Investigation of compensatory postures with videofluoromanometry in dysphagia patients

AIM: To investigate the effectiveness of head compensatory postures to ensure safe oropharyngeal transit. METHODS: A total of 321 dysphagia patients were enrolled and assessed with videofluoromanometry (VFM). The dysphagia patients were classified as follows: safe transit; penetration without aspiration; aspiration before, during or after swallowing; multiple aspirations and no transit. The patients with aspiration or no transit were tested with VFM to determine whether compensatory postures could correct their swallowing disorder. RESULTS: VFM revealed penetration without aspiration in 71 patients (22.1%); aspiration before swallowing in 17 patients (5.3%); aspiration during swallowing in 32 patients (10%); aspiration after swallowing in 21 patients (6.5%); multiple aspirations in six patients (1.9%); no transit in five patients (1.6%); and safe transit in 169 patients (52.6%). Compensatory postures guaranteed a safe transit in 66/75 (88%) patients with aspiration or no transit. A chin-down posture achieved a safe swallow in 42/75 (56%) patients, a head-turned posture in 19/75 (25.3%) and a hyperextended head posture in 5/75 (6.7%). The compensatory postures were not effective in 9/75 (12%) cases. CONCLUSION: VFM allows the speech-language therapist to choose the most effective compensatory posture without a trial-and-error process and check the effectiveness of the posture.

Gravitational effects on global hemodynamics in different postures:A closed-loop multiscale mathematical analysis

We present a novel methodology and strategy to predict pressures and flow rates in the global cardiovascular network in different postures varying from supine to upright. A closed-loop, multiscale mathematical model of the entire cardiovascular system (CVS) is developed through an integration of one-dimensional (1D) modeling of the large systemic arteries and veins, and zero-dimensional (0D) lumped-parameter modeling of the heart, the cardiac-pulmonary circulation, the cardiac and venous valves, as well as the microcirculation. A versatile junction model is proposed and incorporated into the 1D model to cope with splitting and/or merging flows across a multibranched junction, which is validated to be capable of estimating both subcritical and supercritical flows while ensuring the mass conservation and total pressure continuity. To model gravitational effects on global hemodynamics during postural change, a robust venous valve model is further established for the 1D venous flows and distributed throughout the entire venous network with consideration of its anatomically realistic numbers and locations. The present integrated model is proven to enable reasonable prediction of pressure and flow rate waveforms associated with cardiopulmonary circulation, systemic circulation in arteries and veins, as well as microcirculation within normal physiological ranges, particularly in mean venous pressures, which well match the in vivo measurements. Applications of the cardiovascular model at different postures demonstrate that gravity exerts remarkable influence on arterial and venous pressures, venous returns and cardiac outputs whereas venous pressures below the heart level show a specific correlation between central venous and hydrostatic pressures in right atrium and veins.

Quantification of the distribution of blood flow pressure with postures

At a different angle, this study analyzed the contour chart of blood flow pressure, extreme pressure and its position to quantify DBFP in thirteen different postures with gravity considered or not (G ≠ 0 or G = 0). The aim was to determine the suitable body positions, in which the postural model of a single vessel could be simplified to two-dimensional (2D) symmetrical one while only considering such factors as posture and gravity. Computational fluid dynamic simulations were performed. Numerical results demonstrated that the DBFP showed 2D axisymmetry at ±90° and three-dimensional (3D) asymmetry at any other posture with G ≠ 0, and 2D axisymmetrical one at any posture with G = 0. Therefore, modeling a vessel as a 2D model is feasible in space and at ±90° posture on earth. In addition, the maximum pressure occurred between the inlet and the middle of the vessel, and its position variation mainly happened in the range of 0° - 15°. For a single vessel, this study provides the first theoretical evidence for cardiovascular modeling in microgravity and may help guide the researchers in designing defense devices for astronauts or patients clinically.

