Human Posture Research Articles

Finite element analysis of an intact lumbar spine model: Effects of loading under different coordinate systems.

Finite element (FE) analysis has been used in studying the biomechanics of the lumbar spine. While some FE studies used a follower load technique that intended to replace the compressing effect of local muscle force, other studies considered satisfying the relationship between the human body's posture and the centre of gravity (CG) for investigating spine biomechanics. However, the above studies did not reveal the importance of a coordinate system that gratifies the posture-CG relation and follower load techniques. The present FE study compares the variation in ranges of motion (ROM) and stress-strain distributions due to the application of loading via different coordinate systems, follower (FCS) and global (GCS). A subject-specific computed tomography scan-based intact spine (L1-L5) FE model was developed and simulated for physiological movements. The FE results indicated a minimum deviation in ROM of 2.7° for the L1-L5 full model at all physiological activities between the defined coordinate systems. The observed variation for the L3-L4 functional spinal unit was between 4.7° and 19°. The von Mises strain in the vertebrae was between 0.0007 and 0.003 for the FCS case. In contrast, the peak von Mises strain for the GCS case was above the compressive yield strain limit of cancellous bone by 38.5%. The GCS model transferred the load unsymmetrically, whereas the distribution was symmetrical for the FCS case, without any potential risk of bone failure. These observations clearly indicate that the selection of the appropriate loading coordinate system is as crucial as the magnitude of loading.

Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data.

Ambient Assisted Living (AAL) systems are designed to provide unobtrusive and user-friendly support in daily life and can be used for monitoring frail people based on various types of sensors, including wearables and cameras. Although cameras can be perceived as intrusive in terms of privacy, low-cost RGB-D devices (i.e., Kinect V2) that extract skeletal data can partially overcome these limits. In addition, deep learning-based algorithms, such as Recurrent Neural Networks (RNNs), can be trained on skeletal tracking data to automatically identify different human postures in the AAL domain. In this study, we investigate the performance of two RNN models (2BLSTM and 3BGRU) in identifying daily living postures and potentially dangerous situations in a home monitoring system, based on 3D skeletal data acquired with Kinect V2. We tested the RNN models with two different feature sets: one consisting of eight human-crafted kinematic features selected by a genetic algorithm, and another consisting of 52 ego-centric 3D coordinates of each considered skeleton joint, plus the subject's distance from the Kinect V2. To improve the generalization ability of the 3BGRU model, we also applied a data augmentation method to balance the training dataset. With this last solution we reached an accuracy of 88%, the best we achieved so far.

The role and importance of parliamentary associations of the subjects of the Russian Federation in the formation of a system of managed interregional integration in 2000-2005.

The article is devoted to the study of the place and role of parliamentary associations of the subjects of the Russian Federation in the formation of a unified system of interregional integration at the initial stage of the federal reform in 2000-2005. The object of the study is the parliamentary associations of the subjects of the Russian Federation, the subject is the process of including parliamentary associations in the emerging system of interregional integration and cooperation. The following aspects of the problem are considered: the formation of state policy regarding interregional cooperation of the subjects of the Federation and the participation of parliamentary associations in this process; the organization of interaction between the Federation Council, presidential plenipotentiaries in federal districts and parliamentary associations; the conditions and main directions of activity of interparliamentary associations in the context of the redistribution of powers on subjects of joint jurisdiction of the center and regions. General scientific and special historical research methods were used as the methodological basis of the work: methods of analysis and synthesis, historical-genetic, problem-analytical and historical-comparative methods, as well as the method of structural and functional analysis. The author concludes that the potential of parliamentary associations operating in Russia since the early 1990s has been used to form a system of managed interregional integration. They were included in the joint legislative process on the subjects of joint jurisdiction of the center and the subjects of the Federation by establishing cooperation with the Federation Council and exercising general control over their activities by the presidential Plenipotentiary representative in the federal districts. The study showed that in response to the centralizing policy of the federal center, the parliamentary associations operating in 2000-2005, in order to promptly respond to the requests of the federal center, centralize their organizational foundations and transfer the main levers of management of associations to the heads of legislative authorities of the regions. It is noted that under the conditions of legislative restrictions, the activities of associations are mainly concentrated around issues that are jointly managed by the center and the regions. At the same time, their ability to influence the legislative process is significantly reduced, as well as to defend the consolidated position of the legislative bodies of state power of the subjects of the Federation before the federal authorities.

