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Figure 2 – uploaded by IJRASET Publication
Figure 2 Human Pose Estimation (HPE) For the pose estimation component, we utilize a pre-trained real-time system, called Blaze Pose, that can detect human body key points in videos. (More detail on BlazePose and our reasoning for choosing this system can be found in Technical Approach.) This model is functional out of the box, and thus is very simple to install for users of our application. Using BlazePose allows us to take advantage of the state of the art in pose estimation algorithms for our task, and lets us focus on the actual evaluation of exercise posture. For the posture evaluation (pose training) component, we have recorded videos of ourselves performing exercises. Our videos include our best effort to correctly perform the exercise, as well as intentionally incorrect examples. The evaluation of our posture identifier is dependent on the performance of the pose estimator. We work under the assumption that the pose estimator is accurate a majority of the time, with small measurement deviances due to noise, which we correct for. We evaluate our posture identifier in different ways depending on the algorithm: for heuristic. algorithms, we feed in all videos for evaluation, while for machine learning algorithms, we evaluate by splitting our video dataset into train and test sets, and report results on the test set. To design the basic Curl counter we have to select three points from the basic 33 key-points that are responsible for the movement eee. go ee ous
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