Intelligent Sensors for Human Motion Analysis

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...

Full description

Saved in:
Bibliographic Details
Other Authors: Krzeszowski, Tomasz (Editor), Świtoński, Adam (Editor), Kepski, Michal (Editor), Calafate, Carlos Tavares (Editor)
Format: Electronic Book Chapter
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:DOAB: download the publication
DOAB: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000naaaa2200000uu 4500
001 doab_20_500_12854_93177
005 20221025
003 oapen
006 m o d
007 cr|mn|---annan
008 20221025s2022 xx |||||o ||| 0|eng d
020 |a books978-3-0365-5074-9 
020 |a 9783036550732 
020 |a 9783036550749 
040 |a oapen  |c oapen 
024 7 |a 10.3390/books978-3-0365-5074-9  |c doi 
041 0 |a eng 
042 |a dc 
072 7 |a TB  |2 bicssc 
072 7 |a TBX  |2 bicssc 
100 1 |a Krzeszowski, Tomasz  |4 edt 
700 1 |a Świtoński, Adam  |4 edt 
700 1 |a Kepski, Michal  |4 edt 
700 1 |a Calafate, Carlos Tavares  |4 edt 
700 1 |a Krzeszowski, Tomasz  |4 oth 
700 1 |a Świtoński, Adam  |4 oth 
700 1 |a Kepski, Michal  |4 oth 
700 1 |a Calafate, Carlos Tavares  |4 oth 
245 1 0 |a Intelligent Sensors for Human Motion Analysis 
260 |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2022 
300 |a 1 electronic resource (382 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a 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. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a gait recognition 
653 |a biometrics 
653 |a regularized discriminant analysis 
653 |a particle swarm optimization 
653 |a grey wolf optimization 
653 |a whale optimization algorithm 
653 |a FMCW 
653 |a vital sign 
653 |a XGBoost 
653 |a MFCC 
653 |a COVID-19 
653 |a 3D human pose estimation 
653 |a deep learning 
653 |a generalization 
653 |a optical sensing principle 
653 |a modular sensing unit 
653 |a plantar pressure measurement 
653 |a gait parameters 
653 |a 3D human mesh reconstruction 
653 |a deep neural network 
653 |a motion capture 
653 |a neural networks 
653 |a reconstruction 
653 |a gap filling 
653 |a FFNN 
653 |a LSTM 
653 |a BILSTM 
653 |a GRU 
653 |a pose estimation 
653 |a movement tracking 
653 |a computer vision 
653 |a artificial intelligence 
653 |a markerless motion capture 
653 |a assessment 
653 |a kinematics 
653 |a development 
653 |a machine learning 
653 |a human action recognition 
653 |a features fusion 
653 |a features selection 
653 |a recognition 
653 |a fall risk detection 
653 |a balance 
653 |a Berg Balance Scale 
653 |a human tracking 
653 |a elderly 
653 |a telemedicine 
653 |a diagnosis 
653 |a precedence indicator 
653 |a knowledge measure 
653 |a fuzzy inference 
653 |a rule induction 
653 |a posture detection 
653 |a aggregation function 
653 |a markerless 
653 |a human motion analysis 
653 |a gait analysis 
653 |a data augmentation 
653 |a skeletal data 
653 |a time series classification 
653 |a EMG 
653 |a pattern recognition 
653 |a robot 
653 |a cyber-physical systems 
653 |a RGB-D sensors 
653 |a human motion modelling 
653 |a F-Formation 
653 |a Kinect v2 
653 |a Azure Kinect 
653 |a Zed 2i 
653 |a socially occupied space 
653 |a facial expression recognition 
653 |a facial landmarks 
653 |a action units 
653 |a convolutional neural networks 
653 |a graph convolutional networks 
653 |a artifact classification 
653 |a artifact detection 
653 |a anomaly detection 
653 |a 3D multi-person pose estimation 
653 |a absolute poses 
653 |a camera-centric coordinates 
653 |a deep-learning 
653 |a n/a 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/6073  |7 0  |z DOAB: download the publication 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/93177  |7 0  |z DOAB: description of the publication