AIMC Topic: Lower Extremity
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Rehabilitation Technologies by Integrating Exoskeletons, Aquatic Therapy, and Quantum Computing for Enhanced Patient Outcomes.
Sensors (Basel, Switzerland)
Dec 4, 2024
Recent advancements in patient rehabilitation integrate both traditional and modern techniques to enhance treatment efficacy and accessibility. Hydrotherapy, leveraging water's physical properties, is crucial for reducing joint stress, alleviating pa...
MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.
Sensors (Basel, Switzerland)
Nov 28, 2024
Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (L...
A Linear Rehabilitative Motion Planning Method with a Multi-Posture Lower-Limb Rehabilitation Robot.
Sensors (Basel, Switzerland)
Nov 25, 2024
In rehabilitation, physicians plan lower-limb exercises via linear guidance. Ensuring efficacy and safety, they design patient-specific paths, carefully plotting smooth trajectories to minimize jerks. Replicating their precision in robotics is a majo...
Task-agnostic exoskeleton control via biological joint moment estimation.
Nature
Nov 13, 2024
Lower-limb exoskeletons have the potential to transform the way we move, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We i...
Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Nov 6, 2024
Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient...
Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model.
Sensors (Basel, Switzerland)
Nov 4, 2024
To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM...
Artificial intelligence-based analysis of lower limb muscle mass and fatty degeneration in patients with knee osteoarthritis and its correlation with Knee Society Score.
International journal of computer assisted radiology and surgery
Nov 3, 2024
PURPOSE: Lower-limb muscle mass reduction and fatty degeneration develop in patients with knee osteoarthritis (KOA) and could affect their symptoms, satisfaction, expectation and functional activities. The Knee Society Scoring System (KSS) includes p...
A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.
PloS one
Oct 4, 2024
This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches based on Magnetic Resonance Imaging (MRI) scans, crucial for the diagnostic and therapeutic processes in musculoskeletal diseases. In general, segmen...
Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 27, 2024
Decoding continuous human motion from surface electromyography (sEMG) in advance is crucial for improving the intelligence of exoskeleton robots. However, incomplete sEMG signals are prevalent on account of unstable data transmission, sensor malfunct...
Effect and optimal exercise prescription of robot-assisted gait training on lower extremity motor function in stroke patients: a network meta-analysis.
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Sep 23, 2024
OBJECTIVE: This study aimed to evaluate the effectiveness of robot-assisted gait training (RAGT) and explore the optimal exercise prescription using a network meta-analysis approach.
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