ArticleSensors (Basel, Switzerland)2015
A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Personalized gait trajectory generation for lower-limb rehabilitation robots using anthropometric features.iScience · 2026Article
- Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Review
- AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review.Frontiers in robotics and AI · 2024Review
- Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs.Physical and engineering sciences in medicine · 2023Article
- Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications.Sensors (Basel, Switzerland) · 2023Article
- Ground-Reaction-Force-Based Gait Analysis and Its Application to Gait Disorder Assessment: New Indices for Quantifying Walking Behavior.Sensors (Basel, Switzerland) · 2022Article
- Sensors and Actuation Technologies in Exoskeletons: A Review.Sensors (Basel, Switzerland) · 2022Review
- Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion.Applied bionics and biomechanics · 2022Review
- Article
- A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton.Frontiers in neurorobotics · 2021Article
- Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units.Biosensors · 2020Article
- EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges.Biosensors · 2020Review
- A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses.Sensors (Basel, Switzerland) · 2020Review
- Validation of Marker-Less System for the Assessment of Upper Joints Reaction Forces in Exoskeleton Users.Sensors (Basel, Switzerland) · 2020Article
- Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton.Sensors (Basel, Switzerland) · 2020Article
- Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks.Frontiers in bioengineering and biotechnology · 2020Article
- Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method.Sensors (Basel, Switzerland) · 2019Article
- Mechanical vibration does not systematically reduce the tremor in essential tremor patients.Scientific reports · 2019Article
- Curve Similarity Model for Real-Time Gait Phase Detection Based on Ground Contact Forces.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.
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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.