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计算机系统应用:2020,29(6):196-203
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基于加速度传感器的人体运动模式识别
(安徽工业大学 计算机科学与技术学院, 马鞍山 243002)
Human Motion Pattern Recognition Based on Acceleration Sensor
(School of Computer Science and Technology, Anhui University of Technology, Maanshan 243002, China)
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投稿时间:2019-11-07    修订日期:2019-11-28
中文摘要: 本文提出了一种基于MPU9250微处理器的人体运动识别的方法. 用户在佩戴手环的情况下进行各类运动, 手环即可自动采集并存储用户在运动过程中产生的加速度数据. 分析这些数据可以判别人体运动的类别. 通过手环内嵌的加速度传感器采集运动者在X, Y, Z 3个方向上的加速度, 经过滤波算法过滤后, 分别在时域和频域两个方面对数据进行分析, 再经过特征工程提取34个相关特征, 使用特征选择算法选取主要的16个特征, 减小算法复杂度. 实验比较了支持向量机(SVM), 决策树(decision tree)和随机森林(random forest) 3种方法, 对走路、跑步、羽毛球正手挥拍、打乒乓球、划船5种运动模式进行分类, 结果表明随机森林准确率最佳, 可达到97%以上.
Abstract:This paper presents a method based on MPU9250 microprocessor for human motion recognition. The user performs various types of sports while wearing the bracelet, and the bracelet automatically collects and stores the acceleration data generated by the user during the movement. Analysis of these data can identify the type of human motion. The acceleration of the motion in the X, Y, and Z directions is collected by the acceleration sensor embedded in the wristband. After filtering by the filtering algorithm, the data is analyzed in the time domain and the frequency domain respectively, and then subjected to feature engineering extraction. There are 34 related features, feature selection algorithm is used to select the 16 main features, reducing the complexity of the algorithm. Experiments compare the three methods, namely Support Vector Machine (SVM), decision tree, and random forest, in classification of five sports, modes such as walking, running, badminton forehand swing, table tennis, and rowing. The results show that the random forest has the best accuracy which can reach more than 97%.
文章编号:7443     中图分类号:    文献标志码:
基金项目:安徽省教育厅重大课题(KJ2017ZD05)
引用文本:
孙宇航,周建钦,张学锋.基于加速度传感器的人体运动模式识别.计算机系统应用,2020,29(6):196-203
SUN Yu-Hang,ZHOU Jian-Qin,ZHANG Xue-Feng.Human Motion Pattern Recognition Based on Acceleration Sensor.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):196-203

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