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cs.CV2024
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer
Shuangjian Li, Tao Zhu, Mingxing Nie +3
Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields such as…
cs.CV2024
HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mamba
Shuangjian Li, Tao Zhu, Furong Duan +4
Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remain…
cs.CV2024
FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs
Qi Qiu, Tao Zhu, Furong Duan +3
Inertial Measurement Unit (IMU) sensors are widely employed for Human Activity Recognition (HAR) due to their portability, energy efficiency, and growing research interest. However…