Trial reportIEEE transactions on bio-medical engineering2012
Toward design of an environment-aware adaptive locomotion-mode-recognition system.
Trial report in IEEE transactions on bio-medical engineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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
16 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- EMG-driven control in lower limb prostheses: a topic-based systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices-A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Active lower limb prosthetics: a systematic review of design issues and solutions.Biomedical engineering online · 2016Pooled it
- Technology efficacy in active prosthetic knees for transfemoral amputees: a quantitative evaluation.TheScientificWorldJournal · 2014Pooled it
- Toward more intuitive prosthetic control: structured residual muscle training in transtibial amputees.Journal of neuroengineering and rehabilitation · 2026Article
- Review on Portable-Powered Lower Limb Exoskeletons.Sensors (Basel, Switzerland) · 2024Review
- Bilateral Elimination Rule-Based Finite Class Bayesian Inference System for Circular and Linear Walking Prediction.Biomimetics (Basel, Switzerland) · 2024Article
- Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit.Frontiers in neurorobotics · 2022Article
- The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction.Sensors (Basel, Switzerland) · 2021Article
- Review of control strategies for lower-limb exoskeletons to assist gait.Journal of neuroengineering and rehabilitation · 2021Review
- Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU.Sensors (Basel, Switzerland) · 2021Article
- Environment Classification for Robotic Leg Prostheses and Exoskeletons Using Deep Convolutional Neural Networks.Frontiers in neurorobotics · 2021Article
- ExoNet Database: Wearable Camera Images of Human Locomotion Environments.Frontiers in robotics and AI · 2020Article
- Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure.Sensors (Basel, Switzerland) · 2019Article
- Investigation of Timing to Switch Control Mode in Powered Knee Prostheses during Task Transitions.PloS one · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In this study, we aimed to improve the performance of a locomotion-mode-recognition system based on neuromuscular-mechanical fusion by introducing additional information about the walking environment. Linear-discriminant-analysis-based classifiers were first designed to identify a lower limb prosthesis user's locomotion mode based on electromyographic signals recorded from residual leg muscles and ground reaction forces measured from the prosthetic pylon. Nine transfemoral amputees who wore a passive hydraulic knee or powered prosthetic knee participated in this study. Information about the walking terrain was simulated and modeled as prior probability based on the principle of maximum entropy and integrated into the discriminant functions of the classifier. When the correct prior knowledge of walking terrain was simulated, the classification accuracy for each locomotion mode significantly increased and no task transitions were missed. In addition, simulated incorrect prior knowledge did not significantly reduce system performance, indicating that our design is robust against noisy and imperfect prior information. Furthermore, these observations were independent of the type of prosthesis applied. The promising results in this study may assist the further development of an environment-aware adaptive system for locomotion-mode recognition for powered lower limb prostheses or orthoses.
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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.