Research Publications

Google Scholar: https://scholar.google.com/citations?user=sRBr2NQAAAAJ&hl=en

ResearchGate: https://www.researchgate.net/profile/Anindya_Das_Antar

ORCID: https://orcid.org/0000-0001-9912-8757

Dblp: https://dblp.org/pid/229/1935.html

Journal Articles (Refereed and Peer Reviewed)

[J6] Anindya Das Antar, Anna Kratz, and Nikola Banovic. Behavior Modeling Approach to Forecasting Physical Functioning of People with Multiple Sclerosis. In IMWUT, 2023.
[J5] Tahera Hossain*, Wanggang Shen*(Equal Contribution), Anindya Das Antar, Snehal Prabhudesai, Sozo Inoue, Xun Huan, and Nikola Banovic. A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning. In ACM Transactions on Computer-Human Interaction (TOCHI), 2023.
[J4] Md Atiqur Rahman Ahad, Masud Ahmed, Anindya Das Antar, Yasushi Makihara, Yasushi Yagi, “Action recognition using Kinematics Posture Feature on 3D skeleton joint locations“. Pattern Recognition Letters, 2021.
[J3] Masud Ahmed*, Anindya Das Antar*(Equal Contribution), Md Atiqur Rahman Ahad, “Static Postural Transition-based Technique and Efficient Feature Extraction for Sensor-based Activity Recognition“. Pattern Recognition Letters, 2021.
[J2] Anindya Das Antar, Masud Ahmed, and Md Atiqur Rahman Ahad, “Recognition of human locomotion on various transportations fusing smartphone sensors“. Pattern Recognition Letters, 2021.
[J1] Md Atiqur Rahman Ahad, Thanh Trung Ngo, Anindya Das Antar, Masud Ahmed, Tahera Hossain, Daigo Muramatsu, Yasushi Makihara, Sozo Inoue, and Yasushi Yagi, “Wearable Sensor-Based Gait Analysis for Age and Gender Estimation“. Sensors 2020, 20, 2424.

[C4] Snehal Prabhudesai, Ananya Kasi, Anmol Mansingh, Anindya Das Antar, Hua Shen, and Nikola Banovic. “Here the GPT made a choice, and every choice can be biased”: How Students Critically Engage with LLMs through End-User Auditing Activity“. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, Yokohama, Japan, 2025.
Featured as a news article “Bridging the AI literacy gap in higher education” in UM CSE
[C3] Anindya Das Antar, Somayeh Molaei, Yan-Ying Chen, Matthew L Lee, and Nikola Banovic. “VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-Making“. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST ’24). Association for Computing Machinery, Pittsburgh, PA, USA, 2024.
[C2] Anindya Das Antar, Masud Ahmed, and Md Atiqur Rahman Ahad. “Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review”. International Conference on Activity and Behavior Computing, USA, In conjunction with ICIEV&IVPR, 2019.
[C1] Masud Ahmed, Anindya Das Antar, and Md Atiqur Rahman Ahad . “An Approach to Classify Human Activities in Real-time from Smartphone Sensor Data“. International Conference on Activity and Behavior Computing, USA In conjunction with ICIEV&IVPR, 2019.

[W3] Masud Ahmed, Anindya Das Antar, Tahera Hossain, Sozo Inoue, and Md Atiqur Rahman Ahad.“POIDEN: Position and Orientation Independent Deep Ensemble Network for the Classification of Locomotion and Transportation Modes“, 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (UbiComp/ISWC), London, United Kingdom, 2019.
[W2] Md Atiqur Rahman Ahad, Anindya Das Antar, and Omar Shahid (2019). Vision-based Action Understanding for Assistive Healthcare: A Short Review. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1-11), 2019.
[W1] Anindya Das Antar, Masud Ahmed, Mohammad Shadman Ishrak, and Md Atiqur Rahman Ahad, “A Comparative Approach to Classification of Locomotion and Transportation Modes Using Smartphone Sensor Data“, 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2018 International Symposium on Wearable Computers (UbiComp/ISWC), Singapore, 2018. (Awarded in the Sussex-Huawei Locomotion (SHL) Challenge )

[WO1] Trung Ngo, Md Atiqur Rahman Ahad, Daigo Muramatsu, Yasushi Makihara, Yasushi Yagi, Sozo Inoue, Tahera Hossain, Anindya Das Antar, and Masud Ahmed, “OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender“, The 12th IAPR International Conference on Biometrics (ICB 2019), Crete, Greece, 2019.

[WP1] Sarah Jabbour, Trenton Chang, Anindya Das Antar, Joseph Peper, Insu Jang, Jiachen Liu, Jae-Won Chung, Shiqi He, Michael Wellman, Bryan Goodman, Elizabeth Bondi-Kelly, Kevin Samy, Rada Mihalcea, Mosharaf Chowhury, David Jurgens, and Lu Wang, “Evaluation Framework for AI Systems in “the Wild”,” University of Michigan, Ann Arbor, 2025.

[B12] Anindya Das Antar, Anna Kratz, and Nikola Banovic. “Incorporating Domain Knowledge into Human-centered Behavior Models for Time-series Forecasting“. presented at ACM UbiComp/ISWC 2023, Blog post published on Medium, July 2023.

[B1] Md Atiqur Rahman Ahad, Anindya Das Antar, Masud Ahmed, “IoT Sensor-Based Activity Recognition – Human Activity Recognition”, Publisher: Springer Nature Switzerland AG, ISBN 978-3-030-51379-5, 2020.

Chapter 1: Introduction on Sensor-Based Human Activity Analysis: Background
Chapter 2: Basic Structure for Human Activity Recognition Systems: Preprocessing and Segmentation
Chapter 3: Methodology of Activity Recognition: Features and Learning Methods
Chapter 4: Human Activity Recognition: Data Collection and Design Issues
Chapter 5: Devices and Application Tools for Activity Recognition: Sensor Deployment and Primary Concerns
Chapter 6: Sensor-Based Benchmark Datasets: Comparison and Analysis
Chapter 7: An Overview of Classification Issues in Sensor-Based Activity Recognition
Chapter 8: Performance Evaluation in Activity Classification: Factors to Consider
Chapter 9: Deep Learning for Sensor-Based Activity Recognition: Recent Trends
Chapter 10: Sensor-Based Human Activity Recognition: Challenges Ahead

[BC1] Anindya Das Antar, Masud Ahmed, and Md Atiqur Rahman Ahad, “Sensor-Based Human Activity and Behavior Computing“. Book: Vision, Sensing and Analytics: Integrative Approaches, Intelligent Systems Reference Library, volume 207, Springer, 2021.

B.Sc. Thesis: Anindya Das Antar and Masud Ahmed, “Sensor-based Activity Recognition Using Ensemble of Classifiers”, University of Dhaka, 2018.
Link: https://drive.google.com/file/d/1ZOfgzYO8WlHqmUB6yGqg0aWg7fzv86ct/view?usp=sharing
Supervisor: Prof. Md Atiqur Rahman Ahad, University of Dhaka; Osaka University ISIR http://aa.binbd.com/