Sensor based dynamic hand gesture recognition by pairnet

Yun Jie Jhang; Yen Cheng Chu; Tsung Ming Tai; Wen Jyi Hwang; Po Wen Cheng; Cheng Kuang Lee

doi:10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

Sensor based dynamic hand gesture recognition by pairnet

Yun Jie Jhang
, Yen Cheng Chu
, Tsung Ming Tai
, Wen Jyi Hwang
, Po Wen Cheng
, Cheng Kuang Lee

資訊工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章

9 引文斯高帕斯（Scopus）

摘要

This paper presents a novel feedforward neural network for sensor-based dynamic hand gesture recognition. The algorithm, termed PairNet, is capable of carrying out accurate gesture spotting for the sensory data produced by basic accelerators and gyroscopes, which are commonly deployed in internet of things devices. The gesture classification outcomes are then obtained from the spotting results by the Maximum A Posteriori (MAP) estimation. To illustrate the effectiveness of the proposed algorithm, a prototype system based on a mobile phone has been implemented. Experimental results reveal that, while attaining realtime operations, the proposed algorithm has superior accuracy over existing sensor-based counterparts for hand gesture recognition.

原文	英語
主出版物標題	Proceedings - 2019 IEEE International Congress on Cybermatics
主出版物子標題	12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	994-1001
頁數	8
ISBN（電子）	9781728129808
DOIs	https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174
出版狀態	已發佈 - 2019 7月
事件	12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 - Atlanta, 美国持續時間: 2019 7月 14 → 2019 7月 17

出版系列

名字	Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019

會議

會議	12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
國家/地區	美国
城市	Atlanta
期間	2019/07/14 → 2019/07/17

UN SDG

此研究成果有助於以下永續發展目標

SDG 7 可負擔的潔淨能源

ASJC Scopus subject areas

人工智慧
電腦網路與通信
可再生能源、永續發展與環境
硬體和架構
資訊系統與管理
安全、風險、可靠性和品質
通訊

存取文件

10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

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引用此

Jhang, Y. J., Chu, Y. C., Tai, T. M., Hwang, W. J., Cheng, P. W., & Lee, C. K. (2019). Sensor based dynamic hand gesture recognition by pairnet. 於 Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 (頁 994-1001). 文章 8875280 (Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

Sensor based dynamic hand gesture recognition by pairnet. / Jhang, Yun Jie; Chu, Yen Cheng; Tai, Tsung Ming 等.
Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 994-1001 8875280 (Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019).

研究成果: 書貢獻/報告類型 › 會議論文篇章

Jhang, YJ, Chu, YC, Tai, TM, Hwang, WJ, Cheng, PW & Lee, CK 2019, Sensor based dynamic hand gesture recognition by pairnet. 於 Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019., 8875280, Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019, Institute of Electrical and Electronics Engineers Inc., 頁 994-1001, 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019, Atlanta, 美国, 2019/07/14. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

Jhang YJ, Chu YC, Tai TM, Hwang WJ, Cheng PW, Lee CK. Sensor based dynamic hand gesture recognition by pairnet. 於 Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 994-1001. 8875280. (Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019). doi: 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

Jhang, Yun Jie ; Chu, Yen Cheng ; Tai, Tsung Ming 等. / Sensor based dynamic hand gesture recognition by pairnet. Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 頁 994-1001 (Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019).

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title = "Sensor based dynamic hand gesture recognition by pairnet",

abstract = "This paper presents a novel feedforward neural network for sensor-based dynamic hand gesture recognition. The algorithm, termed PairNet, is capable of carrying out accurate gesture spotting for the sensory data produced by basic accelerators and gyroscopes, which are commonly deployed in internet of things devices. The gesture classification outcomes are then obtained from the spotting results by the Maximum A Posteriori (MAP) estimation. To illustrate the effectiveness of the proposed algorithm, a prototype system based on a mobile phone has been implemented. Experimental results reveal that, while attaining realtime operations, the proposed algorithm has superior accuracy over existing sensor-based counterparts for hand gesture recognition.",

keywords = "Continuous hand gesture recognition, Convolutional neural networks, Human machine interface",

author = "Jhang, \{Yun Jie\} and Chu, \{Yen Cheng\} and Tai, \{Tsung Ming\} and Hwang, \{Wen Jyi\} and Cheng, \{Po Wen\} and Lee, \{Cheng Kuang\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 ; Conference date: 14-07-2019 Through 17-07-2019",

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AU - Chu, Yen Cheng

AU - Tai, Tsung Ming

AU - Hwang, Wen Jyi

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AU - Lee, Cheng Kuang

PY - 2019/7

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N2 - This paper presents a novel feedforward neural network for sensor-based dynamic hand gesture recognition. The algorithm, termed PairNet, is capable of carrying out accurate gesture spotting for the sensory data produced by basic accelerators and gyroscopes, which are commonly deployed in internet of things devices. The gesture classification outcomes are then obtained from the spotting results by the Maximum A Posteriori (MAP) estimation. To illustrate the effectiveness of the proposed algorithm, a prototype system based on a mobile phone has been implemented. Experimental results reveal that, while attaining realtime operations, the proposed algorithm has superior accuracy over existing sensor-based counterparts for hand gesture recognition.

AB - This paper presents a novel feedforward neural network for sensor-based dynamic hand gesture recognition. The algorithm, termed PairNet, is capable of carrying out accurate gesture spotting for the sensory data produced by basic accelerators and gyroscopes, which are commonly deployed in internet of things devices. The gesture classification outcomes are then obtained from the spotting results by the Maximum A Posteriori (MAP) estimation. To illustrate the effectiveness of the proposed algorithm, a prototype system based on a mobile phone has been implemented. Experimental results reveal that, while attaining realtime operations, the proposed algorithm has superior accuracy over existing sensor-based counterparts for hand gesture recognition.

KW - Continuous hand gesture recognition

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KW - Human machine interface

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ER -

Sensor based dynamic hand gesture recognition by pairnet

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ASJC Scopus subject areas

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