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An Approach for Human posture recognition Based on the Fusion PSE-CNN-BiGRU Model

Computer Modeling in Engineering & Sciences 2024年第7期140卷 385-408页

作者： Xianghong Cao Xinyu Wang Xin Geng Donghui Wu Houru An School of Building and Environment Engineering Zhengzhou University of Light IndustryZhengzhou450000China

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 ***,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 ***,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 ***,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 ***,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’*** 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 *** 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 rec

关键词： posture recognition mediapipe BiGRU CNN ELU attention

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A posture recognition System for Rat Cyborg Automated Navigation

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Chinese Journal of Electronics 2018年第4期27卷 687-693页

作者： ZHANG Zhenchuan YANG Yingchun WU Zhaohui MA Qian College of Computer Science and Technology Zhejiang University College of Computer Science and Technology Qiushi Academy for Advanced Studies Zhejiang University

Rat cyborg is a rat which can receive cerebral commands and act as human wants. The navigation task is a widely used scenario in which the rat cyborg is required to walk along a specified path according to commands. Mostly the commands are given by human operators, but there’s a need for automatic control which is quite challenging since several modules need to be elaborately combined together to work as a whole, among which the monitoring system and the commands model are two major components. Previously, few attempts were made for behavior monitoring of rat cyborg. Existing works often implement an over-simple rat information extractor which is capable of giving only a few parameters of the rat cyborg, which greatly limited the performance of their automatic control accuracy. In response to this requirement,we develop a monitoring system that is capable of giving detailed motion parameters and accurate body postures of the rat cyborg. We explore the possibility of recognizing rat postures using shape information, which is described by the powerful Zernike moments. We propose several simple shape descriptors which are fast to compute and achieve acceptable performance.

关键词： Rat cyborg posture recognition Rat posture

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A Survey on Artificial Intelligence in posture recognition

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 35-82页

作者： Xiaoyan Jiang Zuojin Hu Shuihua Wang Yudong Zhang School of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210038China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK

Over the years,the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly *** purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years,such as scale-invariant feature transform,histogram of oriented gradients,support vectormachine(SVM),Gaussian mixturemodel,dynamic time warping,hiddenMarkovmodel(HMM),lightweight network,convolutional neural network(CNN).We also investigate improved methods of CNN,such as stacked hourglass networks,multi-stage pose estimation networks,convolutional posemachines,and high-resolution *** general process and datasets of posture recognition are analyzed and summarized,and several improved CNNmethods and threemain recognition techniques are *** addition,the applications of advanced neural networks in posture recognition,such as transfer learning,ensemble learning,graph neural networks,and explainable deep neural networks,are *** was found that CNN has achieved great success in posture recognition and is favored by ***,a more in-depth research is needed in feature extraction,information fusion,and other *** classification methods,HMM and SVM are the most widely used,and lightweight network gradually attracts the attention of *** addition,due to the lack of 3Dbenchmark data sets,data generation is a critical research direction.

关键词： posture recognition artificial intelligence machine learning deep neural network deep learning transfer learning feature extraction classification

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posture recognition for the Elderly Based on Wireless Sensor Networks 26th

Posture Recognition for the Elderly Based on Wireless Sensor...

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第26届中国控制与决策会议

作者： Ye Shang Zhigang Liu Jinkuan Wang School of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479937097

Wireless-sensor-network-based home monitoring for elderly activity behavior involves functional assessment of daily activities. In this paper, a mechanism for estimation of elderly postures through various sensors is presented. The data from the triaxial acceleration sensor placed on the waist and the pressure sensors placed under the insoles are consolidated by the coordinator to distinguish the posture of the elderly, such as standing, sitting, walking, and falling down. The developed system for monitoring and discrimination of the elderly postures is tested through the obtained data in the laboratory and the results are encouraging in distinguishing postures of the elderly.

关键词： Wireless sensor network posture recognition Elderly monitoring Pressure sensor and Triaxial acceleration sensor

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The posture of the recognition under the Numerous and Disorderly Background Anti-interference Method Research and Implementation

The Posture of the Recognition under the Numerous and Disord...

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第24届中国控制与决策会议

作者： Shuying Zhao, Jiwei Li, Mingxiu Lin, Feng Pan, Jie Chen College of Science and Engineering, Northeasten University, Shenyang 110004, China

Numerous and disorderly background is the background which has multiple moving targets. Specific moving targets detection and anti-interference technology are the key technologies in recognition of posture under the numerous and disorderly background. For the case that the identifying target is easily affected by other moving targets, an anti-interference method that can identify specific body gestures in numerous and disorderly scene is proposed. This method combines inter-frame difference method and LK optical flow method. Eventually this anti-interference method is realized and proved to be efficient at the platform of Kinect.

关键词： Numerous and disorderly background posture recognition Anti-interference Inter-frame difference method

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IoMT-Enabled Fusion-Based Model to Predict posture for Smart Healthcare Systems

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Computers, Materials & Continua 2022年第5期71卷 2579-2597页

作者： Taher M.Ghazal Mohammad Kamrul Hasan Siti Norul Huda Abdullah Khairul Azmi Abubakkar Mohammed A.M.Afifi Faculty of Information Science and Technology University Kebangsaan MalaysiaUKM43600SelangorMalaysia Skyline University College SharjahUnited Arab Emirates

Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring *** systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic *** collection of WS data and integration of that data for diagnostic purposes is a difficult *** paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition *** research is based on a case study in a health *** the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized *** a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic *** breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect *** paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance *** of WS data are examined extensively using active and iterative learning to identify errors in specific *** technology improves position detection accuracy,analysis duration,and error rate,regardless of user *** devices play a critical role in the management and treatment of *** can ensure that patients are provided with a unique treatment for their medical *** paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature *** first,the patients’walking patterns are tracked at various time *** characteristics are then evaluated in relation to the stored data using a random forest classifier.

关键词： Data fusion(DF) posture recognition healthcare systems(HCS) wearable sensor(WS) medical data errorless data fusion(EDF)

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