Human Activity and Posture Classification Using Wearable Accelerometer

Authors

Kevin Wunderlich, Sacred Heart UniversityFollow
Eman Abdelfattah, Ramapo College of New Jersey

Document Type

Conference Proceeding

Publication Date

11-2018

Abstract

This paper presents an analysis of several machine learning classification models and their effectiveness in classifying human activity and posture using data gathered from wearable accelerometers. This analysis was done with a publicly available dataset comprising 1656,633 samples organized into five activity classes, provided by researchers at the Pontifical Catholic University of Rio De Janeiro. In particular, this study focuses on the efficacy of Logistic Regression Classification, Stochastic Gradient Descent Classification, Random Forest Classification, Support Vector Machines and Artificial Neural Networks.

Comments

2018, 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York.

At the time of publication Kevin Wunderlich was a graduate student in the School of Computer Science and Engineering at Sacred Heart University.

DOI

10.1109/UEMCON.2018.8796831

Recommended Citation

Wunderlich, K. & Abdelfattah, E. (2018). Human activity and posture classification using wearable accelerometer data. 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 77-81, doi: 10.1109/UEMCON.2018.8796831.