Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine

Wendong Xiao,Yingjie Lu

doi:10.1155/2015/790412

Abstract

Wearable sensor based human physical activity recognition has extensive applications in many fields such as physical training and health care. This paper will be focused on the development of highly efficient approach for daily human activity recognition by a triaxial accelerometer. In the proposed approach, a number of features, including the tilt angle, the signal magnitude area (SMA), and the wavelet energy, are extracted from the raw measurement signal via the time domain, the frequency domain, and the time-frequency domain analysis. A nonlinear kernel discriminant analysis (KDA) scheme is introduced to enhance the discrimination between different activities. Extreme learning machine (ELM) is proposed as a novel activity recognition algorithm. Experimental results show that the proposed KDA based ELM classifier can achieve superior recognition performance with higher accuracy and faster learning speed than the back-propagation (BP) and the support vector machine (SVM) algorithms.

Highlights

Recognition of physical activity plays an important role in many fields such as physical training and health care
A large number of classification methods have been investigated, including the artificial neural network (ANN) [7] and the support vector machine (SVM) [10], which have been widely used in machine learning and data analysis
We can find that Extreme learning machine (ELM) can distinguish the 6 classes of activities better than Levenberg Marquardt BP (LM-BP) and leastsquare SVM (LS-SVM) in general, and the kernel discriminant analysis (KDA) implementation on original features can improve the classification performance

Summary

Introduction

Recognition of physical activity plays an important role in many fields such as physical training and health care. A number of key research issues are related to physical activity recognition, including how to improve the data collection mechanisms, how to select more effective features, and how to design high-performance classification algorithms. A large number of classification methods have been investigated, including the artificial neural network (ANN) [7] and the support vector machine (SVM) [10], which have been widely used in machine learning and data analysis. These popular learning techniques often face some challenging issues such as intensive human intervene, slow learning speed, and poor learning scalability [11,12,13].

Proposed Approach

Approach Implementation and Experimental Results

Conclusion