Mobile golf swing tracking using deep learning with data fusion: poster abstract

Swing tracking is one of the key information for many sports such as golf. One approach to track swing is to use IMU to measure linear acceleration then get position by two-time integration. However, the complex noise model of the IMU limit the accuracy of the tracking. Another approach is to use depth sensor to measure 3D location of a point of interest directly. Unfortunately, the depth sensor-based approach cannot accurately measure the trajectory of a swing when the sensor is occluded, which happens regularly. To overcome these limitations, we develop a novel solution to make use of these two sensor modalities (i.e., IMU and depth sensor) by a novel deep neural network to produce high precision swing trajectory tracking. The learned network automatically makes use of the IMU when the depth sensor is occluded, and relies on depth sensor when IMU signal is noisy. Our experiment shows that the proposed method outperforms state-of-the-art swing tracking method by 62% of error reduction.