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 compared to DT, and by 0.04 compared to RF.
Highlights
With the development of microelectromechanical system (MEMS) technology and the popularization of smartphones with integrated acceleration sensors, action recognition is receiving increasing attention
In order to fully and accurately identify non-normal walking actions, this paper proposes an MEMS action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM)
By observing the daily life of a large number of students and faculty members of Xinjiang University and sampling with a questionnaire survey, we found that the top four most frequent occurrences of non-normal walking actions are: standing, writing, sleeping, and man-made
Summary
With the development of microelectromechanical system (MEMS) technology and the popularization of smartphones with integrated acceleration sensors, action recognition is receiving increasing attention. It is widely used in medical health, pedestrian dead reckoning (PDR) and other fields [1,2]. The current mainstream method for measuring the amount of exercise is to detect the number of steps taken by the patient, and the accuracy of the step counting is largely determined by motion recognition. Existing methods do not have high accuracy in identifying abnormal walking postures, leading to lower accuracy of motion recognition and affecting the overall step counting accuracy. This article will explore the recognition of abnormal walking movements in depth
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