My name is Anjali Sharma-Tiwari, and this project investigates the use of millimeter-wave frequency-modulated continuous-wave (FMCW) radar combined with machine learning to analyze and classify golf swing mechanics in a non-invasive and cost-effective way. Current golf analysis tools, such as launch monitors and wearable sensors, are often expensive, intrusive, or limited to ball-flight metrics rather than underlying swing mechanics. This project explores whether radar-derived motion signatures, specifically range-Doppler maps and time-Doppler spectrograms, can be used to reliably distinguish correct swings from common swing faults such as early extension, limited shoulder rotation, and improper swing paths. Data is collected using a modified FMCW radar placed at multiple angles (0°, 45°, and 90°) relative to the golfer, with ground truth established using high-speed video and expert labeling.

The research combines library and internet research, experimental data collection, signal processing, and machine-learning model development. Raw radar I/Q data is processed through FFT- and STFT-based pipelines to generate spectrograms, which are then used to train and evaluate classification models. The final product of this research will include a novel labeled dataset of golf swing spectrograms, a comparative analysis of sensor placement and observation angles, and a trained machine-learning model capable of classifying correct versus faulty swings with high accuracy. Ultimately, this project aims to demonstrate the viability of radar-based motion sensing as an accessible alternative to traditional coaching technologies, with potential applications in injury prevention, skill development, and real-time feedback systems