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- W4385316904 abstract "In today’s fast-paced life, lower back pain (LBP) has become a common problem affecting the lives of millions of people across the globe. In this disease, the person not only experiences pain but also becomes a major cause of disability and affects the person’s quality of life. There are various LBP treatment options available, but none are adequate. Therefore, the doctor recommends yoga exercise as an initial line of treatment for LBP. The accuracy of the yoga exercise is very important for the patient’s quick recovery. Still, it is very challenging for the patient to go to the professionals to keep track of yoga exercises. In this paper, we propose deep learning (DL)-based approach to check the accuracy of yoga performed by a patient. The convolutional neural network (CNN) and artificial neural network (ANN) system model measure the body keypoint angles and distances to get accuracy on selected yoga poses. The proposed model has successfully recognized the yoga poses based on the accuracy of that particular yoga. We have also created our dataset to train our model, giving the test accuracy of 91.61% and 99.24% for ANN and CNN-based models, respectively." @default.
- W4385316904 created "2023-07-28" @default.
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- W4385316904 date "2023-01-01" @default.
- W4385316904 modified "2023-09-27" @default.
- W4385316904 title "Deep Learning Approach to Recognize Yoga Posture for the Ailment of the Low Back Pain" @default.
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- W4385316904 doi "https://doi.org/10.1007/978-981-99-2710-4_21" @default.
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