Fitness applications are commonly used to monitor activities within the gym,
but they often fail to automatically track indoor activities inside the gym.
This study proposes a model that utilizes pose estimation combined with a novel
data augmentation method, i.e., rotation matrix. We aim to enhance the
classification accuracy of activity recognition based on pose estimation data.
Through our experiments, we experiment with different classification algorithms
along with image augmentation approaches. Our findings demonstrate that the SVM
with SGD optimization, using data augmentation with the Rotation Matrix, yields
the most accurate results, achieving a 96% accuracy rate in classifying five
physical activities. Conversely, without implementing the data augmentation
techniques, the baseline accuracy remains at a modest 64%.

利用姿势估计数据结合新颖的数据增强方法，即旋转矩阵，提出了一种模型，旨在提高基于姿势估计数据的活动识别分类准确性。实验结果表明，使用 SVM 及 SGD 优化，结合旋转矩阵的数据增强技术，可以达到 96% 的准确率在五种不同体育运动的分类中。相比之下，不实施数据增强技术，准确率仅为 64%。