Discover human poses similarity and action recognition based on machine learning

In the computer vision field, human action recognition depending on pose estimation recently made considerable progress, especially by using deep learning, which improves recognition performance. Therefore, it has been employed in various applications, including sports and physical activity follow-up. This paper presents a technique for recognizing the human posture in different images and matching their pose similarity. This aims to evaluate the viability of employing computer vision techniques to verify a person's body pose during exercise and determine whether the pose is executed properly. Exercise is one strategy we use to maintain our health throughout life. Gymnastics and yoga are two examples of this type of exercise. The proposed algorithm identifies human action by recognizing the body's key points. The OpenPose library has been used to detect 18 key points of the human body. The action classification task is performed using the support vector machine (SVM) algorithm. Then, the algorithm computes the similarity of the human pose by comparing a model image to a test image to determine the matching score. Evaluations show that our method can perform at a competitive or state-of-the-art performance on a number of body pose datasets.

Privileged Learning Using Regularization in the Problem of Evaluating the Human Posture

Privileged Learning Using Regularization in the Problem of Evaluating the Human Posture

Real Time Human Body Posture Analysis Using Deep Learning

Abstract: We present a novel approach for accurately estimating the pose of objects in a low-cost and resource-efficient manner, making it suitable for deployment on embedded systems. Our algorithm comprises of two primary stages: object detection and spatial reconstruction. In the first stage, we employ a Convolutional Neural Network (CNN) called PoseNet for object detection. This approach has proven to be effective in detecting and localizing objects in an image. Next, utilizing stereo correspondences, we 3D reconstruct the spatial coordinates of multiple ORB features within the object's bounding box. This enables us to accurately estimate the position of the object in space. To calculate the final position of the object, we compute a weighted average of the stereo-corresponded key points' spatial coordinates. The weights are proportional to the level of ORB stereo matching, which enables us to obtain a more accurate estimate of the object's position in space. Our algorithm was tested in a calibrated environment, and we compared the results with a deep learning-based method using various datasets. The results show that our approach outperforms existing methods in terms of accuracy, while maintaining a low cost and efficient resource utilization. Our proposed method has several applications, including the quantitative and qualitative analysis of human posture. By analyzing all aspects of a person's posture, we can determine if there are any postural deviations, imbalances, or muscle weaknesses that may be causing pain or discomfort. This information can then be used to develop personalized rehabilitation programs, reducing the risk of injury and enhancing athletic performance. Furthermore, our approach can be used in various assistive technology applications, such as the control of robotic arms for pick-andplace tasks. The low-cost and resource-efficient nature of our algorithm make it ideal for deployment in embedded systems, enabling us to develop affordable and accessible assistive technology solutions. In conclusion, our proposed algorithm provides an accurate, low-cost, and resource-efficient solution for pose estimation, with a wide range of potential applications in human posture analysis, assistive technology, and beyond.

Detection method of limb movement in competitive sports training based on deep learning

Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.

Design and Research of Wearable Fall Protection Device for the Elderly

A protective device was designed that can be worn on the elderly, which consists of protective airbag, control box and protective mechanism. The combined acceleration, combined angular velocity and human posture angle are selected as the parameters to determine the fall, and the threshold algorithm and SVM algorithm are used to detect the fall. The protective mechanism is an inflatable device based on CO2 compressed air cylinder, and the equal-width cam structure is applied to its transmission part to improve the puncture efficiency of the compressed gas cylinder. A fall experiment was designed to obtain the combined acceleration and angular velocity eigenvalues of fall actions (forward fall, backward fall and lateral fall) and daily activities (sitting-standing, walking, jogging and walking up and down stairs), showing that the specificity and sensitivity of the protection module reached 92.1% and 84.4% respectively, which verified the feasibility of the fall protection device.

Municipal and Industrial Urban Waste: Legal Aspects of Safe Management

Competent management of the production and consumption of waste is the foundation for ensuring a favorable environment in cities and comfortable living conditions for the population. Laws and regulations play a key role in this process since they determine measures aimed at creating conditions for safe waste management, an effective management system in the field of environmental protection from waste pollution. In the cities of many developing countries, including Russia, despite the efforts being made, there is an increase in the volume of municipal solid waste. Solving the problems of waste management has been set as a national task. The article analyzes the current condition of solid waste management systems in developed and developing countries and identifies the features and prospects of waste management, including the one in Russia. It is established that the existing set of organizational, sanitary, and legal measures, and legal regulation of relations and law enforcement practices in the field of solid municipal waste management in many developing countries is still in the forming stage. The positive experiences of countries in implementing sustainable systems of safe waste management and the positions of judicial bodies on controversial issues of waste management in cities can be used as the basis for an environmental policy of safe waste management at all levels of public authority, as well as improving legislation in the field of waste management.

