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.

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.

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%.

Digital Evaluation of Sitting Posture Comfort in Human-vehicle System under Industry 4.0 Framework

Most of the previous studies on the vibration ride comfort of the human-vehicle system were focused only on one or two aspects of the investigation. A hybrid approach which integrates all kinds of investigation methods in real environment and virtual environment is described. The real experimental environment includes the WBV（whole body vibration） test, questionnaires for human subjective sensation and motion capture. The virtual experimental environment includes the theoretical calculation on simplified 5-DOF human body vibration model, the vibration simulation and analysis within ADAMS/VibrationTM module, and the digital human biomechanics and occupational health analysis in Jack software. While the real experimental environment provides realistic and accurate test results, it also serves as core and validation for the virtual experimental environment. The virtual experimental environment takes full advantages of current available vibration simulation and digital human modelling software, and makes it possible to evaluate the sitting posture comfort in a human-vehicle system with various human anthropometric parameters. How this digital evaluation system for car seat comfort design is fitted in the Industry 4.0 framework is also proposed.

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.

Human Body Modeling and Posture Simulating Based on 3D Surface Scan Data

This paper presents a new approach for modeling the human body by considering the motion state and the shape of whole body. The body model consists of a skeleton kinematic model and a surface model. The former is used to determine the posture of the body,and the latter is used to generate the body shape according to the given posture. The body surface is reconstructed with multi-segment B-spline surfaces based on the 3D scan data from a real human body.Using only a few joints parameters and the original surface scan data, the various body postures and the shape can be generated easily. The model has a strong potential of being used for ergonomic design,garment design, virtual reality environment, as well as creating human animation, etc.

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.

复杂人机共融场景中人体姿态识别及避碰策略综述

智能机器人与人类智慧的融合,即人机协作共融,已经实现了将机器人的机械优势和人类的高级认知能力集中于同一个工作架构之中,能够在复杂环境中协同作业,从而提高效率。针对复杂的人机共融场景,特别是机器人在诸如光线条件变化、背景干扰以及运动过程,对比总结了基于机器视觉的人体姿态识别方法和基于机器学习的避碰策略,详细比较各类方法的研究现状及应用,并探讨了基于深度学习的目标识别和避碰方法的发展及应用。

基于CenterNet的跑步姿态鉴别系统的设计

为了改善目前大众跑步姿势普遍不规范的现状,提出了一种基于CenterNet的跑步姿态鉴别系统。首先,通过截图、拍照的方式自制数据集,并对数据集进行清洗、标注和分析,消除数据无关信息与简化数据。其次,引入多尺度通道注意力机制与添加十字星变形卷积2种方式改进CenterNet算法模型,将动作图像转化为数字信息和特征向量,并以此为基础,利用KNN(K-nearest neighbors)算法对跑步姿态类型进行分类。最后,与经典模型方案进行对比,验证改进CenterNet算法鉴别系统的有效性。结果表明:改进的CenterNet模型的精确率与召回率都有所提升,其参数量与计算量降低。所提算法模型能够对大多数不良姿势作出及时、准确反馈,有效帮助跑步爱好者发现问题,从而改善跑步姿态、提高运动效率、预防伤病。

体感交互式智能型机械臂实验教学平台开发

针对传统机械臂成本高、操作难度大及人机交互受限等弊端,设计了一种以体感交互技术为媒介,兼具通用性强且便于携带的体感机械臂实验教学平台。该平台以ARM+人体姿态传感器+5个舵机+通信模块为核心,构建了五自由度智能机械臂架构;系统分为控制端和执行端,控制端通过人体姿态传感器采集并解析人体手臂姿态信息,执行端根据控制端采集到的信息驱动舵机完成机械臂跟随人体手臂姿态的对应操作。该教学平台已应用于“机器人课程设计”实验教学中,能满足学生综合运用传感器检测技术、嵌入式系统开发等实验需求。

基于神经网络的虚拟人姿态仿真方法

在制造业中,对工人工作姿势进行人体工程学评估对于预防肌肉骨骼疾病(MSD)是一项重要的工作。针对传统MSD评估方法存在的虚拟人作业姿态调整真实性不足、调整效率低等问题,提出一种虚拟人作业姿态的仿真方法。首先,建立了基于6D旋转的虚拟人骨架模型;其次,设计了一种基于编码器-解码器架构,使用循环神经网络(RNN)和注意力机制(AM)的虚拟人关节旋转生成模型,对虚拟人各关节姿态进行求解;然后,基于平衡性方法对虚拟人姿态生成进行迭代,生成虚拟人搬运姿态;最后,根据快速上肢分析(RULA)得分对姿态进行筛选,得到虚拟人最终姿态。所提方法生成的虚拟人作业姿态与作业人员实际操作姿态平均关节角度的误差为5.79°,RULA得分准确率为85.4%,表现出较好的实用性。

基于空间交叉卷积的轻量级人体姿态估计算法

针对改进轻量级OpenPose网络在预测阶段仍有较大参数量会降低模型推理速度,不利于在边缘设备部署的问题,提出一种基于改进卷积方法的人体姿态估计网络,使用空间交叉卷积来代替部分标准卷积,减少网络预测阶段的参数量。网络的输入为单目摄像头捕获的RGB图像,以MobileNetV3-Large为主干网络,并在其中加入了CBAM注意力模块,提取不同重要程度的空间和通道特征。获取图像特征后,送入两个分支中分别预测关键点位置和关键点组合关系。以空间交叉卷积代替两个分支中的部分标准卷积核,相对标准卷积能够减少80%的参数量。实验结果表明,相较于原方法,所提方法在精度下降较小的情况下,总参数量降低了22%,部署在CPU端的测试结果显示,速度能够达到6 FPS,提升了4倍。

