ArticleSensors (Basel, Switzerland)2022
IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
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Who cites it
12 citing papers in PubMed.
- Edge AI-Based Gait-Phase Detection for Closed-Loop Neuromodulation in SCI Mice.Sensors (Basel, Switzerland) · 2026Article
- Quantifying women's water polo overhead movement volumes using inertial measurement units and machine learning techniques: a cross-sectional study.Scientific reports · 2026Article
- Fully textile passive wireless sensing for human movement monitoring with multiple sensors.Frontiers in bioengineering and biotechnology · 2026Article
- Active Rehabilitation Technologies for Post-Stroke Patients.Biosensors · 2025Review
- Activity Detection of Paralympic Athletes with Lower Limb Running-Specific Prosthesis During Extended Periods of Time: Software Development and Preliminary Validation.Sensors (Basel, Switzerland) · 2025Article
- Vibrotactile Haptic and Gesture Feedback in a Smartwatch for Controlling a Multi-Activity Powered Knee-Ankle Prosthesis.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Intelligent prosthetic hips and knees: from actuation to perception and control.Frontiers in neuroscience · 2025Review
- 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
- Continuous Gait Phase Estimation for Multi-Locomotion Tasks Using Ground Reaction Force Data.Sensors (Basel, Switzerland) · 2024Article
- Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.IEEE robotics and automation letters · 2024Article
- A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living.Sensors (Basel, Switzerland) · 2023Article
- 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
4 authors.
Funding
Abstract
This paper focuses on the classification of seven locomotion modes (sitting, standing, level ground walking, ramp ascent and descent, stair ascent and descent), the transitions among these modes, and the gait phases within each mode, by only using data in the frequency domain from one or two inertial measurement units. Different deep neural network configurations are investigated and compared by combining convolutional and recurrent layers. The results show that a system composed of a convolutional neural network followed by a long short-term memory network is able to classify with a mean F1-score of 0.89 and 0.91 for ten healthy subjects, and of 0.92 and 0.95 for one osseointegrated transfemoral amputee subject (excluding the gait phases because they are not labeled in the data-set), using one and two inertial measurement units, respectively, with a 5-fold cross-validation. The promising results obtained in this study pave the way for using deep learning for the control of transfemoral prostheses with a minimum number of inertial measurement units.
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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.