Impact forces acting on the human feet during landing in two typical postures

A proper landing posture is significant to the reduction of both the im-pact force acting on the human body and the injury at landing.In this paper theimpact force acting on human feet is studied.The subjects were 3 maleparachuters.The experiments were performed by means of high-speed photography and amotor analyzer.The experimental results are as follows:(1)When the subjectjumped from two platforms 1.0m and 1.5m in height,a vertical impact force onthe feet in half-squat posture was larger than in side spin posture.(2)When thesubject jumped from the platform 1.0m high,the feet gained a horizontal impactforce in the half-squat posture,larger than in the side spin posture.When thesubject jumped from the platform 1.5m high,the horizontal impact force pro-duced by both of the above-mentioned postures were just the same,which needsfurther research.(3)In terms of reducing the impact force acting on the feet,theside spin posture is better than the half-squat posture.

Numerical Study on Neck Injury Under Different Postures in Vehicle Side Impact

Neck injury is a severe problem in traffic accidents.While most studies are focused on the neck injury in rear and front impacts,few are conducted in side impact.This study focuses on the difference of neck injury under different postures and the difference of 7 cervical vertebras under the same posture using the method of prescribed structure motion（PSM）.The analytical results show that the maximum changes of mean force and mean moment of 7 cervical vertebras under 8 different postures are 20% and 47% respectively.The variation of each cervical vertebra is different under different neck postures.Up cervical vertebras （C1-C4） and low cervical vertebras （C5-C7） suffer different forces and moments under the same neck posture.Generally speaking,No.6 （neck right leaning 40°） is the posture with lowest neck injury risk.

Synergistic control of soft robotic hands for human-like grasp postures

Although significant advances in the design of soft robotic hands have been made to mimic the structure of the human hands,there are great challenges to control them for coordinated and human-like postures.Based on the principle of postural synergies in the human hand,we present a synergistic approach for coordinated control of a soft robotic hand to replicate the human-like grasp postures.To this end,we firstly develop a kinematic model to describe the control variables and the various postures of the soft robotic hand.Based on the postural synergies,we use the developed model and Principal Component Analysis(PCA)method to describe the various postures of the soft robotic hand in a low-dimensional space formed by the synergies of actuator motions.Therefore,the coordinates of these synergies can be used as low-dimensional control inputs for the soft robotic hand with a higher-dimensional postural space.Finally,we establish an experimental platform on a customized soft robotic hand with6 pneumatical actuators to verify the effectiveness of the development.Experimental results demonstrate that with only a 2-dimensional control input,the soft robotic hand can reliably replicate 30 grasp postures in the Feix taxonomy of the human hand.

Therapeutic observation of manipulation plus exercise therapy in treating upper crossed syndrome postures of primary school students

Objective:To observe the therapeutic efficacy of sinew-bone balancing manipulation plus exercise therapy in treating postures of primary school students with upper crossed syndrome(UCS).Methods:Sixty pupils with UCS were divided into an exercise group and a combination group using the random number table method,with 30 cases in each group.The combination group received treatments of sinew-bone balancing manipulation plus exercise therapy,while the exercise group received exercise therapy alone.The two groups received interventions once every other day,for a total of 1 month.The sagittal static posture assessment total score,forward head angle(FHA)and forward shoulder angle(FSA)were compared before and after treatment;the sagittal static assessment total score,FHA and FSA were compared between the exercise group and the combination group.Results:Before treatment,there were no significant differences comparing the sagittal static posture assessment total score,FHA and FSA between the two groups(all P>0.05);after treatment,the sagittal static posture assessment total score,FHA and FSA decreased in the two groups,with intra-group statistical significance(all P<0.01),and were lower in the combination group than in the exercise group,with inter-group statistical significance(all P<0.01).Conclusion:Sinew-bone balancing manipulation plus exercise therapy can notably improve the FHA and FSA and reduce the sagittal static posture total score in pupils with UCS,so as to correct the bad postures and adjust UCS physique.It can produce more significant efficacy compared with exercise therapy alone.

An Approach for Human Posture Recognition Based on the Fusion PSE-CNN-BiGRU Model

This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.

A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns.Consequently,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and comfort.This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone right.The study involves 10 participants under two conditions:with and without blankets.Initially,the database is normalized into a video frame.The subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 model.In the third phase,the extracted features are utilized for classification.The fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal datasets.Finally,the fused vectors are passed to machine learning classifiers for final classification.The dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the blanket.Moreover,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.

