Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM

Sensors
Pan HuangShuxian Liu

Abstract

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235...Continue Reading

References

Aug 16, 2019·Journal of Neuroscience Methods·Primoz RavbarJulie H Simpson
Sep 25, 2019·Sensors·Alexander Diete, Heiner Stuckenschmidt
Oct 3, 2019·Computers in Biology and Medicine·Samuel BoudetDenis Houzé de l'Aulnoit
Feb 15, 2020·Respiratory Medicine·Emily S WanMarilyn L Moy

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Citations

Aug 29, 2021·Sensors·Raúl Gómez RamosJaime Gómez-García-Bermejo

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Methods Mentioned

BETA
feature extraction

Software Mentioned

libsvm
Relief
SVM

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