ArticleCyborg and bionic systems (Washington, D.C.)2025
Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework.
Article in Cyborg and bionic systems (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assessment of Gait Kinematics in the Sagittal Plane in Children with Cerebral Palsy Following Therapy with the PRODROBOT Gait Trainer.Sensors (Basel, Switzerland) · 2026Article
- A Personalized Gait Parameter Prediction-Based Speed-Adaptive Control Method for Hybrid Active-Passive Intelligent Prosthetic Knee.Biomimetics (Basel, Switzerland) · 2026Article
- Application of Artificial Intelligence in the Prediction and Management of Stroke Rehabilitation.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- A multi-task segFormer framework for lesion segmentation and cerebral palsy classification based on multi-modal MRI in infant with periventricular white matter injury.Frontiers in neuroscience · 2026Article
- A Single-Source Dual-Drive Flexible Knee Assistive Exoskeleton for Elderly Daily Locomotion.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Gait phase recognition of children with cerebral palsy via deep learning based on IMU data from a soft ankle exoskeleton.Frontiers in bioengineering and biotechnology · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the "Twin Brother" model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals. The EMG signals of 6 muscles from 10 subjects during lateral walking were collected. Four gait phases were recognized, and the hip angles of both legs were continuously estimated. The sliding window length of 250 ms and the sliding increment of 3 ms were determined by the requirements of response time and recognition accuracy of the real-time system. We compared the performance of CNN-LSTM, CNN, LSTM, support vector machine, NN, K-nearest neighbor, and the "Twin Brother" models. The "Twin Brother" model achieved a recognition accuracy (mean ± SD) of 98.81% ± 0.14%. The model's predicted root mean square error (RMSE) for the left and right hip angles are 0.9183° ± 0.024° and 1.0511° ± 0.027°, respectively, where the
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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.