Fusion of Convolutional Self-Attention and Cross-Dimensional Feature Transformationfor Human Posture Estimation

Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving highaccuracy in key point localization,which is crucial for intelligent applications,contradicts the lowdetection accuracy of human posture detection models in practical scenarios.To address this issue,a human pose estimation network called AT-HRNet has been proposed,which combines convolu-tional self-attention and cross-dimensional feature transformation.AT-HRNet captures significantfeature information from various regions in an adaptive manner,aggregating them through convolu-tional operations within the local receptive domain.The residual structures TripNeck and Trip-Block of the high-resolution network are designed to further refine the key point locations,wherethe attention weight is adjusted by a cross-dimensional interaction to obtain more features.To vali-date the effectiveness of this network,AT-HRNet was evaluated using the COCO2017 dataset.Theresults show that AT-HRNet outperforms HRNet by improving 3.2%in mAP,4.0%in AP75,and3.9%in AP^(M).This suggests that AT-HRNet can offer more beneficial solutions for human posture estimation.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

Trajectory compensation for multi-robot coordinated lifting system considering elastic catenary of the rope

The multi-robot coordinated lifting system is an unconstrained system with a rigid and flexible coupling.The deformation of the flexible rope causes errors in the movement trajectory of the lifting system.Based on the kinematic and dynamic analysis of the lifting system,the elastic catenary mod-el considering the elasticity and mass of the flexible rope is established,and the effect of the deform-ation of the flexible rope on the position and posture of the suspended object is analyzed.According to the deformation of flexible rope,a real-time trajectory compensation method is proposed based on the compensation principle of position and posture.Under the lifting task of the low-speed move-ment,this is compared with that of the system which neglects the deformation of the flexible rope.The trajectoy of the lifting system considering the deformation of flexible rope.The results show that the mass and elasticity of the flexible rope can not be neglected.Meanwhile,the proposed trajectory compensation method can improve the movement accuracy of the lifting system,which verifies the ef-fectiveness of this compensation method.The research results provide the basis for trajectory plan-ning and coordinated control of the lifting system。

Handlers’ Musculoskeletal Disorders in an Airline Operating Company in Conakry (Guinea)

Introduction: Musculoskeletal disorders are disorders of the musculoskeletal system related to work. The objective of this study was to musculoskeletal disorders among SOGEAC handlers. Methodology: This was a descriptive and analytic cross-sectional study over 6 months from November 2021 to April 2022. Results: We collected 110 handlers during the study. All of our population had postural constraints and were men. The average age of workers was 39.2 years. 93 handlers or 84.5% did manual handling, and 79% or 71.8% were baggage handlers. Regarding training, 88.2% or 97 handlers were trained, and 89.1% did alternating work. The standing posture was the most adopted with 89.1% followed by the kneeling position with 66.4%. 89.1% carried weights at work. 80 handlers or 73% had developed musculoskeletal disorders and the lumbar seat was found at 70%. The analysis of factors that may influence the occurrence of musculoskeletal disorders did not find any statistically significant relation. Conclusion: Our study shows that musculoskeletal disorders are an occupational health problem and recommends better compliance with occupational safety and health instructions.

Advancements in Functional Magnetic Resonance Imaging for Persistent Postural-Perceptual Dizziness

Persistent postural-perceptual dizziness, defined in 2017, is a chronic functional vestibular disorder. Which is characterized by persistent dizziness, unsteadiness, and/or non-spinning vertigo. However, the exact mechanisms remain unclear. In recent years, FMRI studies have provided key insights into the pathogenesis of PPPD. This review summarized functional imaging studies of persistent postural dizziness and its predecessors in recent years and found changes in the activity and functional connectivity of important areas of visual processing, multisensory vestibular and spatial cognition in patients with PPPD. In addition, factors such as stimulation mode, personality traits, mental comorbidities and external vestibular lesions have important effects on brain functional activities and connectivity patterns, and further stratified studies on these factors are needed in the future to further clarify and draw exact conclusions on the pathological mechanism of PPPD.

