Human Activity Recognition Challenge

Md Atiqur Rahman Ahad, SMIEEE, is Professor, University of Dhaka (DU), and Specially Appointed Associate Professor, Osaka University. He did B.Sc. (Honors) & Masters (DU), Masters (University of New South Wales), and Ph.D. (Kyushu Institute of Technology) and is JSPS Postdoctoral Fellow and Visiting Researcher. His authored books are “Motion History Images for Action Recognition and Understanding,” in Springer; “Computer Vision and Action Recognition,” in Springer; “IoT-sensor based Activity Recognition,” in Springer (in press). He has been authoring/editing a few more books. He published 130+ peer-reviewed papers, 60+ keynote/invited talks, 25+ Awards/Recognitions. He is Editorial Board Member of Scientific Reports, Nature; Associate Editor of Frontiers in Computer Science; Editor of the International Journal of Affective Engineering; Editor-in-Chief: International Journal of Computer Vision & Signal Processing; General Chair: 9th ICIEV; 4th IVPR; 2nd ABC; Guest-Editor:Pattern Recognition Letters, Elsevier; JMUI, Springer; JHE, Hindawi; IJICIC; Member: OSA, ACM, IAPR.Paula Lago has a Ph.D. from Universidad de los Andes, Colombia. She received her Bachelor’s and Master’s degree in Software Engineering from the same university. From 2018 to 2020, she was a Postdoctoral Researcher at Kyushu Institute of Technology, Japan. Her current research is on how to improve the generalization of activity recognition in real-life settings taking advantage of data collected in controlled settings. In 2016, she was an invited researcher in the Informatics Laboratory of Grenoble, where she participated in smart home research in collaboration with INRIA. She has served as Reviewer for MDPI Sensors and ACM IMWUT journal and for several conferences. She is a Co-Organizer of the HASCA Workshop, held at Ubicomp yearly. She currently volunteers for ACM SIGCHI.Sozo INOUE is a Full 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 dataset for 4 months combined with smart meter data. Inoue has a Ph.D. in Engineering from Kyushu University in 2003. After completion of his degree, he was appointed as an Assistant Professor in the Faculty of Information Science and Electrical Engineering at Kyushu University, Japan. He then moved to the Research Department at Kyushu University Library in 2006. Since 2009, he is appointed as an Associate Professor in the Faculty of Engineering at Kyushu Institute of Technology, Japan, moved to the Graduate School of Life Science and Systems Engineering at Kyushu Institute of Technology in 2018, and appointed as a Full Professor from 2020. Meanwhile, he was a Guest Professor in Kyushu University, a Visiting Professor at Karlsruhe Institute of Technology, Germany, in 2014, a special Researcher at the Institute of Systems, Information Technologies and Nanotechnologies (ISIT) during 2015–2016, and a Guest Professor at the University of Los Andes in Colombia in 2019. He is a Technical Advisor of Team AIBOD Co. Ltd during 2017–2019, and a Guest Researcher at RIKEN Center for Advanced Intelligence Project (AIP) during 2017–2019. He is a 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).

• Chapter 1. Summary of the Cooking Activity Recognition Challenge.- Chapter 2. Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN.- Chapter 3. Let’s not make it complicated - Using only LightGBM and Naive Bayes for macro and micro activity recognition from a small dataset.- Chapter 4. Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data.- Chapter 5. SCAR-Net: Scalable ConvNet for Activity Recognition with multi-modal Sensor Data.- Chapter 6. Multi-Sampling Classifiers for the Cooking Activity Recognition Challenge.- Chapter 7. Multi-class Multi-label Classification for Cooking Activity Recognition.- Chapter 8. Cooking Activity Recognition with Convolutional LSTM using Multi-label Loss Function and Majority Vote.- Chapter 9. Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge.- Chapter 10. Cooking Activity Recognition with Varying Sampling Rates using Deep Convolutional GRU Framework.