If only we had more data!: Sensor-Based Human Activity Recognition in Challenging Scenarios
Author:
Affiliation:
1. School of Interactive Computing, College of Computing, Georgia Institute of Technology,Atlanta,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10149845/10150216/10150267.pdf?arnumber=10150267
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1. IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-08-22
2. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling;JMIR AI;2024-05-20
3. MaskCAE: Masked Convolutional AutoEncoder via Sensor Data Reconstruction for Self-Supervised Human Activity Recognition;IEEE Journal of Biomedical and Health Informatics;2024-05
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