Convolutional neural networks as means to identify apposite sensor combination for human activity recognition-Reference-Cited by-同舟云学术

Convolutional neural networks as means to identify apposite sensor combination for human activity recognition

Published:2018-09-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies
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Author:

Khan Muhammad U. S.¹,Abbas Assad²,Ali Mazhar¹,Jawad Muhammad³,Khan Samee U.⁴

Affiliation:

1. COMSATS University Islamabad, Abbottabad Campus, Pakistan

2. COMSATS University Islamabad, Islamabad Campus, Pakistan

3. COMSATS University Islamabad, Lahore Campus, Pakistan

4. Computer Engineering North Dakota State University

Funder

Higher Education Commission, Pakistan

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3278576.3278594

Reference14 articles.

1. Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

2. M. Chen Y. Ma Y. Li D. Wu Y. Zhang and C.-H. Youn (2017). Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems IEEE Communications Magazine 55 no. 1 2017 pp.54--61. M. Chen Y. Ma Y. Li D. Wu Y. Zhang and C.-H. Youn (2017). Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems IEEE Communications Magazine 55 no. 1 2017 pp.54--61.

3. An efficient big data collection in Body Area Networks

4. M. U. S. Khan A. Abbas M. Ali M. Jawad S. U. Khan K. Li and A. Zomaya. (2018). On the Correlation of Sensor Location and Human Activity Recognition in Body Area Networks (BANs). IEEE Systems Journal vol..12 no. 1 pp. 82--91. M. U. S. Khan A. Abbas M. Ali M. Jawad S. U. Khan K. Li and A. Zomaya. (2018). On the Correlation of Sensor Location and Human Activity Recognition in Body Area Networks (BANs). IEEE Systems Journal vol..12 no. 1 pp. 82--91.

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