ArticleSensors (Basel, Switzerland)2021
Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis-Proof of Concept.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 69 citations in OpenAlex.
- Deep Learning-Based Temporal Gait Analysis Using a Smartphone IMU in Older Adults with and Without Non-Specific Low Back Pain.Bioengineering (Basel, Switzerland) · 2026Article
- IMU-based gait analysis methods: a systematic review of techniques for different body locations.Frontiers in sports and active living · 2026Review
- Deep learning-based gait phase detection using shank-mounted IMU data: Classification approach.PloS one · 2026Article
- A Review of Gait Analysis Using Gyroscopes and Inertial Measurement Units.Sensors (Basel, Switzerland) · 2025Review
- EgoFall: Real-Time Privacy-Preserving Fall Risk Assessment With a Single On-Body Tracking Camera.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Wearable system for the measurement of gait cycle kinematic and kinetic signals.Wearable technologies · 2025Article
- IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Bi-Directional Long Short-Term Memory-Based Gait Phase Recognition Method Robust to Directional Variations in Subject's Gait Progression Using Wearable Inertial Sensor.Sensors (Basel, Switzerland) · 2024Article
- A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.Sensors (Basel, Switzerland) · 2023Article
- Walking-Speed-Adaptive Gait Phase Estimation for Wearable Robots.Sensors (Basel, Switzerland) · 2023Article
- Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2023Article
- Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network.Sensors (Basel, Switzerland) · 2023Review
- Gait Events Prediction Using Hybrid CNN-RNN-Based Deep Learning Models through a Single Waist-Worn Wearable Sensor.Sensors (Basel, Switzerland) · 2022Article
- Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.Sensors (Basel, Switzerland) · 2022Article
- Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics.Sensors (Basel, Switzerland) · 2022Article
- Human Gait Modeling, Prediction and Classification for Level Walking Using Harmonic Models Derived from a Single Thigh-Mounted IMU.Sensors (Basel, Switzerland) · 2022Article
- An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson's Disease.Sensors (Basel, Switzerland) · 2021Article
- Robust Human Gait Speed Recognition Under Non-Ideal Conditions for Suspension-Assisted Walking Systems.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. The aim of this work was to employ Artificial Intelligence (AI), specifically a long short-term memory (LSTM) algorithm, to overcome these weaknesses. Three supervised LSTM-based models were designed to estimate the expected gait phases, including foot-off (FO), mid-swing (MidS) and foot-contact (FC). For collecting gait data two tri-axial inertial sensors were located above each ankle. The angular velocity magnitude, rotation matrix magnitude and free acceleration magnitude were captured for data labeling and turning detection and to strengthen the model, respectively. To do so, a train dataset based on a novel movement protocol was acquired. A validation dataset similar to a train dataset was generated as well. Five test datasets from already existing data were also created to independently evaluate the models. After testing the models on validation and test datasets, all three models demonstrated promising performance in estimating desired gait phases. The proposed approach proves the possibility of employing AI-based algorithms to predict labeled gait phases from a time series of gait data.
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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.