摘要
传统基于加速度传感器的运动识别方法通常假设传感设备是固定放置的,当传感设备的放置方式或位置偏离预定设置时识别性能会受到极大影响。然而,在普适计算环境下使用最为广泛的传感设备——智能手机,通常无法预先固定其放置方式和位置。为解决此问题,提出了一种基于加速度传感器、与放置方式和位置无关的运动识别方法。该方法首先基于一种降维算法将原始三维加速度信号处理成与放置方式无关的一维信号,然后借鉴生物信息学中的"模体"(Motif)概念抽取一维信号中与放置位置无关的模式特征,最后基于模式特征构建向量空间模型(VSM)对运动进行识别。实验结果表明,该方法在不固定传感设备放置方式和位置条件下的运动识别率达到81.41%。
Traditional activity recognition methods based on acceleration sensors generally have the assumption that the orientation and placement of sensing devices are fixed. But the recognition performance will be greatly affected when this assumption fails. However, mobile phones, the most widely used sensing devices in pervasive computing environments, are usually placed at unfixed orientation and placement. In this paper,an activity recognition method based on independent acceleration sensor orientation and placement was proposed to resolve this problem. First, the original 3D acceleration signals are processed into one-dimensional signals. Then, the concept ‘Motif’ from bioinformatics is borrowed to extract position-independent patterns from one-dimensional signals. Finally, Vector Space Model （VSM） based on extracted patterns is built to conduct activity recognition. Experimental results show that recognition rate of the method reaches to 81.41% under the condition of unfixed orientation and placement of sensing devices.
出处
《计算机科学》
CSCD
北大核心
2014年第10期76-79,94,共5页
Computer Science
基金
国家"核高基"重大科技专项课题(2010ZX01042-002-003)
中国自然科学基金(61202282
60703040
61332017)
浙江省重大科技专项(2011C13042)
浙江省自然科学基金(LY12F02046)资助
关键词
运动识别
传感器位置
加速度传感器
模体发现
普适计算
Activity recognition, Sensor placement, Accelerometer, Motif discovery, Pervasive computing
