基于智能手机传感器的基础行为识别方法研究

Basis behavior recognition based on sensors in smartphone

为提高人类行为识别准确性的同时降低实现过程的复杂程度,提出基于智能手机加速度传感器与陀螺仪数据对六种日常基础行为进行识别的方法。在分析传感器框架的基础上,对加速度传感器进行数据采集并对原始数据进行数据预处理,然后采用主成分分析方法结合已有知识对数据统计特征进行降低维数处理,再利用机器学习算法实现对行为特征的分类与识别,目的是简化基础行为的识别过程并提高数据的利用率。实验测试结果验证了决策树与支持向量机分类器结合使用的有效性,识别准确率可接近97%。

To improve the accuracy of human behavior recognition and reduce the complexity of the implementation process,this paper raised a method using the data of acceleration sensor and gyroscoped in smartphone to recognize six kinds of daily basic behaviors.It collected and preprocessed data of acceleration sensor after analyzing the framework of sensor,then it used principal component analysis method combining with existing knowledge to reduce the dimension of the statistical characteristics of the original data.Then it used machine learning algorithm to classify and recognize the behavioral characteristics,which aimed at simplifying the basic behavior recognition process and improving the utilization rate of the data.The experimental result shows that the combination of decision tree and support vector machine classifier is effectual,and the recognition accuracy can be close to 97%.