Human activity recognition using multi-input CNN model with FFT spectrograms-Reference-Cited by-同舟云学术

Human activity recognition using multi-input CNN model with FFT spectrograms

Published:2020-09-10 Issue: Volume: Page:
ISSN:
Container-title:Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
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Author:

Yaguchi Kei¹,Ikarigawa Kazukiyo¹,Kawasaki Ryo¹,Miyazaki Wataru¹,Morikawa Yuki¹,Ito Chihiro¹,Shuzo Masaki¹,Maeda Eisaku¹

Affiliation:

1. Tokyo Denki University, Adachi-ku, Tokyo, Japan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3410530.3414342

Reference10 articles.

1. The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices

2. Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs.CV] Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs.CV]

3. Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Identity Mappings in Deep Residual Networks. arXiv:1603.05027 [cs.CV] Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Identity Mappings in Deep Residual Networks. arXiv:1603.05027 [cs.CV]

4. Application of CNN for Human Activity Recognition with FFT Spectrogram of Acceleration and Gyro Sensors

5. CNN for human activity recognition on small datasets of acceleration and gyro sensors using transfer learning

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