Real-Time Motion Tracking and Classification Using Quaternion Sensor Data

University

Shawnee State University

Major

Computer Engineering Technology/Digital Sim.+Game Prog.

Student Type

Undergraduate Student

Presentation Types

Poster Group Presentation (Live)

Keywords:

Quaternions, Motion Classification, Wearable Sensors, Sports Analytics

Abstract

This project explores the use of wearable inertial sensors to classify human motion. Initial testing focuses on controlled motion trials, where users perform predefined actions while data is recorded and exported for analysis. Quaternion representations enable more robust tracking of rotational motion compared to traditional methods, making them well-suited for applications such as athletic performance analysis. Our long-term goal is to extend this system to classify complex movements in baseball, such as pitching and swinging, through refined sensor placement and data processing techniques.

Human and Animal Subjects

yes

IRB or IACUC Approval

yes

Faculty Mentor Name

Bob Newland

Faculty Mentor Title

Professor

Faculty Mentor Department

Engineering Technologies

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Real-Time Motion Tracking and Classification Using Quaternion Sensor Data

This project explores the use of wearable inertial sensors to classify human motion. Initial testing focuses on controlled motion trials, where users perform predefined actions while data is recorded and exported for analysis. Quaternion representations enable more robust tracking of rotational motion compared to traditional methods, making them well-suited for applications such as athletic performance analysis. Our long-term goal is to extend this system to classify complex movements in baseball, such as pitching and swinging, through refined sensor placement and data processing techniques.