Robust Human Activity Recognition via Wearable Sensors Using Dynamic Gaussian Kernel Learning

During the past decade, deep convolutional neural networks (CNNs) have shown remarkable success in ubiquitous human activity recognition (HAR) scenarios. However, prior most works are static, which have to manually predefine a fixed kernel size before training, hence requiring time-consuming human i...

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Veröffentlicht in:	IEEE sensors journal 2024-03, Vol.24 (6), p.8265-8280
Hauptverfasser:	Wang, Shuaishuai, Sun, Yixiang, Sha, Yetong, Yang, Guangyu, Cheng, Dongzhou, Zhang, Lei, Wu, Hao, Song, Aiguo
Format:	Artikel
Sprache:	eng
Schlagworte:	Activity recognition Artificial neural networks Convolution convolutional neural networks (CNNs) dynamic Gaussian convolution (DgConv) Feature extraction Filtering theory Human activity recognition Kernel Neural networks sensor Sensors Training
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