CBR-ACE: Counting Human Exercise using Wi-Fi Beamforming Reports
Author:
Affiliation:
1. Graduate School of Information Science and Technology, Osaka University
2. Access Network Service Systems Laboratory, NTT Corporation
Publisher
Information Processing Society of Japan
Subject
General Computer Science
Link
https://www.jstage.jst.go.jp/article/ipsjjip/30/0/30_66/_pdf
Reference31 articles.
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3. [3] Dyo, V., Ellwood, S.A., MacDonald, D.W., Markham, A., Mascolo, C., Pásztor, B., Scellato, S., Trigoni, N., Wohlers, R. and Yousef, K.: Evolution and sustainability of a wildlife monitoring sensor network, ACM SenSys, pp.127-140 (2010).
4. [4] Grzeszick, R., Lenk, J.M., Rueda, F.M., Fink, G.A., Feldhorst, S. and ten Hompel, M.: Deep Neural Network based Human Activity Recognition for the Order Picking Process, Proc. 4th International Workshop on Sensor-based Activity Recognition and Interaction, iWOAR '17, No.Article 14, pp.1-6, Association for Computing Machinery (2017).
5. [5] Jiang, W. and Yin, Z.: Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks, Proc. 23rd ACM International Conference on Multimedia, MM '15, pp.1307-1310, Association for Computing Machinery (2015).
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