Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4973580 | Biomedical Signal Processing and Control | 2017 | 11 Pages |
Abstract
The random forests models showed very good prediction accuracy and attained the coefficient of determination R2Â =Â 0.962 for lactate concentration level and R2Â =Â 0.980 for oxygen uptake rate. The linear models showed lower prediction accuracy. Comparable results were obtained also when sEMG amplitude data were removed from the training sets. A feature elimination algorithm allowed to build accurate random forests (R2Â >Â 0.9) using just six (lactate concentration level) or four (oxygen uptake rate) time-domain variables. Models created to predict blood lactate concentration rate relied on variables reflecting interaction between front and back leg muscles, while parameters computed from front muscles and interactions between two legs were the most important variables for models created to predict oxygen uptake rate.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Petras Ražanskas, Antanas Verikas, Per-Arne Viberg, M. Charlotte Olsson,