標題: | Applying Machine Learning to Head Gesture Recognition using Wearables |
作者: | Wu, Cheng-Wei Yang, Hua-Zhi Chen, Yan-Ann Ensa, Bajo Ren, Yi Tseng, Yu-Chee 資訊工程學系 Department of Computer Science |
關鍵字: | gesture recognition;human-computer interaction interface;mobile computing;machine learning;wearable devices |
公開日期: | 1-一月-2017 |
摘要: | 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%. |
URI: | http://hdl.handle.net/11536/147191 |
ISSN: | 2325-5986 |
期刊: | 2017 IEEE 8TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST) |
起始頁: | 436 |
結束頁: | 440 |
顯示於類別: | 會議論文 |