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Human activity recognition challenge

Ahad, Md Atiqur Rahman.

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Human activity recognition challenge

Record Type:	Electronic resources : Monograph/item
Title/Author:	Human activity recognition challenge/ edited by Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue.
other author:	Ahad, Md Atiqur Rahman.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xiv, 126 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Human activity recognition. -
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. - Singapore :Springer Singapore :2021. - xiv, 126 p. :ill., digital ;24 cm. - Smart innovation, systems and technologies,v.1992190-3018 ;. - Smart innovation, systems and technologies ;v.199..

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:

2002448
Human activity recognition.

LC Class. No.: TK7882.P7 / H86 2021

Dewey Class. No.: 006.4

Human activity recognition challenge
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245 0 0 $a Human activity recognition challenge $h [electronic resource] / $c edited by Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xiv, 126 p. : $b ill., digital ; $c 24 cm.
490 1 $a Smart innovation, systems and technologies, $x 2190-3018 ; $v 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 Human activity recognition. $3 2002448
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650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Health Informatics. $3 892928
650 2 4 $a Signal, Image and Speech Processing. $3 891073
700 1 $a Ahad, Md Atiqur Rahman. $3 3486273
700 1 $a Lago, Paula. $3 3487869
700 1 $a Inoue, Sozo. $3 3487870
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Smart innovation, systems and technologies ; $v v.199. $3 3487871
856 4 0 $u https://doi.org/10.1007/978-981-15-8269-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)