Sensor- and Video-Based Activity and Behavior Computing

This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 2022 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 recognitionboth in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.

Presents the research works in the field of behavior computing Gathers comprehensively the outcomes of the ACB 2021, held in October 2021 Offers a reference guide for researchers and practitioners in academia and industry

Auteur

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 Engineering at Kyushu Institute of Technology, Japan, and moved to Graduate School of Life Science and Systems Engineering at Kyushu Institute of Technology in 2018. Meanwhile, he was Guest Professor in Kyushu University, Visiting Professor at Karlsruhe Institute of Technology, Germany, in 2014, Special Researcher at Institute of Systems, Information Technologies and Nanotechnologies (ISIT) during 2015-2016, and Guest Professor at University of Los Andes in Colombia in 2019. He is Technical Advisor of Team AIBOD Co. Ltd since 2017, and Guest Researcher at RIKEN Center for Advanced Intelligence Project (AIP) since 2017. He is Member of the IEEE Computer Society, the ACM, the Information Processing Society of Japan (IPSJ), the Institute of Electronics, Information and Communication Engineers (IEICE), the Japan Society for Fuzzy Theory and Intelligent Informatics, the Japan Association for Medical Informatics (JAMI), and the Database Society of Japan (DBSJ).

Daniel Roggen, Ph.D., received the master's and Ph.D. degrees from the École Polytechnique Fédérale de Lausanne, Switzerland, in 2001 and 2005, respectively. He is currently Associate Professor with the Sensor Technology Research Centre, University of Sussex, where he leads the Wearable Technologies Laboratory and directs the Sensor Technology Research Centre. His research focuses on wearable and mobile computing, activity and context recognition, and intelligent embedded systems. He has established a number of recognized data sets for human activity recognition from wearable sensors, in particular the OPPORTUNITY dataset. He is Member of Task Force on Intelligent Cyber-Physical Systems of the IEEE Computational Intelligence Society.

Kaori Fujinami, Ph.D., received his B.S. and M.S. degrees in electrical engineering and his Ph.D. in computer science from Waseda University, Japan, in 1993, 1995, and 2005, respectively. From 1995 to 2003, he worked for Nippon Telegraph and Telephone Corporation (NTT) and NTT Comware Corporation as Software Engineer and Researcher. From 2005 to 2006, he was visiting Lecturer at Waseda University. From 2007 to 2017, he was Associate Professor at the Department of Computer and Information Sciences at Tokyo University of Agriculture and Technology (TUAT). In 2018, he became Professor at TUAT. His research interests are machine learning, activity recognition, human-computer interaction, and ubiquitous computing. He is Member of IPSJ, IEICE, and IEEE.

Contenu
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.