Abstract
To enjoy the glow of good health, you must exercise [Gene Tunney], because it helps us to feel happier, increase energy levels, reduce chronic disease and helps us to keep our brain and body refresh. Today’s computer vision technology is supported by deep algorithms which use a special type (CNN) of neural networks to sense objects. In this work, we propose a novel system to classify different types of exercise pose detection automatic self-ruling decision making and predictive models using convolutional neural networks (CNN). In earlier, some research has been conducted to pose detection in image classification problems. For strong architecture, we retrained the final layer of the CNN architecture, VGG16, MobileNet, Inception V3 for classification approach. Predicting among five different classes. We will create a new model “Exer-NN” to successfully classify human exercise pose. We proposed an average accuracy is 88% approximately that can be used for different purposes like tool kit assistance, helping management system automatically.
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Hasan, M.W., Ferdosh Nima, J., Sultana, N., Ahmed Foysal, M.F., Karim, E. (2021). Exer-NN: CNN-Based Human Exercise Pose Classification. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1300. Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_34
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DOI: https://doi.org/10.1007/978-981-33-4367-2_34
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