國立虎尾科技大學 |

Human Activity Recognition Challenge

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Human Activity Recognition Challenge/ edited by Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue.
other author:	Ahad, Md Atiqur Rahman.
Description:	XIV, 126 p. 43 illus., 31 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-981-15-8269-1
ISBN:	9789811582691

Human Activity Recognition Challenge
Human Activity Recognition Challenge[electronic resource] /edited by Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue. - 1st ed. 2021. - XIV, 126 p. 43 illus., 31 illus. in color.online resource. - Smart Innovation, Systems and Technologies,1992190-3026 ;. - Smart Innovation, Systems and Technologies,37.

Chapter 1. Summary of the Cooking Activity Recognition Challenge -- Chapter 2. Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN -- Chapter 3. Let’s not make it complicated - Using only LightGBM and Naive Bayes for macro and micro activity recognition from a small dataset -- Chapter 4. Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data -- Chapter 5. SCAR-Net: Scalable ConvNet for Activity Recognition with multi-modal Sensor Data -- Chapter 6. Multi-Sampling Classifiers for the Cooking Activity Recognition Challenge -- Chapter 7. Multi-class Multi-label Classification for Cooking Activity Recognition -- Chapter 8. Cooking Activity Recognition with Convolutional LSTM using Multi-label Loss Function and Majority Vote -- Chapter 9. Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge -- Chapter 10. Cooking Activity Recognition with Varying Sampling Rates using Deep Convolutional GRU Framework. .

The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems focus on recognizing either the complex label (macro-activity) or the small steps (micro-activities) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia).

ISBN: 9789811582691

Standard No.: 10.1007/978-981-15-8269-1doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Human Activity Recognition Challenge
LDR:03188nam a22004095i 4500 001 1049523
003 DE-He213
005 20210921231348.0
007 cr nn 008mamaa
008 220103s2021 si | s |||| 0|eng d
020 $a 9789811582691 $9 978-981-15-8269-1
024 7 $a 10.1007/978-981-15-8269-1 $2 doi
035 $a 978-981-15-8269-1
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Human Activity Recognition Challenge $h [electronic resource] / $c edited by Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue.
250 $a 1st ed. 2021.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a XIV, 126 p. 43 illus., 31 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Smart Innovation, Systems and Technologies, $x 2190-3026 ; $v 199
505 0 $a Chapter 1. Summary of the Cooking Activity Recognition Challenge -- Chapter 2. Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN -- Chapter 3. Let’s not make it complicated - Using only LightGBM and Naive Bayes for macro and micro activity recognition from a small dataset -- Chapter 4. Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data -- Chapter 5. SCAR-Net: Scalable ConvNet for Activity Recognition with multi-modal Sensor Data -- Chapter 6. Multi-Sampling Classifiers for the Cooking Activity Recognition Challenge -- Chapter 7. Multi-class Multi-label Classification for Cooking Activity Recognition -- Chapter 8. Cooking Activity Recognition with Convolutional LSTM using Multi-label Loss Function and Majority Vote -- Chapter 9. Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge -- Chapter 10. Cooking Activity Recognition with Varying Sampling Rates using Deep Convolutional GRU Framework. .
520 $a The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems focus on recognizing either the complex label (macro-activity) or the small steps (micro-activities) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia).
650 0 $a Computational intelligence. $3 568984
650 0 $a Machine learning. $3 561253
650 0 $a Health informatics. $3 1064466
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Health Informatics. $3 593963
650 2 4 $a Signal, Image and Speech Processing. $3 670837
700 1 $a Ahad, Md Atiqur Rahman. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1348987
700 1 $a Lago, Paula. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1353673
700 1 $a Inoue, Sozo. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1285891
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811582684
776 0 8 $i Printed edition: $z 9789811582707
776 0 8 $i Printed edition: $z 9789811582714
830 0 $a Smart Innovation, Systems and Technologies, $x 2190-3018 ; $v 37 $3 1255196
856 4 0 $u https://doi.org/10.1007/978-981-15-8269-1
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)