ArticleJournal of neural engineering2018
Online adaptive neural control of a robotic lower limb prosthesis.
Article in Journal of neural engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 130 citations in OpenAlex.
- Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices-A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Lower-limb prosthetic mechanisms: recent progress, control innovations and barriers to real-world adoption.Biomedical engineering online · 2026Review
- A Deep Learning Framework With Domain Generalization and Few-Shot Learning for Locomotion Mode Classification Across Users, Sessions, and Prostheses.IEEE transactions on medical robotics and bionics · 2026Article
- Real-Time Balancing of Stability and Plasticity in Continual Learning Enables Adaptive Speed Estimation for Lower-Limb Prostheses.IEEE transactions on medical robotics and bionics · 2025Article
- Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration.Journal of neuroengineering and rehabilitation · 2025Article
- Real-time Adaptation of Deep Learning Walking Speed Estimators Enables Biomimetic Assistance Modulation in an Open-Source Bionic Leg.IEEE transactions on medical robotics and bionics · 2025Article
- Myoelectric Control in Rehabilitative and Assistive Soft Exoskeletons: A Comprehensive Review of Trends, Challenges, and Integration with Soft Robotic Devices.Biomimetics (Basel, Switzerland) · 2025Review
- Intelligent prosthetic hips and knees: from actuation to perception and control.Frontiers in neuroscience · 2025Review
- Unified Control of a Powered Knee-Ankle Prosthesis Enables Walking, Stairs, Transitions, and Other Daily Ambulation Activities.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 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
- Recent trends and challenges of surface electromyography in prosthetic applications.Biomedical engineering letters · 2023Review
- Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review.IEEE transactions on medical robotics and bionics · 2023Article
- Toward higher-performance bionic limbs for wider clinical use.Nature biomedical engineering · 2023Review
- 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
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee's neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. APPROACH: We present a powered lower limb prosthesis that was configured to acquire the user's neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user's intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user's model of neural data during ambulation. MAIN
resultsOur proposed algorithm accurately and consistently identified the user's intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm-with a 6.66% [3.16%] relative decrease in error rate. SIGNIFICANCE: This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user's intent with low error rates.
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.