shoots and populations by assembling 3D phytomers. Results show that the method can quickly and efficiently construct 3D models of maize plants and populations, with the ability to show morphological, structural and functional differences among four representative cultivars. The method takes into account both the geometric modelling efficiency and 3D detail features to achieve automatic operation of geometric modelling through the standardized description of 3D phytomers. Therefore, this study provides a theoretical and technical basis for the research and application of in silico plants.

What and how well you exercised? An efficient analysis framework for fitness actions

Human action analysis has been an active research area in computer vision, and has many useful applications such as human computer interaction. Most of the state-of-the-art approaches of human action analysis are data-driven and focus on general action recognition. In this paper, we aim to analyze fitness actions with skeleton sequences and propose an efficient and robust fitness action analysis framework. Firstly, fitness actions from 15 subjects are captured and built to a fitness action dataset (Fitness-28). Secondly, skeleton information is extracted and made alignment with a simplified human skeleton model. Thirdly, the aligned skeleton information is transformed to an uniform human center coordinate system with the proposed spatial–temporal skeleton encoding method. Finally, the action classifier and local–global geometrical registration strategy are constructed to analyze the fitness actions. Experimental results demonstrate that our method can effectively assess fitness action, and have a good performance on artificial intelligence fitness system.

Creation of lattice structures with skeleton model for additive manufacturing

Creation of lattice structures with skeleton model for additive manufacturing

High-Resolution Microstructure Analysis of Cork Spot Disordered Pear Fruit "Akizuki" (Pyrus pyrifolia Nakai) Using X-Ray CT.

Cork spot is one of the most damaging physiological disorders in pear fruit, causing considerable economic loss every year. However, the mechanism of cork spot occurrence requires further examination. In this study, X-ray CT scanning was applied to analyze the microstructure of pear fruit “Akizuki” (Pyrus pyrifolia), a cultivar susceptible to cork spot disorder, to elucidate the fruit texture alteration between healthy and cork spotted fruit. Results showed that cork spotted fruit had much higher porosity (9.37%) than healthy fruit (3.52%). Reconstructed three-dimensional (3D) network skeleton models showed highly branched pore channels in cork spotted fruit and a low degree of pore connectivity in healthy fruit. Even in areas of disordered fruit without cork spot, the pore throat diameter, pore length, and coordinated core number (i.e., 77, 160, and 16, respectively) were much higher than that of healthy fruit. The structure analysis of fruit core showed that core deformation only occurred in cork spotted fruit. A much more highly branched network was observed in cork spotted fruit cores compared with healthy fruit cores. High-resolution observation of flesh tissue directly demonstrated that pore size in cork spotted fruit (87 μm) was four times larger than that of healthy fruit (22 μm). Altered expression of genes related to Ca2+ transport and the uneven distribution of intracellular Ca2+ were also shown to associate with the development of cork spot disorder. Our results suggest that flesh tissue damage likely occurred prior to the initiation of cork spot. The dysfunction of long-distance and transmembrane Ca2+ transport channels could be responsible for the imbalanced distribution of Ca2+ inside the fruit, thus resulting in the development of cork spot.

Scene image and human skeleton-based dual-stream human action recognition

The dual stream-based human action recognition model offers the advantage of high recognition accuracy, but the algorithm is less robust in case of lighting changes. The human skeleton has a strong ability to express human behavior and actions; however, the scene information is ignored. Drawing on the idea of the dual-stream model, this paper proposes a human skeleton and scene image-based dual-stream model for human action recognition. The motion features are extracted through the spatio-temporal graph convolution of the human skeleton, and a scene recognition model is proposed based on the sparse frame sampling of video and video-level consensus strategy to process the scene video and gather the visual scene information. The proposed model exploits the advantages of skeleton information in motion expression and the superiority of the image in scene presentation. The scene information and spatio-temporal graph convolution-based human skeleton limbs are fused complementarily to achieve human action recognition. Compared to the conventional optical flow-based dual-stream action recognition method, this model is verified by experimenting under unstable light conditions, and the performance of human action recognition is robust and promising.

Accuracy Of 6dof Kinematics Of Tibiofemoral Joint Using High-speed Dfis Combined With 3d Mri Model

