Week 10: Wrapping Up and Looking Ahead

When I started this project, I had an ambitious question: Can a small, low-cost radar sensor with no cameras, no markers, and no wearables accurately classify golf swing faults? This is a field of research that has never been explored. While FMCW radar has been used for motion recognition and gait analysis, applying it to full-body golf swing classification is a novel application; no prior work has attempted to use millimeter-wave radar spectrograms to distinguish between specific swing faults across different golfers. Now, after three months of building, collecting, and testing, I can say yes, it works. A tuned ResNet-50 trained on spectrograms from a 60 GHz FMCW radar hit 93% accuracy across six swing categories. That’s not perfect, and there’s still confusion between biomechanically similar faults like early extension and no hip rotation. Still, for a first-of-its-kind single-sensor system, I’m proud of where this landed.

But the results are only part of the story. I have learned a lot and grown as a researcher through this process.

The first few weeks were about building the foundation through writing the signal processing pipeline, assembling the radar hardware, and learning more about Fourier transforms. The first time I ran a test swing through the pipeline and saw the backswing, transition, and impact burst appear in the spectrogram, my vision for this project became clearer.

Week 3’s angle experiments confirmed that the 45° placement captured both vertical and horizontal components of the swing, producing noticeably richer spectrograms than 0° or 90°. Attaching angle reflectors to body parts to isolate individual contributions was tedious but useful since being able to point at a trace in the data and identify which body part produced it gave me the ground truth I needed to interpret the spectrograms.

Recruiting golfers in Weeks 4 and 5 introduced challenges I didn’t fully anticipate. Every player’s body is different, and those physical differences scale the spectrograms in ways that have nothing to do with swing technique. If you’re not careful, your model can learn body geometry rather than swing mechanics. I decided that raw spectrograms with fixed axes preserved more useful information than any normalization scheme I tested. That decision, along with the player-stratified train/test split, was one of the most important design choices I made, as it kept the results true to the raw data.

The model selection and tuning phase was where everything came together. I tested four architectures across a baseline CNN, ResNet-50, Swin Transformer, and MobileNetV2, with ResNet-50 performing best. Hyperparameter tuning and augmentation increased its accuracy from 89% to 93%, with frequency masking in SpecAugment contributing significantly. Masking random low-frequency bands forced the model to rely on mid-frequency features, which is where the previously confused fault pairs actually diverge.

I also learned a lot about managing a project end-to-end on a fixed timeline. There were weeks where progress was painfully slow, especially during the manual labeling of 1,000+ spectrograms. But there were also weeks where everything clicked. Learning to push through the tedious parts was just as valuable as any technical skill I picked up.

Here’s the exciting part: this project isn’t over. I’ll be continuing this work through the summer with the goal of building a fully functional product, including a portable radar device and an app that lets any golfer set up a compact radar sensor, take a few swings, and get real-time feedback on what they’re doing wrong. The 93% accuracy is a strong proof of concept, but there’s a lot of ground between a research prototype and something a golfer would actually use. I want to explore multi-angle setups to resolve the fault pairs that still overlap at 45°, experiment with MobileNetV2 for on-device inference to enable the system to run on a phone, and build a feedback layer that translates predictions into actionable coaching cues. The vision is a tool that brings objective swing analysis to golfers who don’t have access to a $50,000 launch monitor, just a radar module, and their phone.

If there’s one takeaway, it’s that radar is an underexplored sensing modality for sports. It’s contactless, privacy-preserving, works in any lighting, and captures motion information that cameras can’t. Golf was the starting point, but the same approach could extend to tennis, baseball, or physical therapy. I came into this curious whether the idea would work, and I’m leaving convinced there’s something much bigger here.

Thanks for following along!