AIMC Topic: Anthropometry
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Integrated linkage-driven dexterous anthropomorphic robotic hand.
Nature communications
Dec 14, 2021
Robotic hands perform several amazing functions similar to the human hands, thereby offering high flexibility in terms of the tasks performed. However, developing integrated hands without additional actuation parts while maintaining important functio...
A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia.
Computational and mathematical methods in medicine
Dec 7, 2021
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in ...
Artificial neural network model effectively estimates muscle and fat mass using simple demographic and anthropometric measures.
Clinical nutrition (Edinburgh, Scotland)
Nov 24, 2021
BACKGROUND & AIMS: Lean muscle and fat mass in the human body are important indicators of the risk of cardiovascular and metabolic diseases. Techniques such as dual-energy X-ray absorptiometry (DXA) accurately measure body composition, but they are c...
Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.
Nutrients
Oct 26, 2021
INTRODUCTION: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, RE...
A Neural Networks Approach to Determine Factors Associated With Self-Reported Discomfort in Picking Tasks.
Human factors
Oct 4, 2021
OBJECTIVE: A neural networks approach has been proposed to handle various inputs such as postural, anthropometric and environmental variables in order to estimate self-reported discomfort in picking tasks. An input reduction method has been proposed,...
Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.
Scientific reports
Aug 16, 2021
Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantification of MFI requires time-consuming and rater-dependent manual segmentation techniques. A convolutional neural network (CNN) model was trained to se...
Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach.
IEEE journal of biomedical and health informatics
Jul 27, 2021
Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consumi...
Identify dominant dimensions of 3D hand shapes using statistical shape model and deep neural network.
Applied ergonomics
May 25, 2021
Hand anthropometry is one of the fundamentals of ergonomic research and product design. Many studies have been conducted to analyze the hand dimensions among different populations, however, the definitions and the numbers of those dimensions were usu...
Bone Mineral Density and Content Among Patients With Coronary Artery Disease: A Comparative Study.
The American journal of the medical sciences
Nov 23, 2020
INTRODUCTION: Some studies indicate an association between coronary artery disease (CAD) and osteoporosis. This case-control study examined the association between body composition and bone mineral content (BMC) and density (BMD) among patients with ...
Importance of anthropometric features to predict physical performance in elite youth soccer: a machine learning approach.
Research in sports medicine (Print)
Aug 23, 2020
The present study aimed to determine the contribution of soccer players' anthropometric features to predict their physical performance. Sixteen players, from a professional youth soccer academy, were recruited. Several anthropometric features such as...
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