Abstract

The ubiquity of smartphones has made a remarkable influence on everyone's day to day life. Variety of useful built-in sensors provide smartphones with a convenient floor for data collection and analysis. Application development based on the user's location and movement is not a difficult task nowadays. But injuries and deaths due to smartphone-distracted movement on roadways is on the increase. This study explores the capabilities of smartphone inertial sensors for pedestrian activity recognition. Smartphone distracted movements can be predicted from the associated pedestrian's posture, thus inertial sensors can provide effective solution for this specific task. Volunteers were asked to perform different pedestrian activities with smartphones in their hand or in trouser pocket. Accelerometer and gyroscope data were collected, and time windowing was applied for proper segmentation of the data. After time and frequency domain feature extraction of these segmented data streams, two classical supervised machine learning approaches (SVM and Random Forest) were undertaken for correct prediction of seven different pedestrian activity labels. Furthermore, we implemented a deep learning classifier (CNN) for direct activity recognition using raw data. The training and testing procedure includes three types of systems: single-subject, all-subject, and leave-one-subject-out models. For performance evaluation, we used the F-score metric, which can reach up to 92.3%, 98.1% and 97.2% for these three models, respectively. CNN with raw data provides much better accuracy than the classical machine learning models. With the capability to identify pedestrian activity and thus distracted pedestrians with great accuracy, our approach lays the foundation for a smartphone application based real time P2V warning system. In this system, the vehicle's driver gets a warning in his smartphone about the nearby presence of a distracted pedestrian.

Notes

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Graduation Date

2020

Semester

Summer

Advisor

Abdel-Aty, Mohamed

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering; Smart Cities

Format

application/pdf

Identifier

CFE0008577; DP0024253

URL

https://purls.library.ucf.edu/go/DP0024253

Language

English

Release Date

February 2021

Length of Campus-only Access

None

Access Status

Masters Thesis (Campus-only Access)

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