Human Posture Research Articles

A study of the dynamic evolution of spherical gravitating systems

This article discusses the problems of origin and evolution and issues of numerical modeling of spherical systems, such as clusters of galaxies, globular clusters or E0 galaxies, characterized by massiveness and relatively old age, including up to 1000 bodies. First, observational data on galaxy clusters and their results are analyzed, including using a numerical method. Methods for numerical modeling of the evolution of spherical systems are studied. Two models are analyzed: the first is spherically homogeneous, with an isotropic distribution, and the second is with a particle distribution obeying the Plummer model. Changes in the position of bodies in the system and the distribution of velocities for different moments of time were obtained for each individual model. The calculation results are presented in the form of graphs. In the first model, at an early stage of evolution, the system collapses: a dense core is formed in the center, and a shell is formed around it. It is shown that over time the concentration of the nucleus decreases and it begins to stretch and the size of the system begins to increase. In this case, the distribution of bodies in the system obeys the Gaussian distribution and remains unchanged until the end of evolution. The second model also shows that at an early stage of evolution, the system collapses: then the system contracts and a compacted core forms in the center. The difference between the second model and the first is that a dense halo appears around the nucleus. It is found that sometimes in the early stages of evolution the system is slightly elongated and then tends to a spherical shape. It is also discovered that when solving the modeling of spherical systems, the choice of initial conditions plays an important role

Cloud IoT-Oriented Secure College Physical Education Teaching Platform Vased on Deep Learning

In physical education (PE) teaching, the teaching platform is comprehensively applied to provide students with high-quality PE teaching resources to meet their learning needs in different forms. A human motion trajectory detection method is proposed based on deep learning and superpixels. The initial positioning of human body is performed through the attention mechanism and the YOLOv5 model, and the human target tracking is performed through superpixels. Aiming at the difficulty of cross-domain security management due to the lack of security infrastructure in multi-domain cloud IoT, a lightweight certificateless cross-domain authentication scheme is designed. Simulation results show that the proposed detection method has significantly improved accuracy and running time compared with traditional methods and can adapt to different detection scenarios. Furthermore, the proposed certificateless cross-domain authentication scheme can securely access sensor-generated data under the cloud IoT of the PE teaching platform in colleges.

A flexible capacitive pressure sensor with dual-layer microstructure for health monitoring

Flexible pressure sensors have gained increasing acceptance in recent decades due to their stability, low power consumption, and portability. However, compared to traditional sensors, the linearity of flexible sensors still needs improvement. In this study, we designed and prepared a double-layer micro-structured flexible capacitive pressure sensor with a simple structure, low cost, excellent linearity and high sensitivity. The chemical raw materials used in our experiments are inexpensive, and the overall preparation method is simple. The sensor exhibits excellent linearity (0.993, <10 kPa; 0.978, 10～130 kPa) and combines high sensitivity (0.399 kPa−1, <10 kPa) with a wide detection range (0～130 kPa). The sensor can accurately and stably detect certain sound signals and throat movements. We designed and prepared a sensor array to monitor human sitting posture by identifying pressure distribution, thus avoiding irreversible damage caused by poor posture. This demonstrates the potential of the sensor in health monitoring.

Improving the Recognition and Analysis of the Surendra Algorithm in Athletes’ Motion Capture

With the development of computer technology, its application in athletes’ motion capture is more and more extensive, which can be used to design suitable sensors by the Surendra algorithm. In recent years, more and more scholars have begun to use motion capture technology to study human motion posture, analyze and study human motion posture data and apply it to people’s work, study and life. Motion capture technology has also become a key technology in the field of human motion posture research and is playing an increasingly important role. In this paper, a three-dimensional skeletal model of the athlete’s body is first established based on dynamics, and the athlete’s movement characteristics are simulated by this model. Then, the athlete’s movement posture is judged to determine the appropriate form of movement expression. Then, the improved Surendra algorithm is used to detect the movement movements.

