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024 7 $a10.1007/978-981-15-8269-1
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035   $a(DE-He213)978-981-15-8269-1
035   $a(PPN)252504356
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100   $a20210505d2021      uy             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aHuman activity recognition challenge /$fMd Atiqur Rahman Ahad, Paula Lago, Sozo Inoue, editors
205   $a1st ed. 2021.
210  1$aGateway East, Singapore :$cSpringer,$d[2021]
210  4$d©2021
215   $a1 online resource (XIV, 126 p. 43 illus., 31 illus. in color.) 
225 1 $aSmart Innovation, Systems and Technologies,$x2190-3018 ;$v199
311   $a981-15-8268-8 
327   $aChapter 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. .
330   $aThe book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems focus on recognizing either the complex label (macro-activity) or the small steps (micro-activities) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia).
410  0$aSmart Innovation, Systems and Technologies,$x2190-3018 ;$v199
606   $aHuman activity recognition
615  0$aHuman activity recognition.
676   $a006.3
702   $aAhad$b Md. Atiqur Rahman
702   $aLago$b Paula
702   $aInoue$b Sozo
801  0$bCaPaEBR
801  1$bCaPaEBR
801  2$bUtOrBLW
906   $aBOOK
912   $a9910483013203321
996   $aHuman activity recognition challenge$92854506
997   $aUNINA