title: EMG, heart rate, and accelerometer as estimators of energy expenditure in locomotion.
abstract: OBJECTIVE
  Precise measures of energy expenditure (EE) during everyday activities are needed. This study assessed the validity of novel shorts measuring EMG and compared this method with HR and accelerometry (ACC) when estimating EE.
  
  
  METHODS
  Fifty-four volunteers (39.4 ± 13.9 yr) performed a maximal treadmill test (3-min loads) including walking with different speeds uphill, downhill, and on level ground and one running load. The data were categorized into all, low, and level loads. EE was measured by indirect calorimetry, whereas HR, ACC, and EMG were measured continuously. EMG from quadriceps (Q) and hamstrings (H) was measured using shorts with textile electrodes. Validity of the methods used to estimate EE was compared using Pearson correlations, regression coefficients, linear mixed models providing Akaike information criteria, and root mean squared error (RMSE) from cross-validation at the individual and population levels.
  
  
  RESULTS
  At all loads, correlations with EE were as follows: EMG(QH), 0.94 ± 0.03; EMG(Q), 0.91 ± 0.03; EMG(H), 0.94 ± 0.03; HR, 0.96 ± 0.04; and ACC, 0.77 ± 0.10. The corresponding correlations at low loads were 0.89 ± 0.08, 0.79 ± 0.10, 0.93 ± 0.07, 0.89 ± 0.23, and 0.80 ± 0.07, and at level loads, they were 0.97 ± 0.03, 0.97 ± 0.05, 0.96 ± 0.04, 0.95 ± 0.08, and 0.99 ± 0.02, respectively. Akaike information criteria ranked the methods in accordance with the individual correlations.
  
  
  CONCLUSIONS
  It is shown for the first time that EMG shorts can be used for EE estimations across a wide range of physical activity intensities in a heterogeneous group. Across all loads, HR is a superior method of predicting EE, whereas ACC is most accurate for level loads at the population level. At low levels of physical activity in changing terrains, thigh muscle EMG provides more accurate EE estimations than those in ACC and HR if individual calibrations are performed.
authors: 1Neuromuscular Research Center, Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, FINLAND; 2Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, FINLAND; and 3Department of Medical Rehabilitation, Oulu University Hospital, Oulu, FINLAND.
authors: Olli
authors: O
authors: Tikkanen
authors: Salme
authors: S
authors: Kärkkäinen
authors: Piia
authors: P
authors: Haakana
authors: Mauri
authors: M
authors: Kallinen
authors: Teemu
authors: T
authors: Pullinen
authors: Taija
authors: T
authors: Finni
mesh_terms: D061725:Accelerometry; D000328:Adult; D003020:Clothing; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation*; D004734:Energy Metabolism / Q000502:physiology*; D005080:Exercise Test; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D005082:Physical Exertion / Q000502:physiology*; D052097:Quadriceps Muscle / Q000502:physiology; D012420:Running / Q000502:physiology; D016138:Walking / Q000502:physiology