ArticleSpringerPlus2013
Estimating changes in metabolic power from EMG.
Article in SpringerPlus, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- User preference in the personalized control of an ankle prosthesis: a case study.Journal of neuroengineering and rehabilitation · 2026Article
- Vision-based multimodal energy expenditure estimation for aerobic exercise in adults.Frontiers in physiology · 2025Article
- Human-in-the-loop optimization of wearable device parameters using an EMG-based objective function.Wearable technologies · 2024Article
- Linking muscle mechanics to the metabolic cost of human hopping.The Journal of experimental biology · 2023Article
- Muscle coordination and recruitment during squat assistance using a robotic ankle-foot exoskeleton.Scientific reports · 2023Article
- The interplay between gastrocnemius medialis force-length and force-velocity potentials, cumulative EMG activity and energy cost at speeds above and below the walk to run transition speed.Experimental physiology · 2023Article
- Foot contact forces can be used to personalize a wearable robot during human walking.Scientific reports · 2022Article
- A Haptic Sleeve as a Method of Mechanotactile Feedback Restoration for Myoelectric Hand Prosthesis Users.Frontiers in rehabilitation sciences · 2022Article
- Ankle Muscle Activations during Different Foot-Strike Patterns in Running.Sensors (Basel, Switzerland) · 2021Article
- Rapid energy expenditure estimation for ankle assisted and inclined loaded walking.Journal of neuroengineering and rehabilitation · 2019Article
- Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors.Journal of applied physiology (Bethesda, Md. : 1985) · 2019Article
- Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands.Journal of neurophysiology · 2015Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic rates can increase 21 times above resting levels during cycling with the majority attributed to muscular contractions. Metabolic estimates attained through gas exchange parameters are limited by the respiration rate and time delay with respect to these contractions. In contrast surface electromyography (EMG) contains instantaneous muscle contraction information at higher temporal resolutions. An adequate metabolic power-EMG relationship has not been established to use EMG as a metabolic estimate during dynamic activities. The purpose of this study was to establish a metabolic power-EMG relationship during non steady-state conditions. Participants cycled at workloads between 25 and 90% O2max while EMG and gas exchange were monitored. The EMG was resolved into intensities and total EMG intensity was calculated as the sum of intensities across all muscles for each pedal cycle. Metabolic power was estimated from gas exchange parameters and the mean total EMG intensity between breaths was calculated and used as breath-by-breath values. Comparisons were made between breath-by-breath resolutions of metabolic power and total EMG intensity. Different weighting coefficients were also applied to the EMG for each muscle to analyze the effects of different muscle weightings on metabolic power estimations. There was a significant correlation (r = 0.91) between estimates of metabolic power from EMG and gas exchange. Muscle weighting had a significant effect on metabolic power determination with the highest and lowest correlated estimates having the largest weightings on muscles proximal and distal to the knee respectively. This study demonstrates that EMG contains important information about the metabolic costs of muscle contractions and provides good predictions of metabolic changes during non steady-state conditions. Also, the importance of each muscle is workload dependent with inappropriate weightings reducing metabolic estimations. These findings have implications for future EMG applications as they provide more immediate, higher temporal resolution predictions of changes in metabolic power.
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