Difference between revisions of "Publications:Time Domain Features of Multi-channel EMG Applied to Prediction of Physiological Parameters in Fatiguing Bicycling Exercises"
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| − | |Name=Razanskas, Petras (Kaunas University of Technology, Kaunas, Lithuania);Verikas, Antanas | + | |Name=Razanskas, Petras (Kaunas University of Technology, Kaunas, Lithuania);Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Olsson, Charlotte (chaols) (Högskolan i Halmstad (2804), Akademin för ekonomi, teknik och naturvetenskap (16901), Bio- och miljösystemforskning (BLESS) (3907));Wiberg, Per-Arne (pelle) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650)) |
|Title=Time Domain Features of Multi-channel EMG Applied to Prediction of Physiological Parameters in Fatiguing Bicycling Exercises | |Title=Time Domain Features of Multi-channel EMG Applied to Prediction of Physiological Parameters in Fatiguing Bicycling Exercises | ||
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Latest revision as of 22:41, 30 September 2016
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| Title | Time Domain Features of Multi-channel EMG Applied to Prediction of Physiological Parameters in Fatiguing Bicycling Exercises |
|---|---|
| Author | |
| Year | 2015 |
| PublicationType | Journal Paper |
| Journal | Frontiers in Artificial Intelligence and Applications |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.3233/978-1-61499-589-0-118 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:862405 |
| Abstract | A set of novel time-domain features characterizing multi-channel surface EMG (sEMG) signals of six muscles (rectus femoris, vastus lateralis, and semitendinosus of each leg) is proposed for prediction of physiological parameters considered important in cycling: blood lactate concentration and oxygen uptake. Fifty one different features, including phase shifts between muscles, active time percentages, sEMG amplitudes, as well as symmetry measures between both legs, were defined from sEMG data and used to train linear and random forest models. The random forests models achieved the coefficient of determination R2 = 0:962 (lactate) and R2 = 0:980 (oxygen). The linear models were less accurate. Feature pruning applied enabled creating accurate random forest models (R2 >0:9) using as few as 7 (lactate) or 4 (oxygen) time-domain features. sEMG amplitude was important for both types of models. Models to predict lactate also relied on measurements describing interaction between front and back muscles, while models to predict oxygen uptake relied on front muscles only, but also included interactions between the two legs. |
