Estimating changes in metabolic power from EMG

Blake, Ollie M.; Wakeling, James M.

doi:10.1186/2193-1801-2-229

Cited by 30 publications

(21 citation statements)

References 16 publications

(19 reference statements)

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“…Researchers have also built data-driven models using wearable sensors or data that could be collected with wearable sensors, with the aim of portable energy expenditure estimation. Many different sensors have been employed, including accelerometers [22][23][24], inertial measurement units, [25,26], heart rate monitors [27][28][29], immobile electromyography (EMG) systems [30][31][32], and various combinations [33][34][35]. With the exception of [36], these studies used linear regression and hand-designed features to estimate energy expenditure.…”

Section: Introductionmentioning

confidence: 99%

Rapid energy expenditure estimation for ankle assisted and inclined loaded walking

Slade

Troutman

Kochenderfer

et al. 2019

J NeuroEngineering Rehabil

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Background: Estimating energy expenditure with indirect calorimetry requires expensive equipment and several minutes of data collection for each condition of interest. While several methods estimate energy expenditure using correlation to data from wearable sensors, such as heart rate monitors or accelerometers, their accuracy has not been evaluated for activity conditions or subjects not included in the correlation process. The goal of our study was to develop data-driven models to estimate energy expenditure at intervals of approximately one second and demonstrate their ability to predict energetic cost for new conditions and subjects. Model inputs were muscle activity and vertical ground reaction forces, which are measurable by wearable electromyography electrodes and pressure sensing insoles. Methods: We developed models that estimated energy expenditure while walking (1) with ankle exoskeleton assistance and (2) while carrying various loads and walking on inclines. Estimates were made each gait cycle or four second interval. We evaluated the performance of the models for three use cases. The first estimated energy expenditure (in Watts) during walking conditions for subjects with some subject specific training data available. The second estimated all conditions in the dataset for a new subject not included in the training data. The third estimated new conditions for a new subject. Results: The mean absolute percent errors in estimated energy expenditure during assisted walking conditions were 4.4%, 8.0%, and 8.1% for the three use cases, respectively. The average errors in energy expenditure estimation during inclined and loaded walking conditions were 6.1%, 9.7%, and 11.7% for the three use cases. For models not using subject-specific data, we evaluated the ability to order the magnitude of energy expenditure across conditions. The average percentage of correctly ordered conditions was 63% for assisted walking and 87% for incline and loaded walking. Conclusions: We have determined the accuracy of estimating energy expenditure with data-driven models that rely on ground reaction forces and muscle activity for three use cases. For experimental use cases where the accuracy of a data-driven model is sufficient and similar training data is available, standard indirect calorimetry could be replaced. The models, code, and datasets are provided for reproduction and extension of our results.

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Section: Introductionmentioning

confidence: 99%

Rapid energy expenditure estimation for ankle assisted and inclined loaded walking

Slade

Troutman

Kochenderfer

et al. 2019

J NeuroEngineering Rehabil

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“…Where a parabolic relationship was evident, a best-fit 2nd-or 3rd-order polynomial was determined for the data with a linear least-squares fit (Mathematica version 10; Wolfram Research, Champaign, IL) to estimate the cadence at which minimum pedal forces occurred. The relative efficiency was estimated as the ratio of mechanical power output measured at the pedals to the sum of the total EMG intensities across all muscles, where total EMG intensity was used as a proxy for the metabolic power required for cycling (Blake and Wakeling 2013;Wakeling et al 2011). The relationships between cadence and both efficiency and total EMG intensity across all muscles were determined at each pedal cycle, and best-fit polynomials were fit to the data to estimate the cadence at which efficiency was maximized and total EMG intensity was minimized.…”

Section: Methodsmentioning

confidence: 99%

“…This partially explains evidence that efficiency is maximized at increasing cadences for increasing power outputs (Böning et al 1984;Coast and Welch 1985;Foss and Hallén 2004;Hagberg et al 1981;Seabury et al 1977). Obtaining greater efficiency by increasing cadence with workload may be the result of reduced muscle excitation since minimum muscle excitation also occurs at increasing cadences with increasing submaximal workloads (MacIntosh et al 2000), despite different muscle excitationworkload-cadence relationships seen in individual muscles (Blake and Wakeling 2013;Hug et al 2004;Lawrence and De Luca 1983;MacIntosh et al 2000).…”