Cui Zhang1,2,3,Yu Liu2* and Shaobai Wang21Biomechanics Laboratory, College of Sports and Health, Shandong Sport University, Jinan, China. 2School of Kinesiology, Shanghai University of Sport, Shanghai, China. 3Sports Biomehanics Laboratory, Shandong Institute of Sport Science, Jinan, China.*Yu Liu, (Corresponding author) PURPOSE: To validate the precision, accuracy and consistency of 6DOF movement of Tibiofemoral Joint via a custom high-speed Dual plane Fluoroscopic Imaging System(DFIS) combined with 3D MRI model registration. METHODS: 5 copper beads were embed in tibial and femur of one fresh pig hind leg separately. 3D beads rigid model and 3D skeleton model were reconstructed based on CT and MRI images of the beads and the tibia and femur of the pig hind leg separately. Flexion and extension movement of pig leg were recorded by high-speed DFIS. The registration results of 3D beads rigid model was set as gold standard. Bias, SD of bias, Bland-Altman analysis and Pearson correlation analysis were used to test the precision, accuracy and consistency of 6DOF results of MRI bone model, compare with gold standard. RESULTS: The 6DOF kinematic results of tibiofemoral joint obtained by high speed DFIS combined with MRI 3D bone model registration method was highly correlated to the results obtained by gold standard (r > 0.917). The bias and SD of bias of the anterior-posterior, medial-lateral, superior-inferior transitions, and flexion-extension, varus-valgus, internal-external rotations of tibiofemoral joint were in sub-millimeter and sub-degree level. The mean D-value of 3D MRI bone model registration and gold standard results were uniformly distributed within the consistency interval. CONCLUSION: Combined high-speed DFIS with MRI images to reconstruct 3D skeleton model for registration is a method with high accuracy, precision and consistency, which can be used to measure clinical 6DOF movement and soft tissue contact characteristics of the tibiofemoral joint. Funding was provided by the National Nature Science Foundation of China 81702211; the Movement Science Research Innovation Team(2019-183).

On the consolidation and creep behaviour of layered viscoelastic gassy sediments

Gassy sediments are frequently encountered in engineering geology and geotechnical engineering. Gas bubbles contained in these sediments significantly change the engineering properties, and further influence the consolidation and creep process. The long term behaviour of gassy sediment is drastically different from that of saturated and unsaturated soils. This study presents an analytical solution for exploring such behaviour within layered viscoelastic gassy sediments under different type of load. An Analytical layer element method together with a Laplace-Hankel transform are employed to solve the governing equations, and the viscoelastic solution is then captured with the aid of the elastic-viscoelastic correspondence principle. Comparisons of the present solution with published semianalytical solutions and experimental results, for a single and double layered soils, are provided. Extensive parametric analyses were carried out to explore the effects of the soil skeleton model, fractional order, type of load, saturation degree and stratification characteristics on consolidation and creep processes. In the present model, the permeability is saturation degree- and porosity-dependent and the modulus of elasticity is time-dependent, which can simulate the long term behaviours of gassy sediments reasonably.

Inside Humans: Creating a Simple Layered Anatomical Model from Human Surface Scans

3D morphable models are widely used to describe the variation of human body shapes. However, these models typically focus on the surface of the human body, since the acquisition of the volumetric interior would require prohibitive medical imaging. In this paper we present a novel approach for creating a volumetric body template and for fitting this template to the surface scan of a person in a just a few seconds. The body model is composed of three surface layers for bones, muscles, and skin, which enclose the volumetric muscle and fat tissue in between them. Our approach includes a data-driven method for estimating the amount of muscle mass and fat mass from a surface scan, which provides more accurate fits to the variety of human body shapes compared to previous approaches. We also show how to efficiently embed fine-scale anatomical details, such as high resolution skeleton and muscle models, into the layered fit of a person. Our model can be used for physical simulation, statistical analysis, and anatomical visualization in computer animation and medical applications, which we demonstrate on several examples.

Viscoplastic flow of functional cellular materials with use of peridynamics

The subject of the study is the deformation of the oxygen-free high conductivity copper. The copper sample is given in the form of a foam. The sample undergoes an impact into an elastic wall. The strain rate hardening effect is investigated. The numerical model of the open-cell foam skeleton is prepared in the framework of the peridynamics method. The dynamic process of compression with different impact velocities is simulated. It has been found that the strain rate hardening effect is essential for the load-carrying capacity of the material under study. Taylor impact test of solid cylinder analysis precedes the analysis of the metallic foam.

3000-year-old shark attack victim from Tsukumo shell-mound, Okayama, Japan

Modern shark attacks are uncommon and archaeological examples are even rarer, with the oldest previously known case dating to ca. AD 1000. Here we report a shark attack on an adult male radiocarbon dated to 1370–1010 cal BC during the fisher-hunter-gatherer Jōmon period of the Japanese archipelago. The individual was buried at the Tsukumo site near Japan’s Seto Inland Sea, where modern shark attacks have been reported. The victim has at least 790 perimortem traumatic lesions characteristic of a shark attack, including deep, incised bone gouges, punctures, cuts with overlapping striations and perimortem blunt force fractures. Lesions were mapped onto a 3D model of the human skeleton using a Geographical Information System to assist visualisation and analysis of the injuries. The distribution of wounds suggests the victim was probably alive at the time of attack rather than scavenged. The most likely species of shark responsible for the attack is either a white shark (Carcharodon carcharias) or a tiger shark (Galeocerdo cuvier). Shortly after the attack most, though not all, of his body was recovered and buried in the Tsukumo cemetery.