Sep 11, 2017 · We evaluate convolutional neuronal networks (CNNs) on a new large real-world multimodal dataset (RBK) as well as the PAMAP2 dataset.
Sep 15, 2017 · The recognition of basic physical activities (such as walk or cycle) and postures (such as sit or stand) is well researched, showing that good ...
Another study strived to address the problems of multimodal sensor fusion and normalization by developing a CNN-based method for sensor fusion [14] . Several ...
Sep 11, 2017 · Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs · Computer Science, Engineering. ACM Trans.
CNN-based sensor fusion techniques for multimodal human activity recognition ; Author: S. Münzner, P. Schmidt, A. Reiss, M. Hanselmann, R. Stiefelhagen, R.
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This review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human ...
On av- erage, our proposed sensor fusion-based models obtained an increment in HAR accuracy of about 9.14%, Precision of 7.30%, Recall of 8.70%, and F1 score of ...
May 30, 2024 · By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect ...
In this article, we propose a multi-level feature fusion technique for multimodal human activity recognition using multi-head Convolutional Neural Network (CNN)
Dec 9, 2023 · In this paper, we propose a novel method called deep wavelet convolutional neural networks (DWCNN) designed to learn features from the time-frequency domain ...