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

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Congress on Cybermatics
Subtitle of host publication	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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	994-1001
Number of pages	8
ISBN (Electronic)	9781728129808
DOIs	https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174
Publication status	Published - 2019 Jul
Event	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, United States Duration: 2019 Jul 14 → 2019 Jul 17

Publication series

Name	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

Conference

Conference	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
Country/Territory	United States
City	Atlanta
Period	2019/07/14 → 2019/07/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Continuous hand gesture recognition
Convolutional neural networks
Human machine interface

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Renewable Energy, Sustainability and the Environment
Hardware and Architecture
Information Systems and Management
Safety, Risk, Reliability and Quality
Communication

Access to Document

10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

Cite this

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. In 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 (pp. 994-1001). Article 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 et al.
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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jhang, YJ, Chu, YC, Tai, TM, Hwang, WJ, Cheng, PW & Lee, CK 2019, Sensor based dynamic hand gesture recognition by pairnet. in 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., pp. 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, United States, 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. In 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 et al. / 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. pp. 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).

@inproceedings{54168ce9c5bb4b1f94afe06d86bcef5c,

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",

year = "2019",

month = jul,

doi = "10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174",

language = "English",

series = "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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "994--1001",

booktitle = "Proceedings - 2019 IEEE International Congress on Cybermatics",

}

TY - GEN

T1 - Sensor based dynamic hand gesture recognition by pairnet

AU - Jhang, Yun Jie

AU - Chu, Yen Cheng

AU - Tai, Tsung Ming

AU - Hwang, Wen Jyi

AU - Cheng, Po Wen

AU - Lee, Cheng Kuang

PY - 2019/7

Y1 - 2019/7

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

KW - Convolutional neural networks

KW - Human machine interface

UR - https://www.scopus.com/pages/publications/85073378636

UR - https://www.scopus.com/pages/publications/85073378636#tab=citedBy

U2 - 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

DO - 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174

M3 - Conference contribution

AN - SCOPUS:85073378636

T3 - 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

SP - 994

EP - 1001

BT - Proceedings - 2019 IEEE International Congress on Cybermatics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 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

Y2 - 14 July 2019 through 17 July 2019

ER -

Sensor based dynamic hand gesture recognition by pairnet

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this