ArticleSensors (Basel, Switzerland)2016
Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integration of smart insoles for gait assessment in exoskeleton assisted rehabilitation.Scientific reports · 2025Article
- Review on Portable-Powered Lower Limb Exoskeletons.Sensors (Basel, Switzerland) · 2024Review
- Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton.Sensors (Basel, Switzerland) · 2024Article
- Recent Advances in Self-Powered Wearable Flexible Sensors for Human Gaits Analysis.Nanomaterials (Basel, Switzerland) · 2024Review
- A Low-Cost Instrumented Shoe System for Gait Phase Detection Based on Foot Plantar Pressure Data.IEEE journal of translational engineering in health and medicine · 2024Article
- Walking-Speed-Adaptive Gait Phase Estimation for Wearable Robots.Sensors (Basel, Switzerland) · 2023Article
- Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography.Sensors (Basel, Switzerland) · 2023Article
- Effects of age-related changes in trunk and lower limb range of motion on gait.BMC musculoskeletal disorders · 2023Article
- Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton.Sensors (Basel, Switzerland) · 2022Article
- Sensors and Actuation Technologies in Exoskeletons: A Review.Sensors (Basel, Switzerland) · 2022Review
- Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit.Frontiers in neurorobotics · 2022Article
- Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration.Sensors (Basel, Switzerland) · 2021Article
- A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton.Frontiers in neurorobotics · 2021Article
- 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
- Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method.Sensors (Basel, Switzerland) · 2019Article
- Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand.Sensors (Basel, Switzerland) · 2019Article
- Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking.Frontiers in neurorobotics · 2019Article
- ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses.Sensors (Basel, Switzerland) · 2018Article
- An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher's linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.
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