Aerobic Fitness Level Estimation Using Wearables

Radovan Smisek1, Andrea Nemcova1, Lukas Smital1, Daniela Chlibkova2, Martin Kralik1, Jana Kolarova3, Vojtech Myska4, Martin Kolarik4, Michal Harvanek4, Jakub Arm4, Ondrej Bastan4, Martin Pospisil4, Jaromir Hubalek1
1Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, 2Brno University of Technology, Centre of Sports Activities, 3Brno University of Technology, 4Brno University of Technology, Faculty of Electrical Engineering and Communication


Background: Aerobic fitness level (AFL) is a parameter closely related to a person's overall health. The gold standard of measurement is currently using expensive laboratory equipment.
Aims: This study aimed to estimate AFL automatically using data measured with wearables. Methods: AFL was estimated in 2D space. The first dimension is the exertion level, and the second is the body's response to the exertion. Exertion level was determined based on metabolic equivalent calculated for each classified activity using the data of speed and elevation. The activity classification is based on deep neural networks. The body's response estimation is based on heart rate calculated from ECG or PPG. The test set contained 27 subjects. The reference was measured under laboratory conditions using the gold standard method. AFL classification by ACSM guidelines was used. Results: AFL determined by our algorithm were 0.44±0.09, 0.50±0.10, 0.53±0.09, 0.58±0.15, and 0.70±0.07 for the reference classes very poor, poor, fair, good, and excellent, respectively. The correlation between the reference and determined values is 0.76. Conclusion: Our method showed promising results and will be further developed.