標題: Gesture-based Control in a Smart Home Environment
作者: Alemuda, Fariz
Lin, Fuchun Joseph
資訊工程學系
電機資訊國際碩士學位學程
Department of Computer Science
EECS International Graduate Program-Master
關鍵字: Gesture recognition;recognition model;smart home;oneM2M
公開日期: 1-Jan-2017
摘要: 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.
URI: http://dx.doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.120
http://hdl.handle.net/11536/147086
DOI: 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.120
期刊: 2017 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA)
起始頁: 784
結束頁: 791
Appears in Collections:Conferences Paper