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Recently, there were many applications of the posture correction using the AI technology, especially, extracting the human skeleton to evaluate the posture.
Carey et al. [9] compared the effectiveness of two different skeletal pose models for a near real-time, multi-stage classifier and find no significant difference between the 2 pose models. Therefore, we select Microsoft Common Objects in Context (MS COCO) dataset for this study.
Liu et al. [10] proposed a mechanism for estimating and correcting fitness posture based on deep learning. The 14 keypoints of the human body can be obtained after correction.
Jangade et al. [11] pointed out that Human Pose Estimation (HPE) will be a wide range of applications and enter human daily living step by step. In fact, many papers mentioned the potential applications of HPE [12-15].
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IEEE/ICACT20240435 Slide.03
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