ArticleBiosensors2020
Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units.
Article in Biosensors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Deep Convolutional Neural Network-Based Hemiplegic Gait Detection Using an Inertial Sensor Located Freely in a Pocket.Sensors (Basel, Switzerland) · 2022Trial
- Foot Progression Angle Modulates Three-Dimensional Lower-Limb Biomechanics in Flexible Flatfoot: Kinematic-Kinetic Patterns and Clinical Implications.Journal of foot and ankle research · 2026Article
- Deep learning-based gait phase detection using shank-mounted IMU data: Classification approach.PloS one · 2026Article
- Performance Insights in Speed Climbing: Quantitative and Qualitative Analysis of Key Movement Metrics.Bioengineering (Basel, Switzerland) · 2025Article
- A Gait Sub-Phase Switching-Based Active Training Control Strategy and Its Application in a Novel Rehabilitation Robot.Biosensors · 2025Article
- A Review of Gait Analysis Using Gyroscopes and Inertial Measurement Units.Sensors (Basel, Switzerland) · 2025Review
- Long term gait postural characteristics of children with general foot pain using smartphone connected wearable sensors.Scientific reports · 2025Article
- The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports.PloS one · 2025Article
- Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques.Sensors (Basel, Switzerland) · 2024Review
- Wearable Online Freezing of Gait Detection and Cueing System.Bioengineering (Basel, Switzerland) · 2024Article
- iP3T: an interpretable multimodal time-series model for enhanced gait phase prediction in wearable exoskeletons.Frontiers in neuroscience · 2024Article
- AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets.Sensors (Basel, Switzerland) · 2023Article
- A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors.Sensors (Basel, Switzerland) · 2023Article
- Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities.Heliyon · 2023Review
- Human Gait Activity Recognition Machine Learning Methods.Sensors (Basel, Switzerland) · 2023Article
- IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- [Research on gait recognition and prediction based on optimized machine learning algorithm].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force.PPAR research · 2022Article
- Design of a Bio-Inspired Gait Phase Decoder Based on Temporal Convolution Network Architecture With Contralateral Surface Electromyography Toward Hip Prosthesis Control.Frontiers in neurorobotics · 2022Article
- Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion.Applied bionics and biomechanics · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user's current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an 'image' since it exhibits some local 'spatial' pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance.
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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.