基于加速度传感器的可扩展手势识别  

Accelerometer-Based Extensible Hand Gesture Recognition

作　　者：谢仁强 曹俊诚[1] 

机构地区：[1]中国科学院上海微系统与信息技术研究所中国科学院太赫兹固态技术重点实验室,上海200050 [2]上海科技大学信息科学与技术学院,上海201210

出　　处：《传感技术学报》2016年第5期659-664,共6页Chinese Journal of Sensors and Actuators

基　　金：国家重点基础研究发展计划项目(2014CB339803);国家高技术研究发展计划(863)主题项目(2011AA010205);国家自然科学基金项目(61131006);国家重大科学仪器设备开发专项项目(2011YQ150021)

摘　　要：为了提高基于加速度传感器的动态手势识别算法的性能,并且增强系统的可扩展性,提出了一种有效结合机器学习模型与模板匹配的方法。将手势分为基本手势和复杂手势两大类,其中复杂手势可分割为基本手势组成的序列;根据手势运动的特点提取有效的特征量,并利用基本手势样本训练随机森林模型,然后用其对基本手势序列进行分类预测;将预测结果进行约翰逊编码,再与标准模板序列进行相似度匹配。实验结果表明,该方法获得了99.75%的基本手势识别率以及100%的复杂手势识别率。算法既保证了手势识别的精度,也提高了系统的可扩展性。This paper presents an algorithm combining machine learning model and template matching to improve theperformance of accelerometer-based dynamic hand gesture recognition and enhance the extensibility of the system.Gestures are divided into two types,i.e.,the basic gesture and the complex gesture which can be decomposed into abasic gesture sequence. According to the characteristics of hand movements,effective features are extracted. A ran-dom forest model is constructed with the basic gesture samples,and then used to classify the basic gesture sequences.The predicted results are subsequently encoded with Johnson codes,and then matched with the standard templatesequences by comparing the similarity. Experiment achieves 99.75% basic gesture recognition rate and 100%complex gesture recognition rate. The algorithm improves the performance as well as enhances the extensibility.

关 键 词：手势识别 加速度传感器 手势分割 随机森林 相似度匹配 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]