A Gravitational-Double Layer Extreme Learning Machine and its Application in Powerlifting Analysis

Vinh Huy Chau,Ba Tuan Le,Anh Thu Vo

doi:10.1109/access.2019.2944877

Abstract

Powerlifting is a strength sport that is quite popular in the world. Powerlifters have their power levels varied at different ages and body weights, and their power levels are closely related to their performance. Therefore, studying the impact of age and weight on the performance of powerlifters is an important work. The traditional method relies mainly on artificial experience to judge the performance, and often does not get the desired results. In recent years, machine learning has developed rapidly, and applying machine learning in sports is a very interesting topic. This study is based on a new machine learning algorithm to construct a prediction model for the best performance of powerlifters. We propose a double-layer extreme learning machine based on affine transformation and two-layer extreme learning machine theory (AF-DELM). Then use a dynamic weight-gravitational search algorithm to improve the AF-DELM networks. The results show that the algorithm can better predict the performance and provide an effective predictive aid for the powerlifting competition.

Highlights

In modern society, sports are getting more and more public attention
In order to evaluate the performance of the two algorithms, we combine the particle swarm optimization (PSO) [25]with the activation function - double-layer extreme learning machine (AF-DELM) network (PSO-AF-DELM), and compare the proposed algorithms with the PSO-AF-DELM, the affine transformation extreme learning machine (AT-ELM) [23] and the two hidden layer extreme learning machine (T-ELM) [20] algorithms
The results show that the stability of DG-AF-DELM algorithm is better than that of PSO-AF-DELM algorithm

Summary

Introduction

Sports are getting more and more public attention. Sports training is the main method to promote physical health, increase immunity, ensure human growth and development, enhance physical fitness, etc. For people of different ages, genders, occupations and different health conditions, appropriate exercise can be carried out depending to individual circumstances. According to estimates by the World Health Organization, the number of people worldwide who die from lack of exercise exceeds 2 million per year [4]. Lack of exercise can reduce the body’s immunity, and if people in the adolescence do not do enough physical exercise, this may have negative effects on the development of their brain and intelligence. In physical exercise, powerlifting is a long-established sport, and it is very common among people.

Methods

Results

Conclusion