Contactless monitoring of human behaviors in bed using RSSI signals.

In this paper, contactless monitoring and classification of human activities and sleeping postures in bed using radio signals is presented. The major contribution of this work is the development of a contactless monitoring and classification system with a proposed framework that uses received signal strength indicator (RSSI) signals collected from only one wireless link, where different human activities and sleep postures, including (a) no one in the bed, (b) a man sitting on the bed, (c) sleeping on his back, (d) seizuresleeping, and (e) sleeping on his side, are tested. With our proposed system, there is no need to attach any sensors or medical devices to the human body or the bed. That is the limitation of the sensor-based technology. Additionally, our system does not raise a privacy concern, which is the major limitation of vision-based technology. Experiments using low-cost, low-power 2.4GHz IEEE802.15.4 wireless networks have been conducted in laboratories. Results demonstrate that the proposed system can automatically monitor and classify human sleeping postures in real time. The average classification accuracy of activities and sleep postures obtained from different subjects, test environments, and hardware platforms is 99.92%, 98.87%, 98.01%, 87.57%, and 95.87% for cases (a) to (e), respectively. Here, the proposed system provides an average accuracy of 96.05%. Furthermore, the system can also monitor and separate the difference between the cases of the man falling from his bed and the man getting out of his bed. This autonomous system and sleep posture information can thus be used to support care people, physicians, and medical staffs in the evaluation and planning of treatment for the benefit of patients and related people. The proposed system for non-invasive monitoring and classification of human activities and sleeping postures in bed using RSSI signals.

Research on Railway Dispatcher Fatigue Detection Method Based on Deep Learning with Multi-Feature Fusion

Traffic command and scheduling are the core monitoring aspects of railway transportation. Detecting the fatigued state of dispatchers is, therefore, of great significance to ensure the safety of railway operations. In this paper, we present a multi-feature fatigue detection method based on key points of the human face and body posture. Considering unfavorable factors such as facial occlusion and angle changes that have limited single-feature fatigue state detection methods, we developed our model based on the fusion of body postures and facial features for better accuracy. Using facial key points and eye features, we calculate the percentage of eye closure that accounts for more than 80% of the time duration, as well as blinking and yawning frequency, and we analyze fatigue behaviors, such as yawning, a bowed head (that could indicate sleep state), and lying down on a table, using a behavior recognition algorithm. We fuse five facial features and behavioral postures to comprehensively determine the fatigue state of dispatchers. The results show that on the 300 W dataset, as well as a hand-crafted dataset, the inference time of the improved facial key point detection algorithm based on the retina–face model was 100 ms and that the normalized average error (NME) was 3.58. On our own dataset, the classification accuracy based the an Bi-LSTM-SVM adaptive enhancement algorithm model reached 97%. Video data of volunteers who carried out scheduling operations in the simulation laboratory were used for our experiments, and our multi-feature fusion fatigue detection algorithm showed an accuracy rate of 96.30% and a recall rate of 96.30% in fatigue classification, both of which were higher than those of existing single-feature detection methods. Our multi-feature fatigue detection method offers a potential solution for fatigue level classification in vital areas of the industry, such as in railway transportation.

Subject-adaptive Loose-fitting Smart Garment Platform for Human Activity Recognition

The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.

Abnormal sitting posture recognition based on multi-scale spatiotemporal features of skeleton graph

To fuse the spatial dimension, temporal dimension, and whole-body skeleton features of human abnormal sitting posture to improve the recognition accuracy, we propose an abnormal sitting posture recognition method based on the multi-scale spatiotemporal features of skeleton graph (ASPR) from the perspective of changes of human posture. Firstly, we build a human abnormal sitting posture dataset (HASP) with multidimensional features. Then, based on the structure of high-to-low resolution subnetworks, we use the spatiotemporal graph convolution module as the feature extraction unit A multiple scale spatiotemporal feature extraction model based on graph convolutional network (M2SGCN) is proposed. It is used to capture the spatiotemporal features. Next, a feature extraction model of local skeletal angles based on recurrent neural network is proposed to capture the change rule of skeletal angles of human sitting posture. Finally, we investigate the optimal parameters of ASPR by checking its performance with different human skeleton combination schemes and different features fusion coefficient. Experiment results show that ASPR achieves excellent performance compared with four state-of-the-art models and their combined models. ASPR shows an average recognition accuracy, a recall rate, an F1 score, and average time of 95.24%, 95.61%, 95.14%, and 7.094 ms, respectively.