强化先验骨架结构的轻量型高效人体姿态估计

为了更好地利用人体姿态关键点特有的分布属性,提出强化先验骨架结构的轻量型高效人体姿态估计方法.利用高分辨率网络较好地保留空间位置信息,为了进一步降低模型参数量,提出轻量倒残差模块.设计体位强化模块,利用全局空间特征和上下文信息强化躯干位置的先验信息及关键点之间的联系.针对多分辨率特征图像融合时,像素位置模糊、卷积核优化方向偏移导致关键点空间特征信息遗失的问题,提出方向强化卷积模块,利用躯干上关键点分布的水平和垂直方向特性,高效融合关键点先验分布.实验结果表明,利用该网络,可以高效地估计人体姿态.与基准网络相比,该模型在COCO测试集上的平均精度达到78.4,参数量减少了17.4×10^(6),兼顾精度与效率.

基于BlazePose与深度相机的体育评价系统

在目前的体育自动化测试和训练中,往往需要受试者穿戴多种传感器,影响了成绩的发挥,另一方面,仅依靠二维图像或深度相机的动作捕捉方法也难以全面、准确地描述人体运动姿态。论文针对上述问题,提出一种适用于常规体测项目的动作评价系统,首先将二维人体姿态估计模型BlazePose与深度相机结合,实现人体姿态关键点在三维空间中的准确定位,再通过卡尔曼滤波对关节点的轨迹进行平滑与估计,在此基础之上根据有限状态机的思想,以引体向上为例,完成了对多指标体测项目评价方法的设计,能够全面检测出各种犯规动作,实验表明整体正确检测率达到89%,处理速度达到20FPS,能够用于实际的辅助测试以及对个人的训练起到指导作用。

基于深度学习的人体姿态识别算法研究

人体姿态识别技术具有巨大的研究价值及广泛的应用前景。提出一种基于深度学习的人体姿态识别算法及能量序列改进Openpose算法,提取人体骨骼关键点,结合卷积神经网络(CNN)和长短期记忆网络(LSTM)捕获人体骨架数据空间与时间特征,得到最终的分类和识别结果,在数据集上验证。实验结果表明,提出的算法在人体姿态识别任务上取得了较好的性能。

老年人移动触屏人机交互操作影响因素研究

目的研究人机交互因素对老年人移动触屏交互操作绩效和用户感知的影响。方法采用组内组间混合设计的人因学实验,测试了40名受试者(老年和青年人各半)在三种键盘大小(半屏,3/4屏,全屏)、四种按键形状(正方形,0.618:1矩形,圆形,无形状)、两种手持姿势(单手操作,双手操作)及两种设备类型(智能手机,平板电脑)多因素交互下完成基础人机界面交互任务的操作绩效和用户体验感知水平。采用重复测量的方差分析处理实验数据。结果老年人操作绩效低于青年人操作绩效。随着键盘大小的增加,老年人操作绩效显著提高。全屏大小及双手操作下老年人操作绩效最佳。按键形状、手持姿势和设备类型对任务操作绩效和主观感知疲劳度有显著影响,键盘大小和按键形状、按键形状与手持姿势有显著交互作用。正方形按键获得最高用户偏好。结论实验结果为老年人移动触屏的界面设计和人机交互操作提供了参考依据。

ZnO/Ecoflex自驱动柔性压电传感器及其人体姿态监测微系统设计

压力传感器是可穿戴电子设备的重要组成部分,但在能量供应、灵活性和皮肤适应性方面仍存在各种问题。研制了一种主要由ZnO/Ecoflex复合薄膜构成的自驱动柔性压力传感器,并构建了人体姿态监测系统,可用于动态监测人体姿态信号和其他运动参数。在外部压力应力的作用下,所制备的传感器表现出优异的灵敏度(0.068 V/N)、良好的线性度(约0.98)、宽的测量范围(5~80 N)以及卓越的耐久性(超过10000次循环)。此外,基于制备的传感器设计了硬件电路,建立了人机交互测试系统,实现了对人体姿态信号的远程传输功能。因此,这项工作不仅开发了一种新型的无铅化自驱动压力传感器,还设计了人体姿态监测微系统,为信号处理和智能传感提供了新思路,在医学研究、个性化识别和人机交互方面具有重要的应用潜力。

灯心智启——基于AI技术的智能语音台灯设计与实现

为改善传统台灯功能单一与当前智能台灯不够智能化、人性化的问题,该文基于AI技术和STM32单片机设计一款融合温湿度传感器、超声波传感器、光照强度传感器和Wi-Fi模块等一系列模块,以及在照明基础上增加智能调光、检测疲劳、纠正坐姿等功能的智能化、人性化智能语音台灯。

无标记运动捕捉系统在临床步态分析上的研究进展

步态分析是临床研究的重要组成部分,传统的三维步态分析由于设备成本高、专业技术要求高等原因难以广泛应用。随着计算机视觉技术的发展,无标记运动捕捉系统的精确度得到极大提升,无需标记、成本较低的优势逐渐显现,并被应用于步态分析,但由于研究人员和临床医生缺乏对该系统的整体了解,使得该技术尚不能得到广泛推广,本综述拟对无标记运动捕捉系统在临床步态分析上的应用进展进行回顾,分析目前各项技术的优势和局限性,展望无标记运动捕捉系统在临床步态分析上的发展方向,为今后无标记运动捕捉系统在临床上的推广使用提供了研究思路。