2014
DOI: 10.1249/mss.0000000000000298
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EMG, Heart Rate, and Accelerometer as Estimators of Energy Expenditure in Locomotion

Abstract: 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.

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Cited by 42 publications
(42 citation statements)
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“…Heart rate is a superior method of predicting energy expenditure, whereas accelerometer is most accurate for level loads at the population level [20]. It is possible to estimate physical activity energy expenditure from heart rate in a group of individuals with a great deal of accuracy, after adjusting for age, gender, body mass and fitness [14].…”
Section: Discussionmentioning
confidence: 99%
“…Heart rate is a superior method of predicting energy expenditure, whereas accelerometer is most accurate for level loads at the population level [20]. It is possible to estimate physical activity energy expenditure from heart rate in a group of individuals with a great deal of accuracy, after adjusting for age, gender, body mass and fitness [14].…”
Section: Discussionmentioning
confidence: 99%
“…Four different sizes of shorts (XS, S, M, and L) and electrode paste (Redux Electrolyte Creme; Parker, Inc., Fairfield, NJ) were used to optimize the skin-electrode contact. The EMG shorts have been tested for validity, repeatability, and feasibility (10) and can be used to accurately estimate energy expenditure (35). Detailed descriptions of this method have been reported previously (10,34).…”
Section: Methodsmentioning
confidence: 99%
“…To further separate moderate-and vigorous-intensity activities, the moderate-to vigorous-intensity activity threshold was calculated as "light-to moderate-intensity threshold x 2." These thresholds were selected because they correspond to the energy expenditure at 3 and 6 METs, respectively (1,35). EMG data were analyzed with a custom-made MATLAB (The MathWorks, Inc., version 7.11.0.587) algorithm (for de tails, see Pesola et al [30]).…”
Section: Methodsmentioning
confidence: 99%
“…This study is one of very few that have measured muscle activity using EMG shorts, which have been reported to provide an accurate and detailed assessment of muscle activity across the entire continuum of daily life (Tikkanen et al 2013). Compared with analysis of activity outcomes from accelerometers and postural data (Chastin and Granat 2010), EMG as a direct measure of muscle activity provides further physiological insight into changes in muscle activity and inactivity time, and the associated cardio-metabolic impact when reducing sitting time (Tikkanen et al 2014;Pesola et al 2014Pesola et al , 2015.…”
Section: Strengths and Limitationsmentioning
confidence: 99%