Publication Date

2020

Document Type

Dissertation/Thesis

First Advisor

Bharti, Pratool

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Computer Science

Abstract

In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track human physical activities effortlessly. These devices have enabled common citizens to track their physical fitness and encourage them to lead a healthy lifestyle. Among various exercises, walking and running are the most common activities people do in everyday life, either through commute, exercise, or by doing household chores. While performing these activities, the speed at which a person walks and runs is an essential factor to determine the intensity of activity. Therefore, it is important to measure walking/running speed to estimate the burned calories along with preventing them from the risk of soreness, injury, and burnout. Existing wearable technologies use a GPS sensor to measure the speed which is highly energy inefficient and does not work well indoors. To solve this problem, we design, implement, and evaluate a Convolutional Neural Network based algorithm that leverages data from accelerometer and gyroscope sensors in a wrist- worn device to detect the speed with high precision. We have also evaluated various other machine learning algorithms to compare our results.

Extent

66 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

Download

Share

COinS