Gesture-based control in a smart home environment

Fariz Alemuda, Fuchun Lin

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

6 Scopus citations

Abstract

Gesture is a convenient and natural way to control a smart home. The wearable device provides an excellent vehicle for getting a user's hand gesture. Recognition models for gestures can be divided into two types: user dependent and user independent. In this research, we propose a hybrid model that combines both user dependent and user independent models to distinguish a user's hand gestures. Our research investigates which model among three is the best approach for recognizing hand gestures. We employ ten hand gestures as the test cases for comparison. First, from a 6-axis wearable device we extract features based on the collected raw data of hand gestures. Then these extracted features are analyzed by a oneM2M-compliant platform to detect gestures based on Decision Tree and Logistic Regression algorithms. With a data set of over 7 users and 20 repetitions of tests for each user, we tested the effectiveness of recognition models and gesture detection algorithms. The results show that our proposed hybrid model could achieve the best accuracy with either of two detection algorithms.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
EditorsGeyong Min, Xiaolong Jin, Laurence T. Yang, Yulei Wu, Nektarios Georgalas, Ahmed Al-Dubi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages784-791
Number of pages8
ISBN (Electronic)9781538630655
DOIs
StatePublished - 30 Jan 2018
EventJoint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 - Exeter, United Kingdom
Duration: 21 Jun 201723 Jun 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
Volume2018-January

Conference

ConferenceJoint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017
CountryUnited Kingdom
CityExeter
Period21/06/1723/06/17

Keywords

  • Gesture recognition
  • OneM2M
  • Recognition model
  • Smart home

Fingerprint Dive into the research topics of 'Gesture-based control in a smart home environment'. Together they form a unique fingerprint.

Cite this