AIMC Topic: Gait Disorders, Neurologic

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Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.

Neurobiology of disease
BACKGROUND: Despite prior studies on early-stage Parkinson's disease (PD) brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes remain poorly understood. Our ...

Resting-state functional MRI metrics to detect freezing of gait in Parkinson's disease: a machine learning approach.

Computers in biology and medicine
Among the symptoms that can occur in Parkinson's disease (PD), Freezing of Gait (FOG) is a disabling phenomenon affecting a large proportion of patients, and it remains not fully understood. Accurate classification of FOG in PD is crucial for tailori...

A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices.

Computers in biology and medicine
Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease that severely impacts patients' mobility and quality of life. To minimize the risk of falls and injuries associated with FoG, it is crucial to develop highly accurate FoG detecti...

Enhanced neuroplasticity and gait recovery in stroke patients: a comparative analysis of active and passive robotic training modes.

BMC neurology
BACKGROUND: Stroke is a leading cause of long-term disability, with lower limb dysfunction being a common sequela that significantly impacts patients' mobility and quality of life. Robotic-assisted training has emerged as a promising intervention for...

Deep Learning-Based Prediction of Freezing of Gait in Parkinson's Disease With the Ensemble Channel Selection Approach.

Brain and behavior
PURPOSE: A debilitating and poorly understood symptom of Parkinson's disease (PD) is freezing of gait (FoG), which increases the risk of falling. Clinical evaluations of FoG, relying on patients' subjective reports and manual examinations by speciali...

Enhanced Binary Classification of Gait Disorders Using a Machine Learning Majority Voting Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study introduces a machine learning-based methodology for classifying healthy individuals and those with gait disorders, employing a merged data set from 'GaitRec' and 'Gutenberg.' Key gait features were extracted from the normalized ground reac...

Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury.

Medicine
With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, there has been no attempt to predict walking ability after spinal cord injury (SCI) based on ML. In ...

Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

NeuroRehabilitation
BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at...