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pro vyhledávání: '"Wang, Kevin I-Kai"'
Autor:
Ye, Xiaozhou, Wang, Kevin I-Kai
Human Activity Recognition (HAR) plays a crucial role in various applications such as human-computer interaction and healthcare monitoring. However, challenges persist in HAR models due to the data distribution differences between training and real-w
Externí odkaz:
http://arxiv.org/abs/2408.03353
Inertial Measurement Unit (IMU) sensors are widely employed for Human Activity Recognition (HAR) due to their portability, energy efficiency, and growing research interest. However, a significant challenge for IMU-HAR models is achieving robust gener
Externí odkaz:
http://arxiv.org/abs/2406.18569
Human Activity Recognition (HAR) is a cornerstone of ubiquitous computing, with promising applications in diverse fields such as health monitoring and ambient assisted living. Despite significant advancements, sensor-based HAR methods often operate u
Externí odkaz:
http://arxiv.org/abs/2403.15424
Autor:
Ye, Xiaozhou, Wang, Kevin I-Kai
Current research on human activity recognition (HAR) mainly assumes that training and testing data are drawn from the same distribution to achieve a generalised model, which means all the data are considered to be independent and identically distribu
Externí odkaz:
http://arxiv.org/abs/2403.15423
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most st
Externí odkaz:
http://arxiv.org/abs/2403.15422
Autor:
Ye, Xiaozhou, Wang, Kevin I-Kai
In Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed ($
Externí odkaz:
http://arxiv.org/abs/2403.17958
Autor:
Ye, Xiaozhou, Wang, Kevin I-Kai
In human activity recognition (HAR), the assumption that training and testing data are independent and identically distributed (i.i.d.) often fails, particularly in cross-user scenarios where data distributions vary significantly. This discrepancy hi
Externí odkaz:
http://arxiv.org/abs/2403.14682
Autor:
Ye, Xiaozhou, Wang, Kevin I-Kai
Publikováno v:
In Pattern Recognition December 2024 156
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