ArticleFrontiers in neuroscience2019
Effect of User Practice on Prosthetic Finger Control With an Intuitive Myoelectric Decoder.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding.Scientific reports · 2026Article
- SRM: A Source-Reprojection Module for Cross-Day sEMG Gesture Recognition.Sensors (Basel, Switzerland) · 2026Article
- Article
- Preliminary Investigation of Myoelectric Control of an Assistive Neck Exoskeleton by Individuals With Amyotrophic Lateral Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Enhanced spectro-temporal feature extraction for prosthetic control using variational mode decomposition.Scientific reports · 2025Article
- A database of multimodal myography and hand kinematics during realistic daily life activities.Scientific data · 2025Article
- Surface EMG Sensing and Granular Gesture Recognition for Rehabilitative Pouring Tasks: A Case Study.Biomimetics (Basel, Switzerland) · 2025Article
- Simultaneous and Proportional Myoelectric Control of Multiple Degrees of Freedom in Individuals With Chronic Hemiparesis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Training Movement Velocity Significantly Affects the Performance of Myoelectric Control.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- A Multimodal Bracelet to Acquire Muscular Activity and Gyroscopic Data to Study Sensor Fusion for Intent Detection.Sensors (Basel, Switzerland) · 2024Article
- Context-informed incremental learning improves both the performance and resilience of myoelectric control.Journal of neuroengineering and rehabilitation · 2024Article
- Biosignal-integrated robotic systems with emerging trends in visual interfaces: A systematic review.Biophysics reviews · 2024Review
- Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Correcting Temporal Inaccuracies in Labeled Training Data for Electromyographic Control Algorithms.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2023Article
- Recent trends and challenges of surface electromyography in prosthetic applications.Biomedical engineering letters · 2023Review
- A New Labeling Approach for Proportional Electromyographic Control.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of User-Prosthesis-Interfaces for sEMG-Based Multifunctional Prosthetic Hands.Sensors (Basel, Switzerland) · 2021Article
- Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning.Scientific reports · 2021Article
- Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control.Journal of neuroengineering and rehabilitation · 2021Article
- Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals.Frontiers in neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning-based myoelectric control is regarded as an intuitive paradigm, because of the mapping it creates between muscle co-activation patterns and prosthesis movements that aims to simulate the physiological pathways found in the human arm. Despite that, there has been evidence that closed-loop interaction with a classification-based interface results in user adaptation, which leads to performance improvement with experience. Recently, there has been a focus shift toward continuous prosthesis control, yet little is known about whether and how user adaptation affects myoelectric control performance in dexterous, intuitive tasks. We investigate the effect of short-term adaptation with independent finger position control by conducting real-time experiments with 10 able-bodied and two transradial amputee subjects. We demonstrate that despite using an intuitive decoder, experience leads to significant improvements in performance. We argue that this is due to the lack of an utterly natural control scheme, which is mainly caused by differences in the anatomy of human and artificial hands, movement intent decoding inaccuracies, and lack of proprioception. Finally, we extend previous work in classification-based and wrist continuous control by verifying that offline analyses cannot reliably predict real-time performance, thereby reiterating the importance of validating myoelectric control algorithms with real-time experiments.
Indexed as
Identifiers
What OpenQuestion holds
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.