GR‐Former: Graph‐reinforcement transformer for skeleton‐based driver action recognition

AbstractIn in‐vehicle driving scenarios, composite action recognition is crucial for improving safety and understanding the driver's intention. Due to spatial constraints and occlusion factors, the driver's range of motion is limited, thus resulting in similar action patterns that are difficult to differentiate. Additionally, collecting skeleton data that characterise the full human posture is difficult, posing additional challenges for action recognition. To address the problems, a novel Graph‐Reinforcement Transformer (GR‐Former) model is proposed. Using limited skeleton data as inputs, by introducing graph structure information to directionally reinforce the effect of the self‐attention mechanism, dynamically learning and aggregating features between joints at multiple levels, the authors’ model constructs a richer feature vector space, enhancing its expressiveness and recognition accuracy. Based on the Drive &amp; Act dataset for composite action recognition, the authors’ work only applies human upper‐body skeleton data to achieve state‐of‐the‐art performance compared to existing methods. Using complete human skeleton data also has excellent recognition accuracy on the NTU RGB + D‐ and NTU RGB + D 120 dataset, demonstrating the great generalisability of the GR‐Former. Generally, the authors’ work provides a new and effective solution for driver action recognition in in‐vehicle scenarios.

Proposal and validation of a new approach in tele-rehabilitation with 3D human posture estimation: a randomized controlled trial in older individuals with sarcopenia

ObjectiveThrough a randomized controlled trial on older adults with sarcopenia, this study compared the training effects of an AI-based remote training group using deep learning-based 3D human pose estimation technology with those of a face-to-face traditional training group and a general remote training group.MethodsSeventy five older adults with sarcopenia aged 60–75 from community organizations in Changchun city were randomly divided into a face-to-face traditional training group (TRHG), a general remote training group (GTHG), and an AI-based remote training group (AITHG). All groups underwent a 3-month program consisting of 24-form Taichi exercises, with a frequency of 3 sessions per week and each session lasting 40 min. The participants underwent Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-meter walking pace, Timed Up and Go test (TUGT), and quality of life score (QoL) tests before the experiment, during the mid-term, and after the experiment. This study used SPSS26.0 software to perform one-way ANOVA and repeated measures ANOVA tests to compare the differences among the three groups. A significance level of p < 0.05 was defined as having significant difference, while p < 0.01 was defined as having a highly significant difference.Results(1) The comparison between the mid-term and pre-term indicators showed that TRHG experienced significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05); GTHG experienced extremely significant improvements in 6-meter walking pace and QoL (p < 0.01); AITHG experienced extremely significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05). (2) The comparison between the post-term and pre-term indicators showed that TRHG experienced extremely significant improvements in TUGT timing test (p < 0.01); GTHG experienced significant improvements in ASMI and TUGT timing test (p < 0.05); and AITHG experienced extremely significant improvements in TUGT timing test (p < 0.01). (3) During the mid-term, there was no significant difference among the groups in all tests (p > 0.05). The same was in post-term tests (p > 0.05).ConclusionCompared to the pre-experiment, there was no significant difference at the post- experiment in the recovery effects on the muscle quality, physical activity ability, and life quality of patients with sarcopenia between the AI-based remote training group and the face-to-face traditional training group. 3D pose estimation is equally as effective as traditional rehabilitation methods in enhancing muscle quality, functionality and life quality in older adults with sarcopenia.Trial registrationThe trial was registered in ClinicalTrials.gov (NCT05767710).

Research on prevention and control tracking system based on 3D face recognition

In order to improve the tracking effect of prevention and control, this paper combines three-dimensional face recognition technology and posture recognition method to build a prevention and control tracking system, and designs a single-stage human target detection and posture estimation network based on thermal infrared images. By using infrared recognition methods, the face and human body shape can be directly recognized, overcoming the problem of traditional visual recognition methods being easily occluded. The network can simultaneously complete two tasks of human target detection and pose estimation in a multi-task manner. Moreover, this paper uses the knowledge distillation strategy to train a lightweight model to further reduce the amount of model parameters to improve the inference speed. In addition, this paper uses a single-stage human target detection and posture estimation network to judge the effect of prevention and control tracking. Through the prevention and control tracking effect test, it can be seen that the prevention and control tracking system based on 3D face recognition proposed in this paper can effectively improve the prevention and control tracking effect.

