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A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease.

IEEE transactions on bio-medical engineering
The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effecti...

Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation.

IEEE transactions on bio-medical engineering
In this paper, we present a deep learning framework "Rehab-Net" for effectively classifying three upper limb movements of the human arm, involving extension, flexion, and rotation of the forearm, which, over the time, could provide a measure of rehab...

How the input shapes the acquisition of verb morphology: Elicited production and computational modelling in two highly inflected languages.

Cognitive psychology
The aim of the present work was to develop a computational model of how children acquire inflectional morphology for marking person and number; one of the central challenges in language development. First, in order to establish which putative learnin...

Noninvasive prediction of Blood Lactate through a machine learning-based approach.

Scientific reports
We hypothesized that blood lactate concentration([Lac]) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [L...

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome.

BMC medical informatics and decision making
BACKGROUND: Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important ...

Training improvements for ultrasound beamforming with deep neural networks.

Physics in medicine and biology
This paper investigates practical considerations of training ultrasound deep neural network (DNN) beamformers. First, we studied training DNNs using the combination of multiple point target responses instead of single point target responses. Next, we...

Interactive Compliance Control of a Wrist Rehabilitation Device (WRD) with Enhanced Training Safety.

Journal of healthcare engineering
Interaction control plays an important role in rehabilitation devices to ensure training safety and efficacy. Compliance adaptation of interaction is vital for enabling robot movements to better suit the patient's requirements as human joint characte...

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

Journal of healthcare engineering
This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous an...

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

NeuroImage. Clinical
Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophre...

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness.

Computers in biology and medicine
BACKGROUND: The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels.