Accuracy evaluations of human moving pattern using communication quality based on machine learning

Wataru Kawakami; Kenji Kanai; Bo Wei; Jiro Katto

doi:10.1109/GCCE.2017.8229351

Accuracy evaluations of human moving pattern using communication quality based on machine learning

Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

研究成果: Conference contribution

3 引用（Scopus）

抜粋

In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.

元の言語	English
ホスト出版物のタイトル	2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017
出版者	Institute of Electrical and Electronics Engineers Inc.
ページ	1-2
ページ数	2
巻	2017-January
ISBN（電子版）	9781509040452
DOI	https://doi.org/10.1109/GCCE.2017.8229351
出版物ステータス	Published - 2017 12 19
イベント	6th IEEE Global Conference on Consumer Electronics, GCCE 2017 - Nagoya, Japan 継続期間: 2017 10 24 → 2017 10 27

Other

Other	6th IEEE Global Conference on Consumer Electronics, GCCE 2017
国	Japan
市	Nagoya
期間	17/10/24 → 17/10/27

フィンガープリント

ASJC Scopus subject areas

Media Technology
Instrumentation
Electrical and Electronic Engineering

これを引用

Kawakami, W., Kanai, K., Wei, B., & Katto, J. (2017). Accuracy evaluations of human moving pattern using communication quality based on machine learning. ： 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017 (巻 2017-January, pp. 1-2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2017.8229351

Kawakami, W, Kanai, K, Wei, B & Katto, J 2017, Accuracy evaluations of human moving pattern using communication quality based on machine learning. ： 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. 巻. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-2, 6th IEEE Global Conference on Consumer Electronics, GCCE 2017, Nagoya, Japan, 17/10/24. https://doi.org/10.1109/GCCE.2017.8229351

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abstract = "In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.",

keywords = "Communication quality, Human activity recognition, Machine learning, Mobile sensing",

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AB - In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.

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BT - 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017

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文献にアクセス

10.1109/GCCE.2017.8229351