REALTIME IMPLEMENTATION OF SHOULDER POSTURE DETECTION WITH COMPUTER VISION

The development of health technology is currently growing rapidly along with the needs of health workers for medical information. Computer Vision is one of the technologies used in the medical world, including the human shoulder posture detection system using the MediaPipe library. This system automatically calculates the logic of posture measurements based on the basis of manual measurements with anthropometric tools, so that health workers can easily get precise and accurate information about the results of shoulder posture detection. The system created with the Python programming language allows real-time shoulder posture detection and avoids human error. This research successfully created a human shoulder posture detection system using MediaPipe on computer vision technology to make it easier for medical personnel to get real-time measurement results.

Binding Nature and Scope of the Arbitration Agreement Stipulated in the Articles of Incorporation for New Partners

A company formed with bylaws that include an arbitration clause and the entry of the participation of new partners and/or shareholders, a legal debate begins as to whether or not these partners and/or shareholders are subject to compliance with the arbitration agreement established in the bylaws and whether or not it is binding for the new partners and/or shareholders participating in the company. The above legal approach is a problem that has been discussed in the country for a decade now, without having reached a peaceful conclusion, since in different judicial scenarios two theses have been held contrary to each other, in this case the honorable Constitutional Court and the Superintendence of Corporations, have completely opposite positions. The purpose of this paper is to analyze the positions of both bodies in order to determine which of the two theories is more consistent with the regulations governing corporate activity, as well as the regulations governing the arbitration clause and the arbitration agreement.

Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor.

Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor's trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.

Four-quadrant propeller hydrodynamic performance mapping for improving ship motion predictions

On the path toward fully autonomous sea vessels, forecasting a ship’s exact velocity and position during its route plays a crucial role in dynamic positioning, target tracking, and autopilot operations of the unmanned body navigating toward predetermined locations. This paper addresses the prediction of the operational performance of a free-running submarine advancing in a straight route (in surge motion). Along with the forward advancing vessel (straight-ahead motion) the study covers all possible scenarios of ship’s surge, including crash-ahead, crash-back, and astern motions. Conventional maneuvering models cannot handle motions other than forward advancement due to the absence of propeller data in all four quadrants of hydrodynamic performance map. This study proposes an approach for predicting submarine performance in all these surge conditions by utilizing four-quadrant propeller performance and resistance test data. We developed an in-house code, SMot4QP, to simulate ship speed and position in the time domain. We obtained satisfying results for the straight-ahead and crash-ahead motions, while the crash-back and astern maneuvers require further refinement due to propeller wake interaction with the hull. The proposed method is capable of predicting the motions of all types of vessels using the ship’s resistance and four-quadrant propeller test results. Thus, SMot4QP offers a fast and robust alternative to computationally expensive free-running self-propulsion simulations for operational performance prediction in broader naval applications.

Antitrust Legislation in the Context of Constitutional Control: Novelties of Law Enforcement Practice

In December 2023, 30 years have passed since the adoption of the Constitution of the Russian Federation. The positions of the constitutional control bodies have always played and continue to play an important role in the development of antimonopoly legislation and law enforcement practice in cases of protection of competition. In the practice of the Constitutional Court of the Russian Federation (CC of Russia) and a number of other law enforcement officers, competition is considered in the context of the constitutional value that all law enforcement officers, including antimonopoly authorities and courts, are obliged to protect. In the constitutions of a number of foreign countries, there is often no direct indication of the need to protect competition, which increases the role of the highest courts in creating a legal mechanism for its protection. The authors analyze the law enforcement practice of the Constitutional Court of the Russian Federation established after 2018 on the application of antimonopoly legislation and the influence of the positions of the supreme constitutional control body on the development of Russian competition law.