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confidence: 99%

Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands

Blake

Wakeling

2015

Journal of Neurophysiology

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Blake OM, Wakeling JM. Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands. J Neurophysiol 114: 3283-3295, 2015. First published October 7, 2015 doi:10.1152/jn.00765.2015.-This study investigated the influence of cycle frequency and workload on muscle coordination and the ensuing relationship with mechanical efficiency and power output of human limb movement. Eleven trained cyclists completed an array of cycle frequency (cadence)-power output conditions while excitation from 10 leg muscles and power output were recorded. Mechanical efficiency was maximized at increasing cadences for increasing power outputs and corresponded to muscle coordination and muscle fiber type recruitment that minimized both the total muscle excitation across all muscles and the ineffective pedal forces. Also, maximum efficiency was characterized by muscle coordination at the top and bottom of the pedal cycle and progressive excitation through the uniarticulate knee, hip, and ankle muscles. Inefficiencies were characterized by excessive excitation of biarticulate muscles and larger duty cycles. Power output and efficiency were limited by the duration of muscle excitation beyond a critical cadence (120 -140 rpm), with larger duty cycles and disproportionate increases in muscle excitation suggesting deteriorating muscle coordination and limitations of the activation-deactivation capabilities. Most muscles displayed systematic phase shifts of the muscle excitation relative to the pedal cycle that were dependent on cadence and, to a lesser extent, power output. Phase shifts were different for each muscle, thereby altering their mechanical contribution to the pedaling action. This study shows that muscle coordination is a key determinant of mechanical efficiency and power output of limb movement across a wide range of mechanical demands and that the excitation and coordination of the muscles is limited at very high cycle frequencies. electromyography; excitation; power output; cadence; cycling; motor control HUMAN LIMB MOVEMENT IS ACHIEVED through interactions between the neuromuscular and skeletal systems. Movement performance is commonly measured by the resultant mechanical efficiency (ratio of mechanical power output to metabolic power) and power output, both of which are significantly influenced by the coordinated excitation of individual muscles and the coordination of that excitation between muscles Dorel et al. 2012;Samozino et al. 2007;Wakeling et al. 2010). Therefore, investigating movement performance requires a comprehensive understanding of how the individual muscle excitations and the coordination between muscles (muscle coordination) change in response to the demands.Mechanical demands such as workload and movement velocity can be uncoupled during cycling (Wakeling et al. 2006;Wakeling and Horn 2009), making it an appropriate model to investigate the effects of mechanical demands on individual muscle excitation and muscle coordination and the resultant performan...

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“…Data-driven methods used wearable sensors to explore portable energy expenditure estimation. Many different sensors were employed, such as accelerometers [19,20,21], activity monitors [22,23], heart rate monitors [24,25,26], electromyography (EMG) systems [27,28,29], and various combinations [30,31,32]. With the exception of [33], these studies used linear regression and hand-designed features to fit sensor data to estimate energy expenditure.…”

Section: Introductionmentioning

confidence: 99%

Rapid energy expenditure estimation for assisted and inclined loaded walking

Slade

Troutman

Kochenderfer

et al. 2018

Preprint

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Background: Estimating energy expenditure with indirect calorimetry requires expensive equipment and provides slow and noisy measurements. Rapid estimates using wearable sensors would enable techniques like optimizing assistive devices outside a lab. Existing methods correlate data from wearable sensors to measured energy expenditure without evaluating the accuracy of the estimated energy expenditure for activity conditions or subjects not included in the correlation process. Our goal is to assess data-driven models that are capable of rapidly estimating energy expenditure for new conditions and subjects.

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Estimating changes in metabolic power from EMG

Cited by 30 publications

References 16 publications

Rapid energy expenditure estimation for ankle assisted and inclined loaded walking

Rapid energy expenditure estimation for ankle assisted and inclined loaded walking

Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands

Rapid energy expenditure estimation for assisted and inclined loaded walking

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