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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.
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.