Optimization of Human Posture Recognition based on Multi-view Skeleton Data Fusion

This research introduces a novel method for fusing multi-view skeleton data to address the limitations encountered by a single vision sensor in capturing motion data, such as skeletal jitter, self-pose occlusion, and the reduced accuracy of three-dimensional coordinate data for human skeletal joints due to environmental object occlusion. Our approach employs two Kinect vision sensors concurrently to capture motion data from distinct viewpoints extract skeletal data and subsequently harmonize the two sets of skeleton data into a unified world coordinate system through coordinate conversion. To optimize the fusion process, we assess the contribution of each joint based on human posture orientation and data smoothness, enabling us to fine-tune the weight ratio during data fusion and ultimately produce a dependable representation of human posture. We validate our methodology using the FMS public dataset for data fusion and model training. Experimental findings demonstrate a substantial enhancement in the smoothness of the skeleton data, leading to enhanced data accuracy and an effective improvement in human posture recognition following the application of this data fusion method.

Анализ эквивалентности трехмерных моделей при 3D-интерпретации данных аэроэлектроразведки

The article is devoted to the problem of the equivalence of three-dimensional geoelectric models obtained as a result of 3D-interpretation of aerial electrical survey data in medium where local conductive bodies are covered by a heterogeneous near-surface layer. To carry out the research, we used data from aerial electrical prospecting carried out in the Creighton area (Canada) when searching for polymetallic ores. A two-stage 3D-inversion was used to build the geoelectric model. The purpose of the first stage of 3D-inversion was to reconstruct near-surface structures based on survey data obtained at the early times. The second stage of 3D-inversion was based on the residual map constructed from the practical signals and the signals obtained after the first stage of 3D-inversion at later times. Using the residual map, the starting positions of local bodies under the overlying layer were specified, and their geometry and properties were obtained during local 3D-inversions. Based on the results of 3D-inversion, a local conductive body was identified and confirmed by drilling data. An equivalence analysis was carried out for a three-dimensional model of this body. Various options for initial models differing in the number of blocks describing the body were considered, as well as options for changing the size of blocks in the model obtained as a result of 3D-inversion. It was found that the main equivalence is related to the large distance between flight lines, which is several times greater than the distance between positions on the profile. Therefore, most equivalent models are characterized by a decrease in the size of blocks in the direction orthogonal to the profiles, due to an increase in their size along the profile and a change in resistivity. At the same time, according to the criterion for drilling wells, the resulting equivalent models were identical, i.e. from a geological point of view, no fundamental difference was found between the resulting equivalent models.

Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design

The poor application effect of traditional sports training methods and the difficulty of recording data due to the time and space constraints of sports make it difficult for trainers to improve their learning outcomes. Based on this, the study proposes to apply human posture recognition in sports teaching design, and use VGGNet-19 as a feature extractor and OpenCV open source software to capture posture movements, and introduce the concepts of joint angle and movement similarity to design a sports assessment system for physical education based on the geometric spatial feature variability analysis of posture based on limb angle information. The testing outcomes demonstrate that the study’s improved gesture recognition algorithm has a recognition rate of more than 90% on gesture movements, and the maximum recognition error value (0.010) is smaller than that of the dynamic time-regularised gesture algorithm (0.014) and the convolutional neural network algorithm (0.017). The assessment system is also better able to improve students’ professional performance and satisfaction, with its average professional score and satisfaction reaching 86 and 92%, which is significantly better than other comparative algorithms. The method is effective in providing trainers with data-based training scenarios and helping them to improve their learning in sport.

