Sensor- and Video-Based Activity and Behavior Computing

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.

Md Atiqur Rahman Ahad, Senior Member of IEEE, Senior Member of OPTICA (formerly the OSA), is a Professor at University of Dhaka (DU), and a Specially Appointed Associate Professor at Osaka University. He studied at the University of Dhaka, University of New South Wales, and Kyushu Institute of Technology. He authored/edited 10+ books, e.g., "IoT-sensor based Activity Recognition"; "Motion History Images for Action Recognition and Understanding"; "Computer Vision and Action Recognition". He published ~200 journals, conference papers, book chapters, ~130 keynote/invited talks, ~40 Awards/Recognitions. He is an Editorial Board Member of Scientific Reports, Nature; Assoc. Editor of Frontiers in Computer Science; Editor of Int. Journal of Affective Engineering; Editor-in-Chief: IJCVSP; Guest-Editor of Pattern Recognition Letters, Elsevier; JMUI, Springer; JHE; IJICIC; Member: ACM, IAPR.  

Sozo Inoue, Ph.D., is Professor in Kyushu Institute of Technology, Japan. His research interests include human activity recognition with smart phones, and healthcare application of Web/pervasive/ubiquitous systems. Currently he is working on verification studies in real field applications, and collecting and providing a large-scale open dataset for activity recognition, such as a mobile accelerator dataset with about 35,000 activity data from more than 200 subjects, nurses' sensor data combined with 100 patients' sensor data and medical records, and 34 households' light sensor data set for 4 months combined with smart meter data. Inoue has a Ph.D. of Engineering from Kyushu University in 2003. After completion of his degree, he was appointed as Assistant Professor in the Faculty of Information Science and Electrical Engineering at the Kyushu University, Japan. He then moved to the Research Department at the Kyushu University Library in 2006. Since 2009, he is appointed as Associate Professor in the Faculty of