A motion recognition method by constancy-decision
A motion recognition method by constancy-decision
复制标题
DOI：
10.1109/iswc.2010.5665870
复制
发表时间：
2010-12
期刊：
影响因子：
--
通讯作者：
Kazuya Murao;T. Terada
中科院分区：
文献类型：
--
作者：
Kazuya Murao;T. Terada
已经提出了许多使用加速度计的上下文感知系统。已识别的上下文被分类为姿势（例如坐着）、行为（例如行走）和手势（例如出拳）。姿势和行为是持续一定时间的状态。然而，手势是零星的或一次性的动作。寻找隐藏在其他环境中的手势是一项具有挑战性的任务。在本文中，我们提出了一种方法，利用加速度值的自相关性将上下文分类为姿势、行为和手势，并使用适当的方法识别上下文。我们评估了七种手势和五种行为的识别召回率和准确率；传统方法给出的值为 0.75 和 0.59，而我们的方法给出的值为 0.93 和 0.93。我们的系统使用户即使在执行某种行为时也可以通过手势进行输入。
Many context-aware systems using accelerometers have been proposed. Contexts that have been recognized are categorized into postures (e.g. sitting), behaviors (e.g. walking), and gestures (e.g. a punch). Postures and behaviors are states lasting for a certain length of time. Gestures, however, are sporadic or once-off actions. It has been a challenging task to find gestures buried in other contexts. In this paper, we propose a method that classifies contexts into postures, behaviors, and gestures by using the autocorrelation of the acceleration values and recognizes contexts with an appropriate method. We evaluated the recall and precision of recognition for seven kinds of gestures while five kinds of behaviors; The conventional method gave values of 0.75 and 0.59 whereas our method gave 0.93 and 0.93. Our system enables a user to input by gesturing even while he or she is performing a behavior.