摘要
介绍了一种基于SVM算法的波形特征识别算法,并描述了算法如何应用于人体加速度波形识别,首先使用LIBSVM建立波形判决模型,使用摔倒与正常运动的波形建立训练集对判决模型进行训练并交叉验证模型准确性。通过在连续波形上加入滑动观察窗体,对窗体内的波形片段使用判决模型进行判决,能够实时捕获摔倒波形,并能够较准确地区分摔倒与跑步、走路等正常运动的波形。当出现误判/漏判情况时,能够及时修正训练集,让摔倒判定模型不断得到训练,进而不断提高判决准确率。
A waveform feature recognition algorithm based on SVM is introduced, and how the algorithm is applied to human body acceleration waveform recognition is described. First, a waveform decision model is established by using LIBSVM, and a training set is established to train and cross-verifying the accuracy of the model by using a waveform of falling and normal motion. By adding the sliding observation window on the continuous waveform, the decision model can make judgment based on the waveform segment in the window, in this way the falling waveform is detected in real time, and can be divided from the waveform of normal motion such as running and walking. In that case of misjudgment, the training set can corrected in time, the fall determination model is continuously trained, and the judgment accuracy is continuously improved.
出处
《现代导航》
2019年第6期440-444,共5页
Modern Navigation
关键词
加速度波形
机器学习算法
摔倒检测
LIBSVM
Acceleration Waveform
Machine Learning Algorithm
Falling Detection
LIBSVM(Library for Support Vector Machines)