Computer Vision-Based Human Body Posture Correction System

With the development of technology and the progress of life,more and more people,regardless of entertainment,learning,or work,cannot do without computer desks and cannot put down their mobile phones.Due to prolonged sitting and often neglecting the importance of posture,incorrect posture can often lead to health problems such as hunchback,lumbar muscle strain,and shoulder and neck pain over time.To address this issue,we designed a computer vision-based human body posture detection system.The system utilizes YOLOv8 technology to accurately locate key points of the human body skeleton,and then analyzes the coordinate positions and depth information of these key points to establish a criterion for distinguishing different postures.With the assistance of an SVM classifier,the system achieves an average recognition rate of 95%.Finally,we successfully deployed the posture detection system on Raspberry Pi hardware and conducted extensive testing.The test results demonstrate that the system can effectively detect various postures and provide real-time reminders to users to correct poor posture,demonstrating good practicality and stability.

Gesture Recognition Based on Time-of-Flight Sensor and Residual Neural Network

With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.

Early‐onset scoliosis in children aged 4–7 years in Nanjing,China:A cross‐sectional study

Background:This study aimed to investigate the potential variance in the prevalence of early‐onset scoliosis among children aged 4–7 years and analyze the influencing factors.The goal was to establish a crucial reference point for monitoring and evaluating spinal curvature development in preschoolers,ultimately to reduce the occurrence of adverse health outcomes.Methods:Children aged 4–7 years within the main urban area of Nanjing were selected using a stratified random sampling method.A team of four senior therapists conducted screenings for spinal curvature among children using visual inspection,the Adams forward bending test,and an electronic scoliometer to measure the angle of trunk rotation(ATR)and identify children displaying signs of scoliosis.Children with suspected scoliosis in the initial screening underwent X‐ray Cobb angle assessment for confirmation.The prevalence of early‐onset scoliosis was then determined from the screening results.R version 4.2.0 software was used to analyze the factors associated with scoliosis among children using partial least squares structural equation modeling.Results:A total of 2281 children were included in this study,consisting of 1211 boys and 1070 girls,with a mean age of 5.44±0.81 years(ranging from 4 to 7 years).Among them,7.58%exhibited positive signs of scoliosis,5.87%had early‐onset scoliosis,and the positive predictive value was 77.5%.Significant differences in ATR were observed among children in different age groups(Kruskal–Wallis=15,p=0.0104)and by sex(t=3.17,p=0.00153).Significant variations in ATR were noted in children with scoliosis(t=−22.7,p<0.001),with a cutoff at ATR=4.5°,and auxiliary values of 0.947 and 0.990.Children diagnosed with early‐onset scoliosis generally exhibited lower body mass index values,with a statistically significant difference(t=2.99,p=0.003).Conclusions:Using visual inspection,the Adams test,and an electronic scoliometer to measure the ATR,the present triad method is more sensitive for early scoliosis screening in children with abnormal posture aged 4–7 years.A full spine X‐ray is advised in children with an ATR over 4.5°and poor posture.

On Dinosaur Reconstruction: Posture of Dinosaurs

Dinosaurs due to their diverse species and peculiar forms have drawn the interest of both artists and scientists. One way to unlock the unknown life of dinosaurs is to reconstruct dinosaurs through drawings, computer animations or sculptures. Following the Introduction on “Dinosaur Reconstruction” by the present authors, where important Paleontological knowledge was presented, the next step is to examine some specific information along with necessary details for dinosaur reconstruction. The first and basic step to be taken for a reconstruction is the posture of the animal;this is the theme of the current paper. Dinosaurs would move either bipedally or quadrupedally depending on their kind and body construction. Based on the available literature, various issues in relation to the posture of an animal at different instances are examined. These are: postures of bipedal dinosaurs during walking, observation of living bipedal animals, postures of quadruped dinosaurs during walking, feeding styles, and dinosaur tails. Theropods had a locomotor behavior like modern birds, with the step width increasing when the animals decreased speed. The general posture and movement of quadrupeds and especially sauropods, remains a subject of great and much controversy. Some scientists believe that sauropod necks were generally held in a neutral or undeflected state during most of the time, while others believe that sauropod necks behaved like all present-day amniote with the mid-cervical region held nearly vertical. Also, there are indications that dinosaurs usually held their tails above ground. For all dinosaurs, the long tail was acting as a counterbalance to the head and body. As a validating example, the case of Amargasaurus is investigated with the help of a model, where the various positions of the animal are examined. A certain posture was chosen for a full-size steel and concrete reconstruction based on the features of the animal.