ArticleSensors (Basel, Switzerland)2017
A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Regional differences in amplitude and spatial homogeneity of muscle activity in the biceps femoris long head.European journal of applied physiology · 2025Article
- Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions.Frontiers in neuroscience · 2022Article
- Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis.Frontiers in bioengineering and biotechnology · 2022Article
- Alteration in HDEMG Spatial Parameters of Trunk Muscle Due to Handle Design during Pushing.Sensors (Basel, Switzerland) · 2021Article
- High-Density Electromyography Provides Improved Understanding of Muscle Function for Those With Amputation.Frontiers in medical technology · 2021Article
- High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans.Scientific data · 2020Article
- An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.Sensors (Basel, Switzerland) · 2017Article
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Authors and funding
5 authors.
Funding
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Abstract
Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.
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Registered trials
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