TASG: An Augmented Classification Method for Impersonal HAR

Zengwei Zheng,Meimei Huo,Yuanyi Chen,Junjie Du,Lin Sun

doi:10.1155/2018/6751363

Zengwei Zheng, Meimei Huo + Show 3 more

Open Access

https://doi.org/10.1155/2018/6751363

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Abstract

Human activity recognition (HAR) systems are widely used in our lives, such as healthcare, security, and entertainment. Most of the activity recognition models are tested in the personal mode, and the performance is quite good. However, HAR in the impersonal mode is still a great challenge. In this paper, we propose a two-layer activity sparse grouping (TASG) model, in which the first layer clusters the activities into 2–4 groups roughly and the second layer identifies the specific type of activities. A new feature selection metric inspired by the Fisher criterion is designed to measure the importance of the features. We perform the experiment using the TASG model with SVM, KNN, Random Forest, and RNN, respectively. The experiments are tested on HAPT, MobiAct, and HASC-PAC2016 datasets. The experimental results show that the performance of standard classifiers has been improved while combining the TASG method. The features selected by the proposed metric are more effective than other FS methods.

Highlights

In the last decade, adaptable sensor technologies have rapidly developed, which results in the rapid advance in mobile and ubiquitous computing
(2) We propose a new feature selection (FS) metric based on the Fisher criterion [16]
Considering simplicity and easy calculation, we propose a new feature selection metric to measure the importance of feature based on the Fisher criterion. e metric is designed on minimizing variance within a class and maximizing the variance between classes as follows: Fmetric(k)

Summary

Introduction

Adaptable sensor technologies have rapidly developed, which results in the rapid advance in mobile and ubiquitous computing. Human activity recognition (HAR) has many applications, in health monitoring, city planning, sports coaching, entertainment, fitness assessment, and smart homes [1]. Driven by these reasons, human activity recognition based on wearable sensors has become one of the research hotspots. Simple activity recognition has achieved high accuracy [2] These experimental results were obtained on small-scale datasets which only contain a few people’s activity data. Dungkaew et al [7] proposed an impersonal and lightweight model for identifying activities in nonstationary sensory streaming data, and the experimental results on WISDM [8] dataset showed the recognition accuracy was less than 80% for walking, jogging, and stairs. Other research works [9, 10] showed that the accuracy of impersonal activity recognition model was still not satisfactory. erefore, HAR in the impersonal mode is still a difficult problem to be solved

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