Fine-grained and real-time gesture recognition by using IMU sensors

Dian ZHANG, Xiaofeng WU, Chen WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. On the other hand, if we change the person to perform the same gesture, the algorithms also fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in real-time and also recognize complicated gestures. Our experiments results show that, the successfully recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.

Original languageEnglish
Title of host publicationProceedings : 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017
PublisherIEEE Computer Society
Pages747-754
Number of pages8
ISBN (Electronic)9781538621295
DOIs
Publication statusPublished - 29 May 2018
Externally publishedYes
Event23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017 - Shenzhen, China
Duration: 15 Dec 201717 Dec 2017

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2017-December
ISSN (Print)1521-9097

Conference

Conference23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017
CountryChina
CityShenzhen
Period15/12/1717/12/17

Fingerprint

Gesture recognition
Units of measurement
Sensors
Learning algorithms
Learning systems
Experiments
Internet of things

Keywords

  • Gesture-recognition
  • IMU
  • Motion-tracking

Cite this

ZHANG, D., WU, X., & WANG, C. (2018). Fine-grained and real-time gesture recognition by using IMU sensors. In Proceedings : 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017 (pp. 747-754). (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS; Vol. 2017-December). IEEE Computer Society. https://doi.org/10.1109/ICPADS.2017.00100
ZHANG, Dian ; WU, Xiaofeng ; WANG, Chen. / Fine-grained and real-time gesture recognition by using IMU sensors. Proceedings : 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017. IEEE Computer Society, 2018. pp. 747-754 (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS).
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abstract = "Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. On the other hand, if we change the person to perform the same gesture, the algorithms also fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in real-time and also recognize complicated gestures. Our experiments results show that, the successfully recognition rate of our algorithm is 100{\%}. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.",
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ZHANG, D, WU, X & WANG, C 2018, Fine-grained and real-time gesture recognition by using IMU sensors. in Proceedings : 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017. Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS, vol. 2017-December, IEEE Computer Society, pp. 747-754, 23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017, Shenzhen, China, 15/12/17. https://doi.org/10.1109/ICPADS.2017.00100

Fine-grained and real-time gesture recognition by using IMU sensors. / ZHANG, Dian; WU, Xiaofeng; WANG, Chen.

Proceedings : 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017. IEEE Computer Society, 2018. p. 747-754 (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS; Vol. 2017-December).

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

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ZHANG D, WU X, WANG C. Fine-grained and real-time gesture recognition by using IMU sensors. In Proceedings : 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017. IEEE Computer Society. 2018. p. 747-754. (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS). https://doi.org/10.1109/ICPADS.2017.00100