Human Action Recognition and Posture Prediction

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in videos. They are both active research topics in computer vision community, which have attracted considerable attention from academia and industry. They are also the precondition for intelligent interaction and human-computer cooperation, and they help the machine perceive the external environment. In the past decade, tremendous progress has been made in the field, especially after the emergence of deep learning technologies. Hence, it is necessary to make a comprehensive review of recent developments. In this paper, firstly, we attempt to present the background, and then discuss research progresses.

Micro-macro damage, deterioration and cracking of heterogeneous composite rock masses with non-penetrating cracks under uniaxial compression

Rock masses with non-penetrating cracks are widely found in nature. Their mechanical behavior plays a key role in rock engineering applications. However, previous studies have concentrated on the single lithologic layer, and few studies have reported the crack coalescence mechanism in heterogeneous composite rock mass with non-penetrating cracks (HCRC). In this study, uniaxial compression tests were performed on HCRC by discrete element numerical simulation experiments. Through the uniaxial compression simulation tests, the stress–strain variation and acoustic emission (AE) characteristics of HCRC are revealed. The instability and failure characteristics, energy evolution law and crack coalescence process of HCRC are also studied. And the influence of the crack numbers and crack positions (in weak body or strong body) on the mechanical behavior and failure processes in heterogeneous composite rock mass is investigated. The results show that the mechanical properties of HCRC are not all weakened with the increase of the crack numbers. As the number of cracks increases, the failure modes of the HCRC changes from brittle failure to ductile failure. The number of cracks will regulate the relative relationship between the strength of the two bodies of heterogeneous composite rock masses, so the crack coalescence forms, instability forms and failure characteristics of the HCRC will also change accordingly. If the number of cracks in the weak body is large, the damage of the HCRC mainly occurs in the weak body. The strong body produces a rebound energy release to the weak body, accelerating the destruction of the weak body. At this time, the overall strength of the HCRC is low. Conversely, increasing the number of cracks in a strong body will weaken the strength of the strong body. At this time, the strength difference between the strong body and the weak body decreases. The HCRC shows that the overall bearing capacity is not lower but becomes stronger. The research results provide a theoretical reference for the stability and disaster prevention of jointed rock mass engineering.

Control Mouse using Hand Gesture and Voice

Abstract: In Human Computer Interaction (HCI), the finest invention is the mouse. Even though a wireless mouse or the Bluetooth mouse is in demand today, it still lacks in many fields like cost and power. This paper, therefore, puts forward a modern approach to regulate the mouse movements using a real-time camera with the help of the hand. Our System aims to improve the recognition of human hand postures in a Human Computer Interaction application, reduce the time spent computing and improve user comfort related to human hand postures. We have developed an application for computer mouse control. Based on the proposed algorithm and selected hand feature, the application has good time-based performance. The user finds it easier to operate the system due to the proposed hand postures in combination with the voice assistant. Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system.

The airflow distribution and aerosol diffusion rules in the negative pressure isolation ward

Negative pressure wards are significant in preventing the spread of infectious pathogens which play a crucial role in fighting against COVID-19. Owing to the negative pressure, contaminated air with pathogens is not able to flow from the wards to non-contaminated zones while fresh filtered air will be transported to the ward via the ventilation system. As airflow controlled by ventilation systems affects the motion of pathogens, for example, infectious aerosol particles, the ability of a negative pressure ward to reduce the risk of infection highly relies on an effective ventilation system. In this investigation, impacts of airflow patterns under various human postures and ventilation processes aerosols diffusion are analyzed via the computational fluid dynamics (CFD) simulation. According to the results, among three airflow patterns, the highest contaminant removal efficiency is 57% at 200 s with the top supply and bottom return mode; besides, in three postures, in the case that the patient is in a standing position, the contaminant removal efficiency is the highest. Furthermore, it is found that the best airflow scheme is a slit tuyere in the ward, with a top supply and side return mode and a sitting position for the patient. This study may provide a reference for the design of airflow in negative pressure isolation wards, control of contaminants, and prevention of viral infections, so as to ensure a good working and recovery environment for medical staff and patients.

