ArticlePloS one2015
Robust muscle activity onset detection using an unsupervised electromyogram learning framework.
Article in PloS one, 2015. 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.
- Trial
- Electromyography Signal Acquisition, Filtering, and Data Analysis for Exoskeleton Development.Sensors (Basel, Switzerland) · 2025Review
- Enhancing uterine contraction detection through novel EHG signal processing: a pilot study leveraging the relationship between slow and fast wave components to improve signal quality and noise resilience.Frontiers in physiology · 2025Article
- Review of electromyography onset detection methods for real-time control of robotic exoskeletons.Journal of neuroengineering and rehabilitation · 2023Review
- Review
- Application of Surface Electromyography in Exercise Fatigue: A Review.Frontiers in systems neuroscience · 2022Review
- Predicting postural control adaptation measuring EEG, EMG, and center of pressure changes: BioVRSea paradigm.Frontiers in human neuroscience · 2022Article
- Comparing the Accuracy of Visual and Computerized Onset Detection Methods on Simulated Electromyography Signals with Varying Signal-to-Noise Ratios.Journal of functional morphology and kinesiology · 2021Article
- Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks.Journal of neuroengineering and rehabilitation · 2019Article
- [Onset detection of surface diaphragmatic electromyography based on sample entropy and individualized threshold].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2018Article
- Unsupervised Stochastic Strategies for Robust Detection of Muscle Activation Onsets in Surface Electromyogram.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2018Article
- Active Braking of Whole-Arm Reaching Movements Provides Single-Trial Neuromuscular Measures of Movement Cancellation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in which the two components respectively correspond to the posterior distribution of EMG burst and non-burst logarithmic powers. The parameter set of the GMM was sequentially estimated based on maximum likelihood, subject to constraints derived from the relationship between EMG burst and non-burst distributions. An optimal threshold for EMG burst/non-burst classification was determined using the GMM at each frequency band, and the final decision was obtained by a voting procedure. The proposed novel framework was applied to simulated and experimental surface EMG signals for muscle activity onset detection. Compared with conventional approaches, it demonstrated robust performance for low and changing signal to noise ratios in a dynamic environment. The framework is applicable for real-time implementation, and does not require the assumption of non EMG burst in the initial stage. Such features facilitate its practical application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.