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Sensor- and video-based activity and behavior computingproceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021) /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sensor- and video-based activity and behavior computingedited by Md Atiqur Rahman Ahad ... [et al.].
Reminder of title:	proceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021) /
remainder title:	ABC 2021
other author:	Ahad, Atiqur Rahman.
corporate name:
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xiv, 264 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Human activity recognitionCongresses.
Online resource:	https://doi.org/10.1007/978-981-19-0361-8
ISBN:	9789811903618$q(electronic bk.)

Sensor- and video-based activity and behavior computingproceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021) /
Sensor- and video-based activity and behavior computingproceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021) /[electronic resource] :ABC 2021edited by Md Atiqur Rahman Ahad ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xiv, 264 p. :ill., digital ;24 cm. - Smart innovation, systems and technologies,v. 2912190-3026 ;. - Smart innovation, systems and technologies ;v.12..

Chapter 1. Toward the Analysis of Office Worker's Mental Indicators Based on Activity Data -- Chapter 2. Open-Source Data Collection for Activity Studies at Scale -- Chapter 3. Using LUPI to Improve Complex Activity Recognition -- Chapter 4. Attempts toward Behavior Recognition of the Asian Black Bears using an Accelerometer -- Chapter 5. Using Human Body Capacitance Sensing to Monitor Leg Motion Dominated Activities with a Wrist Worn Device -- Chapter 6. BoxerSense: Punch Detection and Classification Using IMUs -- Chapter 7. FootbSense: Soccer Moves Identification Using a Single IMU -- Chapter 8. A data-driven approach for online pre-impact fall detection with wearable devices -- Chapter 9. Modeling Reminder System for Dementia by Reinforcement Learning -- Chapter 10. Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity? -- Chapter 11. Identification of Food Packaging Activity Using MoCap Sensor Data -- Chapter 12. Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features -- Chapter 13. Bento Packaging Activity Recognition Based on Statistical Features -- Chapter 14. Using k-Nearest-Neighbors Feature Selection for Activity Recognition -- Chapter 15. Bento Packaging Activity Recognition from Motion Capture Data -- Chapter 16. Bento Packaging Activity Recognition with Convolutional LSTM using Autocorrelation Function and Majority Vote -- Chapter 17. Summary of the Bento Packaging Activity Recognition Challenge.

This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 20-22 October 2021. The book includes works related to the field of vision- and sensor-based human action or activity and behavior analysis and recognition. It covers human activity recognition (HAR), action understanding, gait analysis, gesture recognition, behavior analysis, emotion, and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition-both in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.

ISBN: 9789811903618$q(electronic bk.)

Standard No.: 10.1007/978-981-19-0361-8doiSubjects--Topical Terms:

889259
Human activity recognition
--Congresses.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Sensor- and video-based activity and behavior computingproceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021) /
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505 0 $a Chapter 1. Toward the Analysis of Office Worker's Mental Indicators Based on Activity Data -- Chapter 2. Open-Source Data Collection for Activity Studies at Scale -- Chapter 3. Using LUPI to Improve Complex Activity Recognition -- Chapter 4. Attempts toward Behavior Recognition of the Asian Black Bears using an Accelerometer -- Chapter 5. Using Human Body Capacitance Sensing to Monitor Leg Motion Dominated Activities with a Wrist Worn Device -- Chapter 6. BoxerSense: Punch Detection and Classification Using IMUs -- Chapter 7. FootbSense: Soccer Moves Identification Using a Single IMU -- Chapter 8. A data-driven approach for online pre-impact fall detection with wearable devices -- Chapter 9. Modeling Reminder System for Dementia by Reinforcement Learning -- Chapter 10. Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity? -- Chapter 11. Identification of Food Packaging Activity Using MoCap Sensor Data -- Chapter 12. Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features -- Chapter 13. Bento Packaging Activity Recognition Based on Statistical Features -- Chapter 14. Using k-Nearest-Neighbors Feature Selection for Activity Recognition -- Chapter 15. Bento Packaging Activity Recognition from Motion Capture Data -- Chapter 16. Bento Packaging Activity Recognition with Convolutional LSTM using Autocorrelation Function and Majority Vote -- Chapter 17. Summary of the Bento Packaging Activity Recognition Challenge.
520 $a This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 20-22 October 2021. The book includes works related to the field of vision- and sensor-based human action or activity and behavior analysis and recognition. It covers human activity recognition (HAR), action understanding, gait analysis, gesture recognition, behavior analysis, emotion, and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition-both in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.
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856 4 0 $u https://doi.org/10.1007/978-981-19-0361-8
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