标题:Wavelet Package Analysis based Fall Detection and Diagnosis
作者:Huang, Meichuan; Wang, Xiaoxuan; Sun, Peng; Wang, Sheng; Wang, Zhi
通讯作者:Huang, MC
作者机构:[Huang, Meichuan; Wang, Xiaoxuan; Sun, Peng; Wang, Zhi] Zhejiang Univ, Dept Control Sci & Engn, Hangzhou 310012, Zhejiang, Peoples R China.; [Wang, 更多
会议名称:37th Chinese Control Conference (CCC)
会议日期:JUL 25-27, 2018
来源:2018 37TH CHINESE CONTROL CONFERENCE (CCC)
出版年:2018
页码:4206-4211
关键词:Fall detection; Multi-resolution analysis; Acceleration signal;; Principal component analysis
摘要:As a possible hazard to public health, the fall problem has gradually attracted researchers' attention. Detecting the occurrence of fall and diagnosing its intensity can be critical and helpful for deciding the next move, In this work, a novel accelerometer-based signal processing and analysis framework for fall detection and diagnosis is proposed, Wavelet package decomposition (WPD) is introduced for denoising and multi-resolution time-frequency analysis of signals from smartphone's accelerometer sensor. The extraction of features takes into account the statistical properties in both time domain and wavelet domain. To reduce the computational complexity of training and testing the classifiers, the reduction of dimension is performed by additionally evaluating features with principal component analysis (PCA). Then these features become the input of the first classifier for fall detection and if a fall occurs, the second classifier for intensity diagnosis will take the matter further. Test subjects undertake the experiments of falls and activities of daily living (ADLs) to generate data for analysis. The performance of classification based on different algorithms including k-nearestneighbor (k-NN)and support vector machine (SVM) is presented and compared. The good results indicate this work's applicability in real-world scenarios.
收录类别:CPCI-S
资源类型:会议论文
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