Fitness Tracker Data Analytics

• Published in: Control systems and computers (Online) 2024-07 (2 (306)), p.65-76
• Main Authors: Bychkov, Oleksii S., Gezerdava, Oleksandr V., Dukhnovska, Kseniia K., Kovtun, Oksana I., Leshchenko, Olga O.
• Format: Article
• Language: English
• ISSN: 2706-8145; 2706-8153
• DOI: 10.15407/csc.2024.02.065

The health status of patients is recorded in various sources, such as medical records, portable devices (smart watches, fitness trackers, etc.), forming a characteristic current health status of patients. The goal of the study was the development of medical card software for the analysis of data from fitness bracelets. This will provide an opportunity to collect data for further use of cluster analysis and improvement of the functionality and accuracy of medical monitoring. The object of the study is the use of linear regression to analyze and predict heart rate based on data collected using fitness bracelets. In order to solve this problem, an information system was developed that uses linear regression to analyze the effect of parameters such as Very Active Distance, Fairly Active Minutes, and Calories on the heart rate (Value). Training and validation were performed on data from fitness bracelets. The results confirm the effectiveness of linear regression in predicting heart rate based on the parameters of fitness bracelets. The accuracy of the model was compared under the conditions of aggregation and without it, which allows us to draw conclusions about the optimal conditions for using linear regression for the analysis of fitness data. The study proves the adequacy of the obtained results according to the Student’s criterion. The calculated Student’s t test is 1.31, with the critical test ¾ 2.62. Which proves the adequacy of the developed model. The results of the study confirm that the linear regression model is an effective tool for individual monitoring and optimization of physical activity based on data from fitness bracelets. It is worth considering that the use of linear regression has its limitations and is not always the best choice for complex nonlinear dependencies. In such cases, other machine learning methods may need to be considered.