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IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition.

, , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4 (3): 87:1-87:29 (2020)

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Can You See It?: Good, So We Can Sense It!, , , , , , and . GetMobile Mob. Comput. Commun., 25 (2): 38-42 (2021)Contrastive Predictive Coding for Human Activity Recognition., , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 5 (2): 65:1-65:26 (2021)Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition., , and . CoRR, (2022)Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition., , , and . CoRR, (2022)IMUTube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition., , , , , , and . CoRR, (2020)Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition., , and . Sensors, 24 (4): 1238 (February 2024)How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition?, , , and . ISWC, page 66-70. ACM, (2023)Clustering of Human Activities from Wearables by Adopting Nearest Neighbors., , and . ISWC, page 1-5. ACM, (2022)Investigating Self-supervised Learning for Predicting Stress and Stressors from Passive Sensing., , , , , , , and . ACIIW, page 1-8. IEEE, (2023)On the role of features in human activity recognition., , and . UbiComp, page 78-88. ACM, (2019)