Date Approved

6-4-2024

Graduate Degree Type

Thesis

Degree Name

Engineering (M.S.E.)

Degree Program

Biomedical Sciences

First Advisor

Dr. Yunju Lee

Second Advisor

Dr. Samhita Rhodes

Third Advisor

Dr. Sunghwan Joo

Academic Year

2023/2024

Abstract

Tennis, a widely played sport across various age and skill groups, prompts continual skill improvement among competitive players seeking a competitive edge. This study explores two approaches for enhancing playing style: motion capture (MoCap) and surface electromyographic (sEMG) signals. The study addresses a gap in simultaneous examination of MoCap and entire dominant leg muscle activation, particularly concerning the influence of skill level and gender on various tennis strokes. To fill this void, the research records and analyzes MoCap and dominant leg sEMG data during serves and strokes on both court sides. The hypothesis posits differences in muscle activation and body mechanics between professional and non-professional players, as well as between male and female players. Recording employed XSENS MVN MoCap and an EMG system with WinDaq Pro Data Acquisition software. MoCap and sEMG data were recorded at rates of 60 Hz and 1200 Hz, respectively, focusing on seven target muscles on the dominant side. Gel-type electrodes were strategically placed on the skin for sEMG data collection and full body XSENS sensors were placed according to guidelines. Following comprehensive warm-up, baseline readings and various tennis strokes were recorded on both court sides. Data processing and analysis were conducted in MATLAB, encompassing filtering, rectification, interpolation, and visualization. The study's findings contribute valuable insights into the relationship between motion and muscle activity in tennis, shedding light on skill-level and gender-related distinctions in player performance.

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