22 citations · 23 across the 2 of their papers we have counts for
2 papers
eess.SP2022★ 1 cited
SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition
Rong Hu, Ling Chen, Shenghuan Miao +1
In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becom…
eess.SP2021★ 22 cited
SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition
Ling Chen, Yi Zhang, Shenghuan Miao +4
Unsupervised user adaptation aligns the feature distributions of the data from training users and the new user, so a well-trained wearable human activity recognition (WHAR) model c…