Applying machine learning to head gesture recognition using wearables

Cheng Wei Wu, Hua Zhi Yang, Yan Ann Chen, Bajo Ensa, Yi Ren, Yu-Chee Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Recently, some studies for head gestures recognition have been proposed, but most of them are based on image processing technology. Moreover, there is less focus on the use of wearable devices in recognizing head gestures. In this paper, we apply machine learning techniques to recognize some common head activities using a head-mounted wearable device. We use the wearable device to collect sensor data related to user's head activities. Then, we apply energy-based segmentation method on the collected data to find out the data segments where the activities may occur. Finally, we extract candidate features from the segments and feed them into a pre-Trained classifier to identify the type of head gesture. We implement the prototype of above methods on Arduino platform and evaluate the efficiency of the proposed methods on real datasets. The experiment results show that our proposed methods can effectively and efficiently identify different types of head gestures with an average accuracy rate of 95%.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages436-440
Number of pages5
ISBN (Electronic)9781538629659
DOIs
StatePublished - 12 Jan 2018
Event8th IEEE International Conference on Awareness Science and Technology, iCAST 2017 - Taichung, Taiwan
Duration: 8 Nov 201710 Nov 2017

Publication series

NameProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
Volume2018-January

Conference

Conference8th IEEE International Conference on Awareness Science and Technology, iCAST 2017
CountryTaiwan
CityTaichung
Period8/11/1710/11/17

Keywords

  • gesture recognition
  • human-computer interaction interface
  • machine learning
  • mobile computing
  • wearable devices

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