Author:
Aju Abin,Mathew Christa,Prakasi O. S. Gnana
Abstract
In the domain of computer vision, human pose estimation is becoming increasingly significant. It's one of the most compelling areas of research, and it's gaining a lot of interest due to its usefulness and flexibility in a variety of fields, including healthcare, gaming, augmented reality, virtual trainings and sports. Human pose estimation has opened a door of opportunities. This paper proposes a model for estimation and classification of karate poses which can be used in virtual karate posture correction and trainings. A pretrained model, PoseNet has been used for pose estimation using the results of which the angles between specific joints are calculated and fed into a K-Nearest Neighbors Classifier to classify the poses. The results obtained show that the model achieves an accuracy of 98.75%.
Publisher
Inventive Research Organization
Subject
General Agricultural and Biological Sciences
Cited by
3 articles.
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1. An ML pipeline for real-time activity detection on low computational power devices for metaverse applications;2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC);2023-05-22
2. Poses Estimation Technology for Physical Activity Monitoring;Advances in Intelligent Systems and Computing;2023
3. Yoga Pose Classification using Angle Heuristic Approach;2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA);2022-09-21