Week 1: From the Fairway to the Frequency Domain: How Radar Can Transform Golf Coaching

Golf is a highly technical sport, yet most golfers, from beginners to competitive amateurs, receive feedback that is either subjective, such as your hips are too early, or dependent on tools that cost thousands of dollars and require controlled environments to operate. Launch monitors like TrackMan can measure ball flight data with impressive precision, but they tell you relatively little about what your body is actually doing wrong. Wearable sensors require physical attachment to the club or glove, which can alter the feel of the swing itself. Optical motion-capture systems, the gold standard in biomechanics research, demand expensive cameras, careful lighting, and reflective markers placed across the golfer’s body. The result is that objective, accessible swing analysis remains out of reach for the overwhelming majority of the 28 million Americans who play golf regularly. My research asks a technically ambitious question: Can a compact, low-cost radar sensor that requires no ball, no markers, no special lighting, and no physical contact accurately classify correct golf swings and the common faults that lead to inconsistent performance and injury?

The technology at the center of my project is a 60 GHz frequency-modulated continuous-wave (FMCW) radar, specifically the Infineon XENSIV™ BGT60TR13C module. FMCW radars work by transmitting a signal whose frequency increases linearly over time, called a chirp, and then analyzing the difference between the transmitted signal and the signal that bounces back from a moving target. That difference encodes both how far away the target is and how fast it is moving, which together produce rich representations of human motion called time-Doppler spectrograms. A key question I am investigating from the outset is where to physically position the radar relative to the golfer. Preliminary reasoning from the physics of Doppler sensing suggests that a 45° diagonal placement, rather than directly in front of or perpendicular to the swing plane, may capture the richest motion data, because it would simultaneously pick up both horizontal and vertical velocity components. I plan to test this systematically by recording swings from all three angles (0°, 45°, and 90°) and comparing the resulting spectrograms. My expectation going in is that these spectrograms will contain distinct, classifiable signatures for six swing types I am targeting: a correct swing, no shoulder rotation, an outside-in path, an inside-out path, early extension, and no hip rotation. Whether that is actually the case, and whether a machine learning model can learn to distinguish them reliably, is what this project is designed to find out.

This week, I dedicated my time to reading the literature and planning the hardware. A few findings stood out as particularly significant. A 2025 survey of millimeter-wave radar in medical applications (Zhang et al.) reported that CNN-based systems achieved up to 98.8% accuracy in gait classification tasks, a benchmark that frames what may be achievable for golf swing classification if the dataset and architecture are well-designed. A study by Samimi Fard et al. evaluated five machine learning models (SVMs, MLPs, CNNs, LSTMs, and ConvLSTMs) on FMCW radar activity-recognition data, finding that a Convolutional LSTM achieved the highest accuracy on cross-scene validation, which directly informs which models I plan to implement and compare. I also reviewed the Titleist Performance Institute’s swing characteristics catalogue, which provided biomechanical grounding for each of the six fault categories my radar system will attempt to classify. With the literature review complete, I turned to hardware planning. Poring over the full Infineon BGT60TR13C datasheet, I noted that a 4 GHz bandwidth gives the sensor a theoretical range resolution of 3.75 cm, sufficient in principle to resolve the subtle torso translations that distinguish one fault from another. From there, I sketched a preliminary baseboard schematic, finalized the first design iteration, and placed all component orders.

My central hypothesis is that 60 GHz FMCW radar spectrograms contain sufficient discriminative information to classify all six swing categories with high accuracy, and that a deep learning model, likely a fine-tuned ResNet or ConvLSTM, will outperform traditional handcrafted-feature approaches such as SVMs when trained on a sufficiently large and augmented dataset. I also expect that the 45° observation angle will yield the highest classification accuracy among the three angles I will test. The next two weeks will be spent assembling and calibrating the radar hardware, building the full signal processing pipeline from I/Q streaming to spectrogram generation, and finalizing the experimental protocol before participant data collection begins. What I am most curious about heading into that work is whether the spectrograms I generate will cleanly separate the six fault categories visually before any model is even trained, or whether the differences will be subtle enough that machine learning will have to do real work to find them. That question will shape many of the decisions ahead.