AIMC Topic: Anthropometry

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Relative Attribute SVM+ Learning for Age Estimation.

IEEE transactions on cybernetics
When estimating age, human experts can provide privileged information that encodes the facial attributes of aging, such as smoothness, face shape, face acne, wrinkles, and bags under-eyes. In automatic age estimation, privileged information is unavai...

On Configuration Trajectory Formation in Spatiotemporal Profile for Reproducing Human Hand Reaching Movement.

IEEE transactions on cybernetics
Most functional reaching activities in daily living generally require a hand to reach the functional position in appropriate orientation with invariant spatiotemporal profile. Effectively reproducing such spatiotemporal feature of hand configuration ...

Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning.

IEEE journal of biomedical and health informatics
The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, to date, no study has assessed the predictive power of phenotypes based on individual anthropometric measurements and triglyceride (TG) levels. The ai...

Design of a Robotic System to Measure Propulsion Work of Over-Ground Wheelchair Maneuvers.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
A wheelchair-propelling robot has been developed to measure the efficiency of manual wheelchairs. The use of a robot has certain advantages compared to the use of human operators with respect to repeatability of measurements and the ability to compar...

Identification of the Best Anthropometric Predictors of Serum High- and Low-Density Lipoproteins Using Machine Learning.

IEEE journal of biomedical and health informatics
Serum high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol levels are associated with risk factors for various diseases and are related to anthropometric measures. However, controversy remains regarding the best anthropometric...

Machine learning assisted noncontact neonatal anthropometry using FMCW radar.

Scientific reports
This study proposes a method for measuring the height and weight of a neonate conveniently, safely, and accurately by applying a convolutional neural network to frequency-modulated continuous-wave (FMCW) radar sensor data. Fifteen neonates, with pare...

Predicting Body Fat Percentage from Simple Anthropometric Measurements: A Machine Learning Approach.

Studies in health technology and informatics
Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered by gold-standard methods that are costly and invasive. This study employs a dataset containing variables such as age, sex, Body Mass Index (BMI), and...

Correspondence between three-dimensional ear depth information derived from two-dimensional images and magnetic resonance imaging: Use of a neural-network model.

JASA express letters
There is much interest in anthropometric-derived head-related transfer functions (HRTFs) for simulating audio for virtual-reality systems. Three-dimensional (3D) anthropometric measures can be measured directly from individuals, or indirectly simulat...

Prediction of Adult Height by Machine Learning Technique.

The Journal of clinical endocrinology and metabolism
CONTEXT: Prediction of AH is frequently undertaken in the clinical setting. The commonly used methods are based on the assessment of skeletal maturation. Predictive algorithms generated by machine learning, which can already automatically drive cars ...

Deep learning for biological age estimation.

Briefings in bioinformatics
Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data minin...