Improving Postural Ergonomics during Human–Robot Collaboration Using Particle Swarm Optimization: A Study in Virtual Environment

Musculoskeletal disorders caused by poor work posture are a serious concern in the industry since they lead to absenteeism and medical leave from work. In the context of human–robot collaboration, this issue can be mitigated if collaborative robots support human workers to perform their tasks more ergonomically. In this work, we propose a method to optimize human posture during human–robot collaboration using the Particle Swarm Optimization (PSO) algorithm. Our approach involves assigning an appropriate location to the robot’s end-effector to minimize the distance between the optimized posture of the human and their current posture in the working space. To measure human posture, we use the Rapid Entire Body Assessment score (REBA) calculated from body joint angles captured by a Kinect camera. To validate the effectiveness of our proposed method, we conducted a user study with 20 participants in a virtual reality environment. The PSO algorithm could position the robot end-effector to the optimal position close to real time. Our results showed that our method could improve ergonomics by 66%, indicating its potential for use in human–robot collaborative applications.

Modelling Proper and Improper Sitting Posture of Computer Users Using Machine Vision for a Human–Computer Intelligent Interactive System during COVID-19

Human posture recognition is one of the most challenging tasks due to the variation in human appearance, changes in the background and illumination, additional noise in the frame, and diverse characteristics and amount of data generated. Aside from these, generating a high configuration for recognition of human body parts, occlusion, nearly identical parts of the body, variations of colors due to clothing, and other various factors make this task one of the hardest in computer vision. Therefore, these studies require high-computing devices and machines that could handle the computational load of this task. This study used a small-scale convolutional neural network and a smartphone built-in camera to recognize proper and improper sitting posture in a work-from-home setup. Aside from the recognition of body points, this study also utilized points’ distances and angles to help in recognition. Overall, the study was able to develop two objective datasets capturing the left and right side of the participants with the supervision and guidance of licensed physical therapists. The study shows accuracies of 85.18% and 92.07%, and kappas of 0.691 and 0.838, respectively. The system was developed, implemented, and tested in a work-from-home environment.

When vision loss is beyond cataract: a case of fatal neurodegenerative disease (P1-9.010)

<h3>Objective:</h3> NA <h3>Background:</h3> Creutzfeldt Jakob disease (CJD) is a rare and fatal neurodegenerative disorder of unclear etiology. Presentation include myoclonic jerks, visual disturbances, pyramidal/extrapyramidal signs and rapidly progressive cognitive decline. Visual symptoms are a common manifestation of CJD. Here we present a case of progressive vision loss initially attributed to cataract with eventual diagnosis of CJD. <h3>Design/Methods:</h3> Patient is a 75-year-old woman with history of osteoporosis, dyslipidemia, and arthritis presented to the emergency (ED) with bilateral vision loss and cognitive decline. Symptoms started 8 weeks prior presentation with progressive vision loss and difficulty recognizing color and shapes. She also had intermittent horizontal diplopia. Patient was initially evaluated by an ophthalmologist where vision loss was attributed to cataract and underwent bilateral cataract extraction with no improvement in vision. Vision loss worsened and she became unable to recognize familiar faces or identify objects, and had recurrent falls. She suffered visual hallucination, progressive abnormal body posturing, memory loss, dysphagia and severe dysarthria. On her exam she was noted to be mute, unable to follow commands, and had stimulus induced myoclonic jerks of limbs. CXR, urine analysis, urine and blood cultures, and urine drug screen were all unrevealing. Serum and CSF paraneoplastic panel were negative. EEG showed absence of the posterior dominant rhythm. Brain MRI demonstrated cortical ribbon sign and hyperintensities of basal ganglia (image 1). CSF Rt-Quic was indeterminate. Given the history, exam and MRI findings, she was diagnosed with Heidenhain variant of CJD and passed away shortly after transfer to inpatient hospice. <h3>Results:</h3> NA <h3>Conclusions:</h3> CJD can present with isolated visual symptoms that precede cognitive decline by weeks. Detailed history and careful exam are essential for early diagnosis as this will allow patients and their families to prepare for the expected disease course, consider goals of care, and possibly have palliative care if desired. <b>Disclosure:</b> Dr. Abusamra has nothing to disclose. Dr. Ali has nothing to disclose. Dr. Sudhakar has nothing to disclose.