Pose estimation is a difficult subject to solve. Existing position estimate approaches are general-purpose and do not function effectively on all yoga poses since a small number of yoga poses are sophisticated and not observed in everyday activities. A person can learn yoga in a variety of methods. Yoga can be learned at a yoga class or at home via self-teaching. With the help of books and videos, one can also read for herself. Many people prefer to study alone, yet it might be difficult for them to identify improper postures. Our project intends to replace traditional yoga learning techniques. The AI-Based Yoga Instructor project seeks to create an easy-to-use web interface to assist people in learning yoga in a simple and straightforward manner. In this paper, we provide improved pose comparison scoring metrics as well as enhancements to existing pose estimation algorithms to include yoga positions. The Media-Pipe pose estimation model is used in our study to recognize real-time human poses. Additionally, the detected pose is categorized using a heuristic angle-based approach to deliver relevant feedback to the user in order to help him or her improve their yoga poses.
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Pose estimation is a difficult subject to solve. Existing position estimate approaches are general-purpose and do not function effectively on all yoga poses since a small number of yoga poses are sophisticated and not observed in everyday activities. A person can learn yoga in a variety of methods. Yoga can be learned at a yoga class or at home via self-teaching. With the help of books and videos, one can also read for herself. Many people prefer to study alone, yet it might be difficult for them to identify improper postures. Our project intends to replace traditional yoga learning techniques. The AI-Based Yoga Instructor project seeks to create an easy-to-use web interface to assist people in learning yoga in a simple and straightforward manner. In this paper, we provide improved pose comparison scoring metrics as well as enhancements to existing pose estimation algorithms to include yoga positions. The Media-Pipe pose estimation model is used in our study to recognize real-time human poses. Additionally, the detected pose is categorized using a heuristic angle-based approach to deliver relevant feedback to the user in order to help him or her improve their yoga poses. 68 pp. Englisch. N° de réf. du vendeur 9786205491362
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Pose estimation is a difficult subject to solve. Existing position estimate approaches are general-purpose and do not function effectively on all yoga poses since a small number of yoga poses are sophisticated and not observed in everyday activities. A person can learn yoga in a variety of methods. Yoga can be learned at a yoga class or at home via self-teaching. With the help of books and videos, one can also read for herself. Many people prefer to study alone, yet it might be difficult for them to identify improper postures. Our project intends to replace traditional yoga learning techniques. The AI-Based Yoga Instructor project seeks to create an easy-to-use web interface to assist people in learning yoga in a simple and straightforward manner. In this paper, we provide improved pose comparison scoring metrics as well as enhancements to existing pose estimation algorithms to include yoga positions. The Media-Pipe pose estimation model is used in our study to recognize real-time human poses. Additionally, the detected pose is categorized using a heuristic angle-based approach to deliver relevant feedback to the user in order to help him or her improve their yoga poses. N° de réf. du vendeur 9786205491362
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar AtulEr. Pragati Singh and Er. Shashank Sahu are the graduates of Computer Science and Engineering of SRMCEM Lucknow. Dr. Atul is working as an Associate Professor in CSE Department.Pose estimation is a difficult subject to . N° de réf. du vendeur 706504609
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Pose estimation is a difficult subject to solve. Existing position estimate approaches are general-purpose and do not function effectively on all yoga poses since a small number of yoga poses are sophisticated and not observed in everyday activities. A person can learn yoga in a variety of methods. Yoga can be learned at a yoga class or at home via self-teaching. With the help of books and videos, one can also read for herself. Many people prefer to study alone, yet it might be difficult for them to identify improper postures. Our project intends to replace traditional yoga learning techniques. The AI-Based Yoga Instructor project seeks to create an easy-to-use web interface to assist people in learning yoga in a simple and straightforward manner. In this paper, we provide improved pose comparison scoring metrics as well as enhancements to existing pose estimation algorithms to include yoga positions. The Media-Pipe pose estimation model is used in our study to recognize real-time human poses. Additionally, the detected pose is categorized using a heuristic angle-based approach to deliver relevant feedback to the user in order to help him or her improve their yoga poses.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch. N° de réf. du vendeur 9786205491362
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. AI-BASED YOGA INSTRUCTOR | Atul Kumar (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205491362 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 124654053
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