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.

Moving Human Posture Recognition Based on Joint Quaternion

Posture recognition plays an important role in many applications,such as security system and monitoring system.Joint quaternion combined with support vector machine(SVM) can solve the problem of moving human posture recognition.It is a simple and effective algorithm that only three joints are used as the feature points in the whole human skeleton.Using the quaternion of the three joints,a feature vector with five parameters in gait cycle is extracted.The efficiency of the proposed method is demonstrated through an experimental study,and walking and running postures can be distinguished accurately.

Research on Fall Detection Based on Improved Human Posture Estimation Algorithm

According to recent research statistics,approximately 30%of people who experienced falls are over the age of 65.Therefore,it is meaningful research to detect it in time and take appropriate measures when falling behavior occurs.In this paper,a fall detection model based on improved human posture estimation algorithm is proposed.The improved human posture estimation algorithm is implemented on the basis of Openpose.An im-proved strategy based on depthwise separable convolution combined with HDC structure is proposed.The depthwise separable convolution is used to replace the convolution neural network structure,which makes the network lightweight and reduces the redundant layer in the network.At the same time,in order to ensure that the image features are not lost and ensure the accuracy of detecting human joint points,HDC structure is introduced.Experiments show that the improved algorithm with HDC structure has higher accuracy in joint point detection.Then,human posture estimation is applied to fall detection research,and fall event modeling is carried out through fall feature extraction.The designed convolution neural network model is used to classify and distinguish falls.The experimental results show that our method achieves 98.53%,97.71%and 97.20%accuracy on three public fall detection data sets.Compared with the experimental results of other methods on the same data set,the model designed in this paper has a certain improvement in system accuracy.The sensitivity is also improved,which will reduce the error detection probability of the system.In addition,this paper also verifies the real-time performance of the model.Even if researchers are experimenting with low-level hardware,it can ensure a certain detection speed without too much delay.

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%.

Flexible and wearable BaTiO_(3)/polyacrylonitrile-based piezoelectric sensor for human posture monitoring

Tactile sensors are essential components of wearable electronic devices,but there are still various problems in terms of energy supply,flexibility and skin adaptability.In this paper,we report a self-powered flexible tactile sensor(FTS)mainly composed of a BaTiO_(3)/polyacrylonitrile/Ecoflex(BTO/PAN/Ecoflex)composite film,which can be used for dynamically monitoring human plantar pressure,posture and other physiological and motion parameters.Combining the synergistic piezoelectric properties of PAN and BTO,the output voltage/current of the BTO/PAN/Ecoflex composite film is 4.5/5.8 times that of the BTO/Ecoflex composite film,with maximum instantaneous power that can reach up to 3.375μW.Under the action of external pressure stress,the FTS can reach a normalized voltage sensitivity and voltage linearity of 0.54 V/N and 0.98,respectively.Furthermore,a human-machine interaction test system is built,which can display the stress changes of human body monitoring parts in real time according to voltage changes and different color assignments.The developed human-machine interaction test system provides a new idea for the diagnosis of flatfoot and other medical diseases.Hence,this work proposes new FTSs that use a BTO/PAN/Ecoflex composite film with high sensitivity and great output performance,thus exhibiting immense potential application prospects in medical research,personalized recognition and human-machine interaction.

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data is first separated from the foreground and background.Then,the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally,human posture nodes are extracted from each frame of the image sequence,which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.