Classification and comparison of human activities by machine learning

The current study of human activity recognition and classification has been an important part of promoting the development of science and technology in society. Human activity recognition and classification are in several fields, such as competitive sports, criminal investigation field, etc. As the field of micro-electromechanics continues to evolve, more accurate human recognition is becoming possible, with wearable multi-axis inertial sensors that allow us to visually detect the desired data. In this paper, the data of 19 human activities for 8 testers are feature extracted and normalized. The data are divided into training and test sets by machine learning models: support vector machine (SVR) classification, XGBoost classification, and logistic regression. The experiment was repeated 10 times to take the average value. The models were then scored, and by comparing integrated machine learning with traditional machine learning, it was found that integrated learning improved by 5%−29% in terms of accuracy compared to traditional machine learning.