Title

Using machine learning to detect pedestrian locomotion from sensor-based data

Date of Publication

2014

Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Solomon See
Roberto Legaspi

Abstract/Summary

The integration of low cost micro-electro-mechanical (MEM) sensors into smart phones have made inertial navigation systems possible for ubiquitous use. Many research studies developed algorithms to detect a user's steps, and to calculate a user's stride to know the position displacement of the user. Subsequent research have already integrated the phone's heading to map out the user's movement across a physical area. These research, however, have not taken into account negative pedestrian locomotion, wherein the user is moving but is not exhibiting any position displacement. This research aims to solve this problem by collecting positive and negative pedestrian locomotion with data from phone-embedded sensors positioned in the research subject's front pocket. Using these data, a model will be built to classify negative pedestrian locomotion from positive ones.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTG005524

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

leaves ; 4 3/4 in.

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