Date of Award

2024

Degree Type

Thesis

Degree Name

Master of Science in Electrical Engineering (MSEE)

Specialization

Biomedical Engineering

Department

Electrical, Computer, and Biomedical Engineering

First Advisor

Dhaval Solanki

Abstract

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity, which often lead to significant challenges in academic and social settings. Diagnostic methods for ADHD rely on subjective assessments which are prone to bias. Researchers have explored technology-based objective assessment methods to help diagnose ADHD. In particular, this thesis investigates behavioral differences between children with ADHD and neurotypical children (6-11 years old) using smartwatch data collected from both hands during various classroom-like activities including drawing, math and English worksheets, and magnetic tiles puzzles. The primary objective is to identify distinct behavioral patterns to enhance diagnostic methods for ADHD by analyzing accelerometer and gyroscope data which was the data collected from the study. We pre-processed the smartwatch data and calculated various statistical features such as mean, median, standard deviation, skewness, and kurtosis. Wrist angles were also used to differentiate on-task and off-task states. Hypothesis testing indicated that the calculated angle of the collected data from the y-axis (left to right movement in the transverse plane) in activity 1 (magnetic tiles puzzling) and the 'sum of consecutive differences' in activity 4 (drawing), were significant in distinguishing between the two groups (p-value of ~0.025). Notably, the left hand generally provided more discriminative data than the right hand indicating that future research could focus on the left hand. Our findings suggest that specific patterns in smartwatch data can differentiate between ADHD and neurotypical children during classroom activities. These insights hold promise for developing more objective and data-driven diagnostic tools for ADHD, though the small sample size and data loss pose limitations that should be addressed in future research.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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