Baseball Swing Pose Estimation Using OpenPose

Li, Yung-Che; Chang, Ching-Tang; Cheng, Chin-Chang; Huang, Yu-Len

doi:10.1109/raai52226.2021.9507807

Cited by 15 publications

(19 citation statements)

References 4 publications

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“…In clinical scenarios, this could enhance the understanding of anatomical variations, support personalized interventions in physical therapy and ergonomics, potentially aid preventive screening, and help define the boundaries between correctable and pathological posture. Furthermore, it could help coaches evaluate the effectiveness of athletic movements [65], optimize training, and prevent posture-related injuries [66,67]. Requiring minimal clothing removal, this ML-based method reduces subjectivity in traditional postural analysis and could guide physiotherapy, remote rehabilitation, and pathology management [68,69].…”

Section: Discussionmentioning

confidence: 99%

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Roggio,

Di Grande,

Cavalieri

et al. 2024

Sensors

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Posture analysis is important in musculoskeletal disorder prevention but relies on subjective assessment. This study investigates the applicability and reliability of a machine learning (ML) pose estimation model for the human posture assessment, while also exploring the underlying structure of the data through principal component and cluster analyses. A cohort of 200 healthy individuals with a mean age of 24.4 ± 4.2 years was photographed from the frontal, dorsal, and lateral views. We used Student’s t-test and Cohen’s effect size (d) to identify gender-specific postural differences and used the Intraclass Correlation Coefficient (ICC) to assess the reliability of this method. Our findings demonstrate distinct sex differences in shoulder adduction angle (men: 16.1° ± 1.9°, women: 14.1° ± 1.5°, d = 1.14) and hip adduction angle (men: 9.9° ± 2.2°, women: 6.7° ± 1.5°, d = 1.67), with no significant differences in horizontal inclinations. ICC analysis, with the highest value of 0.95, confirms the reliability of the approach. Principal component and clustering analyses revealed potential new patterns in postural analysis such as significant differences in shoulder–hip distance, highlighting the potential of unsupervised ML for objective posture analysis, offering a promising non-invasive method for rapid, reliable screening in physical therapy, ergonomics, and sports.

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Section: Discussionmentioning

confidence: 99%

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Roggio,

Di Grande,

Cavalieri

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Consequently, motion tracking analysis is being used in various sports (baseball, hurdles, basketball, soccer, etc.) for effective athletic motion analysis [42][43][44].…”

Section: Discussionmentioning

confidence: 99%

When Taekwondo Meets Artificial Intelligence: The Development of Taekwondo

Shin,

Lee,

Chung

et al. 2024

Applied Sciences

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This study explores the comprehensive understanding of taekwondo, the application of fourth industrial revolution technologies in various kinds of sports, the development of taekwondo through artificial intelligence (AI), and essential technology in the fourth industrial revolution while suggesting advanced science directions through a literature review. Literature was sourced from six internet search electronic databases, consisting of three English databases and three Korean databases, from January 2016 to August 2023. The literature indicated cases of sports convergence with the application of fourth industrial revolution technologies, such as the game of go, golf, table tennis, soccer, American football, skiing, archery, and fencing. These sports not only use big data but also virtual reality and augmented reality. Taekwondo is a traditional martial art that originated in Republic of Korea and gradually became a globally recognized sport. Since taekwondo’s competition analysis is an analysis in which researchers manually write events, it takes a very long time to analyze, and the scale of the analysis varies depending on the researcher’s tendencies. This study presented the development of an AI Taekwondo performance improvement analysis and evaluation system and a metaverse-based virtual Taekwondo pumsae/fighting coaching platform through an AI-based motion tracking analysis method.

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“…OpenPose is another widely used pose estimation model known for its accuracy and ability to handle complex body poses. OpenPose's open-source nature allows for flexibility and customization (Cao, Simon, Wei & Sheikh, 2017;Li, Chang, Cheng & Huang, 2021).…”

Section: Methodsmentioning

confidence: 99%

Computer Vision Technologies for Human Pose Estimation in Exercise: Accuracy and Practicality

Latyshev,

Lopatenko,

Shandryhos

et al. 2024

SIE

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Information technologies are increasingly being integrated into all aspects of human life. Over the past few years, the use of machine learning models for human pose detection has significantly increased. As the realms of technology and physical activity converge, understanding the potential of these innovations becomes imperative for refining exercise monitoring systems. The aim of the research - evaluate the accuracy and viability of employing modern computer vision technologies in the identification of human pose during physical exercises. The study employed a combination of machine learning methods, video analysis, a review of scientific literature, and methods from mathematical statistics. The precision evaluation of contemporary machine learning models was conducted on a prepared dataset, comprising annotated images featuring students executing a body balance test with the camera positioned directly towards the subjects. The obtained data showed that both MediaPipe and OpenPose models proficiently recognize key anatomical landmarks during the conducted test. The MediaPipe model demonstrates a lower percentage of deviation from manual annotation compared to OpenPose for most key points: the mean deviation exceeds the threshold for 11 out of 15 key points and 7 out of 18 key points, as defined by the OpenPose and MediaPipe models, respectively. The most significant deviations are noticeable in the detection of points corresponding to the foot and wrist. The derived conclusions underscore the models can address only a portion of the tasks set. Essentially, this raises scepticism regarding the practical application of contemporary machine learning methods for human pose estimation without additional refinement.

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Baseball Swing Pose Estimation Using OpenPose

Cited by 15 publications

References 4 publications

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

When Taekwondo Meets Artificial Intelligence: The Development of Taekwondo

Computer Vision Technologies for Human Pose Estimation in Exercise: Accuracy and Practicality

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