Enhancing deadlift training through an artificial intelligence-driven personal coaching system using skeletal analysis DOI Creative Commons

Bolganay Kaldarova,

Aigerim Toktarova,

Rustam Abdrakhmanov

и другие.

Retos, Год журнала: 2024, Номер 60, С. 439 - 448

Опубликована: Авг. 27, 2024

This paper presents an innovative AI-driven personal coaching system designed to enhance deadlift training through advanced skeletal analysis and deep learning techniques. The proposed employs the PoseNet model capture analyze real-time video feeds, extracting keypoint coordinates angles monitor user posture movements accurately. Utilizing Local Histograms of Oriented Gradients (LHOG) Optical Flow (LHOF) methods, performs comprehensive feature extraction, assessing both static dynamic aspects exercise. model, trained on extensive dataset correctly incorrectly performed deadlifts, classifies correctness exercise with high accuracy, providing feedback personalized recommendations users. immediate corrective facilitates prompt adjustments, reduces injury risk, promotes proper technique, enhancing overall efficacy strength training. system's ability offer user-specific feedback, tailored individual body structures movement patterns, ensures relevance effectiveness in diverse environments. Practical applications this span gyms, rehabilitation centers, home settings, making it a valuable tool for trainers physiotherapists. While study demonstrates significant potential, also identifies areas future research, including algorithm refinement, expansion, integration additional metrics technologies. Overall, represents substantial advancement monitoring improvement, contributing broader field fitness health technologies, paving way safer more effective routines. Keywords: coaching, training, analysis, PoseNet, learning, monitoring, feedback.

Язык: Английский

Applying an augmented reality game-based learning environment in physical education classes to enhance sports motivation DOI Creative Commons
Nurlan Omarov, Bakһytzһan Omarov, Zhanar Azhibekova

и другие.

Retos, Год журнала: 2024, Номер 60, С. 269 - 278

Опубликована: Авг. 27, 2024

This research investigates the impact of an Augmented Reality (AR) game-based learning environment on enhancing motivation and physical activity levels in sports education. The study involved two groups 30 students each, with one group using AR-enhanced other participating traditional education methods. Over course semester, data were collected through structured questionnaires monitoring. analysis, which included independent samples t-tests Levene's tests for equality variances, revealed that exhibited significantly higher compared to control group. These findings suggest AR can transform by making experiences more engaging interactive, thereby increasing student participation enthusiasm. supports integration technologies educational curricula enhance dynamics outcomes It also underscores need further explore scalability long-term impacts applications across various settings disciplines. this advocate a systematic inclusion innovative technological solutions meet evolving needs learners educators increasingly digital interactive environments today. Keywords: augmented reality, learning, education, enhancement, motivational improvement, technology integration, environments.

Язык: Английский

Процитировано

5

Exploring changes in attitudes towards doping in sport in different groups of the russian population: a mixed methods study with policy recommendations DOI Creative Commons
Tarasova Mariya Ilinichna, Оlga S. Kulyaminа, Evgeniya M. Bronnikova

и другие.

Retos, Год журнала: 2024, Номер 58, С. 855 - 861

Опубликована: Июль 17, 2024

Introduction: This article examines the evolution of anti-doping culture in Russia through a comparative analysis sociological surveys conducted 2019, 2020, and 2021. It focuses on changing attitudes Russian citizens towards doping sports, their awareness regulations, underlying causes motivations for violations rules. The significance understanding public opinion countermeasures is highlighted, underscoring its value specialists, researchers, executive authorities addressing mitigating issue. Materials Methods: study employed across various categories groups within population, spanning three consecutive years. These aimed to gather comprehensive insights into public's perception doping, knowledge efforts, effectiveness existing measures. methodology was designed enable detailed analysis, shedding light trends shifts over time. Results: Findings from reveal noticeable development among marked by changes use growing research identified specific motives behind rule violations, illustrating complex nature Additionally, provided an objective assessment problem's scale, based social banned substances, whether natural, narcotic, or synthetic origin. Conclusions: paper concludes with up-to-date data situation Federation perspectives scientists enhancing efforts. Based authors propose set measures at improving activities Russia. recommendations are intended bolster fight against reflecting study's contribution forming societal this challenge paving way more effective countermeasures. Keywords: rules, measures, culture, toward poll.

Язык: Английский

Процитировано

0

Enhancing deadlift training through an artificial intelligence-driven personal coaching system using skeletal analysis DOI Creative Commons

Bolganay Kaldarova,

Aigerim Toktarova,

Rustam Abdrakhmanov

и другие.

Retos, Год журнала: 2024, Номер 60, С. 439 - 448

Опубликована: Авг. 27, 2024

This paper presents an innovative AI-driven personal coaching system designed to enhance deadlift training through advanced skeletal analysis and deep learning techniques. The proposed employs the PoseNet model capture analyze real-time video feeds, extracting keypoint coordinates angles monitor user posture movements accurately. Utilizing Local Histograms of Oriented Gradients (LHOG) Optical Flow (LHOF) methods, performs comprehensive feature extraction, assessing both static dynamic aspects exercise. model, trained on extensive dataset correctly incorrectly performed deadlifts, classifies correctness exercise with high accuracy, providing feedback personalized recommendations users. immediate corrective facilitates prompt adjustments, reduces injury risk, promotes proper technique, enhancing overall efficacy strength training. system's ability offer user-specific feedback, tailored individual body structures movement patterns, ensures relevance effectiveness in diverse environments. Practical applications this span gyms, rehabilitation centers, home settings, making it a valuable tool for trainers physiotherapists. While study demonstrates significant potential, also identifies areas future research, including algorithm refinement, expansion, integration additional metrics technologies. Overall, represents substantial advancement monitoring improvement, contributing broader field fitness health technologies, paving way safer more effective routines. Keywords: coaching, training, analysis, PoseNet, learning, monitoring, feedback.

Язык: Английский

Процитировано

0