A novel feature incremental learning method for sensor-based activity recognition
Recognizing activities of daily living is an important research topic for health monitoring and elderly care. However, most existing activity recognition models only work with static and pre-defined sensor configurations. Enabling an existing activity recognition model to adapt to the emergence of n...
Main Authors: | Hu, Chunyu, Chen, Yiqiang, Peng, Xiaohui, Yu, Han, Gao, Chenlong, Hu, Lisha |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/140790 |
Similar Items
-
Joint Feature Learning for Face Recognition
by: Lu, Jiwen, et al.
Published: (2016) -
Semantic-discriminative mixup for generalizable sensor-based cross-domain activity recognition
by: Lu, Wang, et al.
Published: (2023) -
Cross-position activity recognition with stratified transfer learning
by: Chen, Yiqiang, et al.
Published: (2020) -
Revisiting class-incremental learning with pre-trained models: generalizability and adaptivity are all you need
by: Zhou, Da-Wei, et al.
Published: (2024) -
Combining pose-invariant kinematic features and object context features for RGB-D action recognition
by: Ramanathan, Manoj, et al.
Published: (2019)