Research on the Application of VR Technology based on Human Posture Recognition in Online Physical Education

Offline and current online physical education classes are characterized by low learning efficiency and poor teaching quality. To solve this problem, the study combines a dynamic time regularization algorithm and local image feature action recognition with virtual technology, which is applied to online teaching to improve teaching quality. Experimental results indicate that the overall model accuracy reaches more than 94%, and in the public dataset MSR, the model accuracy reaches 91.4%. In the UTK dataset, the accuracy reaches 85.8%. When the accuracy of pose recognition is compared, the research model is superior to the two traditional models. The validation results against KNN and SVM are 62% and 71% as well as 79% and 84%, respectively, and the experimental results of the research model have improved by 9% and 5%. The research technique achieves an overall fit of more than 90% for different parts in the analysis of motion capture, which is superior to the traditional online instruction. The overall teaching satisfaction reaches more than 93.5% in the comparison of online physical education courses with offline courses. In summary, the optimized and improved posture recognition system is better for human motion capture, and the applied virtual technology can effectively improve the learning effect and teaching quality of online physical education courses.

Pose Estimation Algorithm for Construction Workers

In order to solve the problem of difficulty in judging the human posture of construction workers in complex environments, this paper uses the method based on bone points to identify the human posture, first produces a video dataset of construction workers, and then compares the two algorithms, and selects a suitable pose estimation algorithm for construction site scenarios, which contributes to the pose estimation of construction workers.

AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale

ABSTRACT Background The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients’ walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor’s empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. Objective To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. Methods Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. Results The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. Conclusion This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.

Research on entertainment robots based on artificial intelligence interaction for human posture recognition and sports activity monitoring

With the application of artificial intelligence technology in sports activity monitoring, new technologies such as interactive robots and the Internet of Things are being more widely used, which can provide entertainment and interactive guidance for sports training. This article analyzes the research on human pose recognition and sports activity monitoring of entertainment robots based on artificial intelligence interaction. This paper designs and develops an entertainment robot system that can recognize human posture and monitor physical activities. Users can interact with the robot and get real-time feedback about their posture and activities, thereby improving motor skills and physical fitness. After in-depth analysis of the theory of dynamic Bayesian network, we used EM algorithm to build a dynamic Bayesian network model, carried out detailed research on the random variables represented by the nodes of the dynamic model, and used the Internet of Things human posture recognition technology to extract the features of human behavior according to the characteristics of human behavior. The results show that the attitude recognition accuracy can be improved by adding the dynamic Bayesian network based on the random forest algorithm. The entertainment robot can accurately identify human posture and monitor sports activities, and has good real-time and stability. Through interaction with the robot, users can get personalized suggestions for individual postures and activities, improving the training effect and enjoyment of physical activities. Therefore, through the in-depth research and analysis of the dynamic Bayesian network and the human posture recognition of the Internet of Things, it is found that the sports activity monitoring system proposed in this paper can comprehensively monitor students’ sports activities, so as to achieve the goal of comprehensively improving students’ physical fitness.

CLASSIFICATION OF RHYTHMIC GYMNASTICS SPORT ELEMENTS BY VIDEO

The work devoted to human posture recognition during rapid movements and complex non-standard poses due to the large number of limbs involved in the movement. Rhythmic gymnastics was chosen as the subject area and, accordingly, the specifics of the judge's assessment of the athlete's performance. Many synchronized videos with fast movements and sequences of complex poses from different angles allows us to form a data set necessary for further research and implementation of the results obtained both in socially important industries and in the market of commercial services using artificial intelligence technologies. A computer system has been developed that can be used to increase the objectivity of sports judging at rhythmic gymnastics competitions, as well as to become an alternative to the traditional judging system in the case of competitions held in a remote format. By scaling up the task, the system can also be used to diagnose problems with the human nervous system and musculoskeletal system. As a result of the research, a dataset depicting the performance of sports elements was collected and structured. The peculiarities of the mediapipe and ViTPose models were identified and the best solution for preprocessing the prepared set was chosen. The main result of this work is a built and trained model for classifying sports elements, which classifies 7 elements with an accuracy of 0.9048. The accuracy indicates that the model performs at a high level, correctly classifying sports elements in most cases. This level of accuracy indicates that the model has been effectively trained to classify these specific elements. In the future, to be able to fully evaluate the performances of female rhythmic gymnasts, it is necessary to add tracking of the object with which the athlete performs, to create a method for tracking interaction with it.