Latest AI and machine learning research in exercise & fitness for healthcare professionals.
Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were the minimum and maximum values of heart contraction force, the time of ventricular filling, the volume used for dilution, heart rate and weight, volume,...
Although body mass index (BMI) and body fat percentage (B F %) are well known as indicators of nutritional status, there are insuficient data whether the relationship between them is linear or not. There are appropriate linear and quadratic formulas that are available to predict B F % from age, gender and BMI. On the other hand, our previous research has shown that artificial neural network (ANN) ...
BACKGROUND: Wearable monitors (WMs) are used to estimate the time spent in sedentary behaviors (SBs) and light-intensity physical activities (LPAs) an...
PURPOSE: The integration of sufficient cardiovascular stress into robot-assisted gait (RAG) training could combine the benefits of both RAG and aerobi...
Maximal oxygen uptake (VOmax) is an essential part of health and physical fitness, and refers to the highest rate of oxygen consumption an individual ...
It is important to verify the old findings of Cumming (1972) and Goldberg and Shephard (1980) who showed that stroke volume (SV) may be higher during ...
This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (...
BACKGROUND: Several robotic devices have been proposed for upper limb rehabilitation, but they differ in terms of application fields and the technical...
An accurate classification of neuromuscular disorders is important in providing proper treatment facilities to the patients. Recently, the microarray ...
BACKGROUND: Exercise testing devices for evaluating cardiopulmonary fitness in patients with severe disability after stroke are lacking, but we have a...
BACKGROUND: Over 50% of patients with symptomatic heart failure (HF) experience HF with preserved ejection fraction (HFpEF) Exercise training (ET) is ...
This paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressu...
Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor o...
BACKGROUND: Atherosclerosis is a chronic inflammatory disease which starts early in life and depends on many factors, an important one being dyslipopr...
To investigate whether the learning curve of robotic surgery simulator training depends on the probands' characteristics, such as age and prior experi...
We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ej...
Menopause-related withdrawal of ovarian estrogens is associated with reduced energy metabolism and overall impairment of substrate oxidation. Estradio...
BACKGROUND: The motivation for the BioHub project is to create an Integrated Knowledge Management System (IKMS) that will enable chemists to source in...
Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new...
Ecological limits to phenotypic plasticity (PP), induced by simultaneous biotic and abiotic factors, can prevent organisms from exhibiting optimal pla...