Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise

Sensors

Petras Ra៪nskas²  Antanas Verikas²  Charlotte Olsson¹  Per-Arne Viberg³ 

[1]Biological and Environmental Systems Laboratory, Halmstad University, P.O. Box 823, Halmstad S-30118, Sweden

[2] E-Mail:

[3]Department of Electric Power Systems, Kaunas University of Technology, Studentų g. 50, Kaunas LT-51368, Lithuania

[4]Swedish Adrenaline, Pilefeltsgatan 73, S-30250 Halmstad, Sweden

[5] E-Mail:

关键词: blood lactate concentration;    cycling;    surface electromyography;    oxygen uptake;    random forest;    ridge regression;   

DOI  :  10.3390/s150820480

来源: mdpi

【 摘 要 】

This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from(for oxygen uptake) were obtained when using random forest regressors.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.