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The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the processing technology of human behavior data for learning, technology of expressing feature values of images, technology of extracting spatiotemporal information of images, technology of recognizing human posture, and technology of gesture recognition. Research on these technologies has recently been conducted using general deep learning network modeling of artificial intelligence technology, and excellent research results have been included in this edition.
Technology: general issues --- History of engineering & technology --- human action recognition --- graph convolution --- high-order feature --- spatio-temporal feature --- feature fusion --- dynamic gesture recognition --- multi-modalities network --- class regularization --- 3D-CNN --- spatiotemporal activations --- class-specific features --- Dynamic Hand Gesture Recognition --- human-computer interaction --- hand shape features --- pose estimation --- stacked hourglass network --- deep learning --- convolutional receptive field --- hand gesture recognition --- human–machine interface --- artificial intelligence --- feedforward neural networks --- spatio-temporal image formation --- human activity recognition --- fusion strategies --- transfer learning --- activity recognition --- data augmentation --- multi-person pose estimation --- partitioned centerpose network --- partition pose representation --- continuous hand gesture recognition --- gesture spotting --- gesture classification --- multi-modal features --- 3D skeletal --- CNN --- spatiotemporal feature --- embedded system --- real-time --- action recognition --- Long Short-Term Memory --- spatio–temporal differential --- n/a --- human-machine interface --- spatio-temporal differential
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The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems.
Technology: general issues --- History of engineering & technology --- gait recognition --- biometrics --- regularized discriminant analysis --- particle swarm optimization --- grey wolf optimization --- whale optimization algorithm --- FMCW --- vital sign --- XGBoost --- MFCC --- COVID-19 --- 3D human pose estimation --- deep learning --- generalization --- optical sensing principle --- modular sensing unit --- plantar pressure measurement --- gait parameters --- 3D human mesh reconstruction --- deep neural network --- motion capture --- neural networks --- reconstruction --- gap filling --- FFNN --- LSTM --- BILSTM --- GRU --- pose estimation --- movement tracking --- computer vision --- artificial intelligence --- markerless motion capture --- assessment --- kinematics --- development --- machine learning --- human action recognition --- features fusion --- features selection --- recognition --- fall risk detection --- balance --- Berg Balance Scale --- human tracking --- elderly --- telemedicine --- diagnosis --- precedence indicator --- knowledge measure --- fuzzy inference --- rule induction --- posture detection --- aggregation function --- markerless --- human motion analysis --- gait analysis --- data augmentation --- skeletal data --- time series classification --- EMG --- pattern recognition --- robot --- cyber-physical systems --- RGB-D sensors --- human motion modelling --- F-Formation --- Kinect v2 --- Azure Kinect --- Zed 2i --- socially occupied space --- facial expression recognition --- facial landmarks --- action units --- convolutional neural networks --- graph convolutional networks --- artifact classification --- artifact detection --- anomaly detection --- 3D multi-person pose estimation --- absolute poses --- camera-centric coordinates --- deep-learning --- n/a
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