标题: 使用延伸形状探勘之捡取手势辨识
Item-picking Gesture Recognition Using Extended Shapelet Mining
作者: 杨飞龙
曾煜棋
Nyoto Arif Wibowo
Tseng, Yu-Chee
电机资讯国际学程
关键字: 自动感应;手势识别;穿戴式;统计特征;决策树;传感器数据处理;Shapelet Mining;Automatic Sensing;Hand Gesture Recognition;Wearable;Statistical Features;Machine Learning;Decision Tree;Sensor Data Processing
公开日期: 2017
摘要: 穿戴式装置识别人体手势的使用越来越多,在现场还有许多工作能产生良好的识别结果。然而,现有的研究仍然无法显示特征和特征能够对手势进行分类的关键特征的形状。在这项工作中,我们提出了SenseShape,一种基于Shapelet Mining的算法。这是一种强大的算法,由于本地功能的使用,噪声和失真都很健壮。我们修改算法以处理数据为多维的惯性传感器,以识别物品采集手势中的微量活动。 SenseShape通过将距离计算以外的统计特征实现为惯性数据,改进了传统的形状挖掘。为了探索我们视野的可行性,我们使用用户腕部佩戴的6轴传感器进行实验,收集物品在3个不同高度的货架上放置物品采集手势数据。新提出的方法表明,无论货架位置或移动速度如何,精确度和回归率分别为94.5%和91.6%,可以确定采摘姿势的微观活动。
The usage of wearable device to recognize human gestures are getting more exposed, there are many work in the field which yield good recognition result. However, existing study still can not show the shape of a key feature which special and distinct-able to classify gestures. In this work we propose SenseShape, an algorithm based on Shapelet Mining. It is a powerful algorithm which robust to noise and distortions because of local features usage. We modify the algorithm to work with inertial sensor whose data are multidimensional to recognize micro activity from item-picking gesture. SenseShape improved the traditional shapelet mining by implementing statistical features other than distance calculation into inertial data. To explore the feasibility of our vision, we conducted experiments using 6-axis sensor worn at wrist of the user to collect item-picking gesture data where objects are put at 3 different height levels of shelf. The new proposed method shows that it can identify micro activity of item-picking gesture regardless of shelf-location or movement speed with 94.7\% accuracy while precision and recall rate is at 94.5\% and 91.6\%, respectively.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070456168
http://hdl.handle.net/11536/142177
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