Geriatrics

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from , a publicly available dataset containing records of Activities of Daily Living and ...

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as t...

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box.

Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in ...

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated geneti...

Screening of Alzheimer's disease by facial complexion using artificial intelligence.

Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia...

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia.

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve de...

Cognitive and MRI trajectories for prediction of Alzheimer's disease.

The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer's disease (AD), and identification and treatment befo...

Modeling autosomal dominant Alzheimer's disease with machine learning.

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

An End-to-End Foreground-Aware Network for Person Re-Identification.

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identificat...

Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated With ECG-Derived Physiological Aging.

Background An artificial intelligence algorithm that detects age using the 12-lead ECG has been suggested to signal "physiologic age." This study aime...

4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network.

Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervise...

DaNet: dose-aware network embedded with dose-level estimation for low-dose CT imaging.

Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly employ the end-to-end networks on low-dose training d...

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.

Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficul...

Transformer neural network for protein-specific de novo drug generation as a machine translation problem.

Drug discovery for a protein target is a very laborious, long and costly process. Machine learning approaches and, in particular, deep generative netw...

Deep Differentiable Random Forests for Age Estimation.

Age estimation from facial images is typically cast as a label distribution learning or regression problem, since aging is a gradual progress. Its mai...

Modular machine learning for Alzheimer's disease classification from retinal vasculature.

Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get ea...

Human locomotion with reinforcement learning using bioinspired reward reshaping strategies.

Recent learning strategies such as reinforcement learning (RL) have favored the transition from applied artificial intelligence to general artificial ...

The impact of errors in infant development: Falling like a baby.

What is the role of errors in infants' acquisition of basic skills such as walking, skills that require immense amounts of practice to become flexible...

At the intersection of machine learning, biology, and health: an interview with Lorin Crawford.

Lorin Crawford began his independent career at Brown University School of Public Health with his own lab in the summer of 2017. He is currently a Seni...

The Combined Effects of Sports Smart Bracelet and Multi-Component Exercise Program on Exercise Motivation among the Elderly in Macau.

Faced with the serious problem of an aging population, exercise is one of the most effective ways to maintain the health of the elderly. In recent ye...