Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing. (May 2017)

• Record Type: Journal Article
• Authors: Ražanskas, Petras; Verikas, Antanas; Viberg, Per-Arne; Olsson, M. Charlotte
• Is Part Of: Biomedical signal processing and control. Volume 35(2017)
• Page Start: 19
• Page End: 29
• Publication Date: 2017-05
• Subjects: Random forests -- Surface electromyography -- Muscle activation patterns -- Fatigue detection -- Bicycling; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.02.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: A novel approach to predicting physiological parameters from EMG data is presented. Blood lactate concentration prediction models rely on interaction between muscles of one leg. Oxygen uptake prediction models are reliant on interaction between legs. Timing-event features provided higher prediction accuracy than frequency domain features. Abstract: This article is concerned with a novel technique for prediction of blood lactate concentration level and oxygen uptake rate from multi-channel surface electromyography (sEMG) signals. The approach is built on predictive models exploiting a set of novel time-domain variables computed from sEMG signals. Signals from three muscles of each leg, namely, vastus lateralis, rectus femoris, and semitendinosus were used in this study. The feature set includes parameters reflecting asymmetry between legs, phase shifts between activation of different muscles, active time percentages, and sEMG amplitude. Prediction ability of both linear and non-linear (random forests-based) models was explored. The random forests models showed very good prediction accuracy and attained the coefficient of determination R 2 = 0.962 for lactate concentration level and R 2 = 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 ( R 2 > 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.