In this project, we explore and experiment with Google MediaPipe’s BlazePose to detect and track pose in real-time, and tried diffent methods of pose classification, such as classic KNN and TensorFLow trained deep learning networks, to classify human poses and compared them to reference. Besides, we add repetition counting mechanism to only add counting for those poses that match the reference and the threshold can be adjusted according to use cases, such as strictness of how accurate the exercise of users and force them to do it properly. We also implement the whole experiment from Pytorch and TensorFlow to Swift language for iOS development.