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Full Description
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.
