Gesture based Human Computer Interaction for Athletic Training

Khazaeli, Mehdi; Kari, Chadi; Baizer, Jacob; Javadpour, Leili

doi:10.22631/ijaep.v6i3.193

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…Depth sensors and gesture recognition tools are used for analysis. The position and angle of an athlete's body parts are analyzed [6] . The paper proposes the use of depth sensors and gesture recognition tools for analyzing the body movements of athletes during exercise.…”

Section: Methods Used and Resultsmentioning

confidence: 99%

“…The paper proposes the use of depth sensors and gesture recognition tools for analyzing the body movements of athletes during exercise. The goal is to assess training performance and decrease injury risk [6] .…”

Section: Methods Used and Resultsmentioning

confidence: 99%

“…The manuscript expounds upon the utilization of depth sensors and gesture recognition tools to meticulously track the bodily movements of athletes during exercise and evaluate their training performance. The ultimate objective is to minimize the risk of injury by issuing warnings regarding activities with a high potential for harm [6] .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Gesture recognition tools were utilized in this article to analyze the position and angle of an athlete's body parts during exercise. This analysis aims to assess training performance and reduce injury risk by providing warnings when the athlete is engaging in high-risk activities [6] .…”

Section: Literature Reviewmentioning

confidence: 99%

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Review of Application of Gesture and Poses for Reducing Injury in Sports

2024

ijcih

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the application of gesture and poses analysis has emerged as a promising avenue for reducing injuries in sports. This review delves into the intersection of technology, biomechanics, and injury prevention, exploring how the careful scrutiny of athletes' gestures and poses can revolutionize the way we approach sports training and rehabilitation.In the relentless pursuit of excellence, athletes often push their bodies to the limits. However, this pursuit is not without risks, as injuries can derail careers, shatter dreams, and impede the overall progress of a team. Traditional methods of injury prevention and rehabilitation have long been reliant on the expertise of coaches and medical professionals, combined with subjective assessments of an athlete's form and movement. The integration of gesture and poses analysis into this realm introduces a data-driven

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Section: Methods Used and Resultsmentioning

confidence: 99%

Section: Methods Used and Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Review of Application of Gesture and Poses for Reducing Injury in Sports

2024

ijcih

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Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

Örücü

Selek

2020

Applied Sciences

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In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a 28-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (25.8 ± 5.47 years; 74.69 ± 14.81 kg; 173.5 ± 9.52 cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired t-test analysis (p < 0.05) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by 58.08 ± 11.32% in LR training and 54.84 ± 12.72% in DSP training.

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Gesture based Human Computer Interaction for Athletic Training

Cited by 2 publications

References 8 publications

Review of Application of Gesture and Poses for Reducing Injury in Sports

Review of Application of Gesture and Poses for Reducing Injury in Sports

Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

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