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논문검색

A Study on the Accuracy Improvement of One-repetition Maximum based on Deep Neural Network for Physical Exercise

초록

영어

In this paper, we conducted a study that utilizes deep learning to calculate appropriate physical exercise information when basic human factors such as sex, age, height, and weight of users come in. To apply deep learning, a method was applied to calculate the amount of fat needed to calculate the amount of one repetition maximum by utilizing the structure of the basic Deep Neural Network. By applying Accuracy improvement methods such as Relu, Weight initialization, and Dropout to existing deep learning structures, we have improved Accuracy to derive a lean body weight that is closer to actual results. In addition, the results were derived by applying a formula for calculating the one repetition maximum load on upper and lower body movements for use in actual physical exercise. If studies continue, such as the way they are applied in this paper, they will be able to suggest effective physical exercise options for different conditions as well as conditions for users.

목차

Abstract
1. Introduction
2. Deep Learning Methods and Accuracy Improvement
2.1 DNN Algorithm
2.2 Accuracy Improvement procedure of deep learning results
3. RM calculation method
4. Conclusion
References

저자정보

  • Byung-Hoon Lee Radio & Communications Engineering, Chungbuk National University, Korea
  • Myeong-Jin Kim Radio & Communications Engineering, Chungbuk National University, Korea
  • Kyung-Seok Kim Radio & Communications Engineering, Chungbuk National University, Korea

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