ArticleSensors (Basel, Switzerland)2017
An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.
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 26 papers, 1 of them a synthesis that pooled it.
What it found
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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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices-A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Vibrotactile Haptic and Gesture Feedback in a Smartwatch for Controlling a Multi-Activity Powered Knee-Ankle Prosthesis.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Ambilateral Activity Recognition and Continuous Adaptation with a Powered Knee-Ankle Prosthesis.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2025Article
- Bilateral Elimination Rule-Based Finite Class Bayesian Inference System for Circular and Linear Walking Prediction.Biomimetics (Basel, Switzerland) · 2024Article
- Robustification of Bayesian-Inference-Based Gait Estimation for Lower-limb Wearable Robots.IEEE robotics and automation letters · 2024Article
- Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review.IEEE transactions on medical robotics and bionics · 2023Article
- Vision-Based Recognition of Human Motion Intent during Staircase Approaching.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2023Article
- The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development.Frontiers in neuroscience · 2023Article
- Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Adaptive Lower Limb Pattern Recognition for Multi-Day Control.Sensors (Basel, Switzerland) · 2022Article
- Analysis of the Bayesian Gait-State Estimation Problem for Lower-Limb Wearable Robot Sensor Configurations.IEEE robotics and automation letters · 2022Article
- Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis.IEEE transactions on bio-medical engineering · 2022Article
- Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees.Wearable technologies · 2022Article
- The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction.Sensors (Basel, Switzerland) · 2021Article
- Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions.Journal of neural engineering · 2021Review
- Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration.Sensors (Basel, Switzerland) · 2021Article
- Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU.Sensors (Basel, Switzerland) · 2021Article
- Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review.Cyborg and bionic systems (Washington, D.C.) · 2021Review
- Continuous Myoelectric Prediction of Future Ankle Angle and Moment Across Ambulation Conditions and Their Transitions.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
Algorithms for locomotion mode recognition (LMR) based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM), were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.
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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.