Sensor- and Video-Based Activity and Behavior Computing : Proceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021) / edited by Md Atiqur Rahman Ahad, Sozo Inoue, Daniel Roggen, Kaori Fujinami.

Format:

Book

Contributor:

Ahad, Atiqur Rahman, editor.

Series:

Smart Innovation, Systems and Technologies, 2190-3026 ; 291

Language:

English

Subjects (All):

Computational intelligence.

Quantitative research.

Artificial intelligence.

Data protection.

Medical informatics.

Computational Intelligence.

Data Analysis and Big Data.

Artificial Intelligence.

Data and Information Security.

Health Informatics.

Local Subjects:

Computational Intelligence.

Data Analysis and Big Data.

Artificial Intelligence.

Data and Information Security.

Health Informatics.

Physical Description:

1 online resource (268 pages)

Edition:

1st ed. 2022.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2022.

Summary:

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. .

Contents:

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 fromMotion 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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Ahad, Atiqur Rahman Sensor- and Video-Based Activity and Behavior Computing

ISBN:

981-19-0361-1