The Importance of Movement Detection in Yoga

Oct 13, 2025 by recoveryshake.com 117 views

Introduction

Yoga is one of the most popular sports in Indonesia, known for its various movements that are suitable for all age groups. The uniqueness of yoga movements is believed to increase balance and health, both physically and mentally. However, in practice, errors in making yoga movements can result in injuries with different severity. Therefore, we need a system that can detect every yoga movement, provide information about the name of the movement, and ensure the user's trust by verifying the accuracy of the poses carried out.

The Importance of Movement Detection in Yoga

Movement detection in yoga is crucial to ensure that users perform the correct poses and avoid injuries. With the increasing popularity of yoga, it is essential to develop a system that can detect and provide feedback on yoga movements in real-time. This can be achieved using advanced object detection algorithms, such as the You Only Look Once (Yolo) method.

The You Only Look Once (Yolo) Method

The Yolo method is a sophisticated and effective object detection algorithm that can detect objects quickly and accurately in one analysis process. By utilizing deep learning techniques, Yolo is able to process images and recognize objects in it with high efficiency. In the context of yoga, the speed and accuracy in detecting movement become the main key, because many movements that require attention to details so as not to pose a risk of injury.

Implementation of the Yolo Method in Yoga Movement Detection

In this study, we used the Yolo version 5 (Yolov5) to detect the yoga movement in real-time. This method can identify five movement classes, namely bridge pose, downward dog pose, shoulder stand pose, tree poses, and plank. The dataset used consists of 1,000 data points, which are divided into 80% for training data and 20% for validation data. In addition, as many as 75 data points are used for testing purposes. The results of the use of the Yolov5 method in detecting yoga movements showed an accuracy of 92%. These results indicate that the system developed by this method has good performance in detecting yoga movements.

Advantages of the Yolo Method

The Yolo method has several advantages that make it suitable for detecting yoga movements. Some of the advantages include:

Speed and Accuracy: The Yolo method can detect objects quickly and accurately in one analysis process.
Deep Learning Techniques: The Yolo method utilizes deep learning techniques to process images and recognize objects in it with high efficiency.
Real-time Detection: The Yolo method can detect yoga movements in real-time, making it suitable for applications that require immediate feedback.

Dataset and Training

The dataset used in this study consists of 1,000 data points, which are divided into 80% for training data and 20% for validation data. In addition, as many as 75 data points are used for testing purposes. The use of dataset divided between training data, validation, and testing is very important to ensure that the developed model can generalize well in various real conditions.

Results and Discussion

The results of the use of the Yolov5 method in detecting yoga movements showed an accuracy of 92%. These results indicate that the system developed by this method has good performance in detecting yoga movements. The accuracy of the system is high, which means that users can feel more confident when doing yoga movements, because this system can provide feedback about the right or wrong pose.

Conclusion

In conclusion, the use of the Yolo method in detecting yoga movements has shown promising results. The system developed by this method has good performance in detecting yoga movements, with an accuracy of 92%. The advantages of the Yolo method, such as speed and accuracy, deep learning techniques, and real-time detection, make it suitable for detecting yoga movements. Further development can include model training with more data to increase accuracy, as well as the application of this algorithm in mobile applications so that users can access the system anywhere and anytime.

Future Work

Further development of the Yolo method in detecting yoga movements can include:

Model Training with More Data: Increasing the accuracy of the system by training the model with more data.
Application in Mobile Applications: Developing a mobile application that allows users to access the system anywhere and anytime.
Integration with Other Sports: Applying the Yolo method in other sports to improve the quality and safety of physical activities.

References

[1] Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
[2] Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov5: An open-source object detection system. arXiv preprint arXiv:2004.01173.
Q&A: Movement Detection in Yoga Sports Using the You Only Look Once (Yolo) Method =====================================================================================

Q: What is the You Only Look Once (Yolo) method?

A: The Yolo method is a sophisticated and effective object detection algorithm that can detect objects quickly and accurately in one analysis process. By utilizing deep learning techniques, Yolo is able to process images and recognize objects in it with high efficiency.

Q: How does the Yolo method work in detecting yoga movements?

A: The Yolo method works by processing images of yoga movements and recognizing the objects (in this case, the yoga poses) in the images. The system is trained on a dataset of images of yoga movements, and it uses this training to detect and identify the poses in real-time.

Q: What are the advantages of using the Yolo method in detecting yoga movements?

A: The Yolo method has several advantages that make it suitable for detecting yoga movements. Some of the advantages include:

Speed and Accuracy: The Yolo method can detect objects quickly and accurately in one analysis process.
Deep Learning Techniques: The Yolo method utilizes deep learning techniques to process images and recognize objects in it with high efficiency.
Real-time Detection: The Yolo method can detect yoga movements in real-time, making it suitable for applications that require immediate feedback.

Q: What is the accuracy of the Yolo method in detecting yoga movements?

A: The results of the use of the Yolov5 method in detecting yoga movements showed an accuracy of 92%. These results indicate that the system developed by this method has good performance in detecting yoga movements.

Q: Can the Yolo method be used in other sports?

A: Yes, the Yolo method can be used in other sports to improve the quality and safety of physical activities. The method can be applied to detect and identify movements in various sports, such as football, basketball, and tennis.

Q: How can the Yolo method be integrated with mobile applications?

A: The Yolo method can be integrated with mobile applications to allow users to access the system anywhere and anytime. This can be achieved by developing a mobile application that uses the Yolo method to detect and identify yoga movements.

Q: What are the future directions for the Yolo method in detecting yoga movements?

A: Further development of the Yolo method in detecting yoga movements can include:

Model Training with More Data: Increasing the accuracy of the system by training the model with more data.
Application in Mobile Applications: Developing a mobile application that allows users to access the system anywhere and anytime.
Integration with Other Sports: Applying the Yolo method in other sports to improve the quality and safety of physical activities.

Q: What are the potential applications of the Yolo method in detecting yoga movements?

A: The potential applications of the Yolo method in detecting yoga movements include:

Yoga Instruction: The Yolo method can be used to provide feedback to yoga instructors on the accuracy of their students' poses.
Yoga Training: The Yolo method can be used to provide feedback to yoga students on the accuracy of their poses.
Yoga Safety: The Yolo method can be used to detect and prevent injuries in yoga by identifying incorrect poses.

Q: What are the limitations of the Yolo method in detecting yoga movements?

A: The limitations of the Yolo method in detecting yoga movements include:

Data Quality: The quality of the data used to train the model can affect the accuracy of the system.
Model Complexity: The complexity of the model can affect the accuracy of the system.
Real-time Detection: The Yolo method may not be able to detect yoga movements in real-time in all situations.

Q: What are the future research directions for the Yolo method in detecting yoga movements?

A: Future research directions for the Yolo method in detecting yoga movements include:

Improving Model Accuracy: Improving the accuracy of the system by training the model with more data.
Developing Mobile Applications: Developing mobile applications that use the Yolo method to detect and identify yoga movements.
Applying to Other Sports: Applying the Yolo method to other sports to improve the quality and safety of physical activities.