AI in Healthcare based on Vision, Skeleton, and IoT Sensor

Md Atiqur Rahman Ahad, Ph.D. (SMIEEE, SMOPTICA) is an Associate Professor of Artificial Intelligence & Machine Learning (Champion, Research & Innovation), Dept. of Computer Science & Digital Technologies, University of East London. He is a Visiting Professor of Kyushu Institute of Technology, Japan; and a Visiting Professor of University of Brawijaya, Indonesia. He became a Professor at the University of Dhaka (DU) in 2018 and served as a specially appointed Associate Professor at Osaka University (2018~2022). He works on AI, ML, AMR, healthcare, well-being, vision, IoT, and biometrics. He studied at Kyushu Institute of Technology (PhD), University of New South Wales (MCompSc), and DU (BSc(Honors), MSc). He got 52 awards/recognitions (e.g., UGC Gold Medal). He is a TOP 2% researcher (as per Stanford University, 2022). He published 13 books (more to appear soon), 210+ peer-reviewed papers & book chapters. Ahad was invited as keynote/invited speaker 150+ times at different conferences/universities. He is an Editorial Board Member, Scientific Reports, Nature; Associate Editor, Frontiers in Computer Science; Editor, IJAC; Editor-in-Chief, IJCVSP; General Chair: 5 th  ABC,   10 th  ICIEV, 5 th  IVPR; Workshop Chair, 17 th  IEEE PiCom; Publication Chair, 2018 IEEE SMC; Vice Publication Co-chair & Vice Award Chair, 17 th  WC of IFSA; Guest Editor in Pattern Recognition Letters, JMUI, JHE, etc. He serves as reviewer of IEEE TPAMI, IEEE TBIOM, IJCV, Pattern Recognition, IEEE Sensors, SR Nature, JACIII, IEEE TAC, ACM IMWUT, PRL, CVIU, MVA. More: http://ahadVisionLab.com

 Abstract: Video, skeleton joint points are widely explored for human activity recognition (HAR). On the other hand, various sensors are engaged in human activity and behavior understanding. Vision-based human action or activity recognition approaches are based on RGB video sequences, depth maps, or skeleton data – taken from normal video cameras or depth cameras. On the other hand, sensor-based activity recognition methods are basically based on the data collected from wearable sensors having accelerometers, gyroscopes, and so on. There are numerous applications on HAR, however, healthcare, elderly support, and related applications become very important arenas with huge social and financial impacts. Due to the advent of various IoT sensors, it becomes more competitive as well as easier to explore different applications. The keynote will cover our works related to HAR approaches, highlighting healthcare perspectives and methods. The presentation will be based on the books and our recent works.

Reference:

1. Md Atiqur Rahman Ahad, Anindya Das Antar, and Masud Ahmed, “IoT Sensor-Based Activity Recognition - Human Activity Recognition”, Springer Nature Switzerland AG, 2021.
2. Md Atiqur Rahman Ahad, Upal Mahbub, and Tauhidur Rahman, “Contactless Human Activity Analysis, Springer Nature Switzerland AG, 2021.  
3. Md Atiqur Rahman Ahad and Upal Mahbub, "Action and Activity Recognition: Datasets and Challenges", Springer Nature Switzerland AG, 2022.
4. Md Atiqur Rahman Ahad, Sozo Inoue, Daniel Roggen, and Kaori Fujinami, "Sensor- and Video-based Activity and Behavior Computing", Springer Nature Switzerland AG, 2022.
5. Md Atiqur Rahman Ahad, “Motion History Images for Action Recognition and Understanding”, Springer, 2013.
6. Md Atiqur Rahman Ahad, “Computer Vision and Action Recognition: A Guide for Image Processing and Computer Vision Community for Action Understanding”, available in Springer, 2011.
7. Md Atiqur Rahman Ahad, Sozo Inoue, Daniel Roggen, and Kaori Fujinami, "Activity and Behavior Computing", Springer Nature Switzerland AG, 2021.
8. Md Atiqur Rahman Ahad, Paula Lago, and Sozo Inoue, "Human Activity Recognition Challenge", Springer Nature Switzerland AG, 2021.