ArticleSensors (Basel, Switzerland)2021
Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- sEMG-Based Motion Intention Recognition for Interactive Upper Limb Nursing Assistance.Sensors (Basel, Switzerland) · 2026Article
- Towards naturalistic prosthetics: enhancing motor control with sensory feedback mechanism.Annals of medicine and surgery (2012) · 2025Article
- Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration.Journal of neuroengineering and rehabilitation · 2025Article
- An adaptive segmentation scheme based on recurring action potentials for sEMG controlled movement decoding.Physical and engineering sciences in medicine · 2025Article
- Influence of electrode placement on the recognition of different gesture categories using high-density sEMG.Frontiers in neuroscience · 2025Article
- Surface electromyography evaluation for decoding hand motor intent in children with congenital upper limb deficiency.Scientific reports · 2024Article
- SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force from Highly Sparse Ultrasound Images.IEEE transactions on human-machine systems · 2024Article
- Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot.Sensors (Basel, Switzerland) · 2022Article
- Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (
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