Wearable Sensor-Based Human Activity Recognition via Two-Layer Diversity-Enhanced Multiclassifier Recognition Method.

Yiming Tian,Zuojun Liu,Lingling Chen,Xitai Wang

doi:10.3390/s19092039

Abstract

Sensor-based human activity recognition can benefit a variety of applications such as health care, fitness, smart homes, rehabilitation training, and so forth. In this paper, we propose a novel two-layer diversity-enhanced multiclassifier recognition method for single wearable accelerometer-based human activity recognition, which contains data-based and classifier-based diversity enhancement. Firstly, we introduce the kernel Fisher discriminant analysis (KFDA) technique to spatially transform the training samples and enhance the discrimination between activities. In addition, bootstrap resampling is utilized to increase the diversities of the dataset for training the base classifiers in the multiclassifier system. Secondly, a combined diversity measure for selecting the base classifiers with excellent performance and large diversity is proposed to optimize the performance of the multiclassifier system. Lastly, majority voting is utilized to combine the preferred base classifiers. Experiments showed that the data-based diversity enhancement can improve the discriminance of different activity samples and promote the generation of base classifiers with different structures and performances. Compared with random selection and traditional ensemble methods, including Bagging and Adaboost, the proposed method achieved 92.3% accuracy and 90.7% recall, which demonstrates better performance in activity recognition.

Highlights

Human activity recognition (HAR), as a new research area in the field of pattern recognition, has become a topic of focus for many scholars
principal component analysis (PCA)-based features well obtained as the results
The inertial signals obtained by the same kind of activity under different conditions exhibit different characteristics

Summary

Introduction

Human activity recognition (HAR), as a new research area in the field of pattern recognition, has become a topic of focus for many scholars. HAR, especially the activities of daily living (ADL) such as walking, sitting, lying, jumping, and so forth, has attracted much attention from researchers worldwide. Various HAR systems have been proposed by researchers as a medium to obtain additional information about people’s activities. Activities, doctors have been able to diagnose some chronic diseases [1] as well as develop rehabilitation plans for Parkinson’s patients [2]. HAR can provide the elderly with better-quality healthcare. HAR is important for applications including human–computer interaction, surveillance, keeping track of athletic activities [3], and so on

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