Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.

Background: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods.

A computer approach to assess age-related changes of the brain white matter in Alzheimer's disease.

Background: Age is one of the major risk factors for Alzheimer's disease (AD) which is considered the most common adult-onset dementia. There is little information about age-related changes during brain dementia.

Methods: This study observed age-related variations in the brain throughout adulthood in magnetic resonance imaging (MRI) of the AD and healthy brains.

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

Background: The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, and caregivers. Socially assistive robots (SARs) offer promising supportive care to assist in the complex management associated with AD.

Objectives: To conduct a scoping review of published articles that proposed, discussed, developed or tested SAR for interacting with AD patients.

An Optimized Decision Tree with Genetic Algorithm Rule-Based Approach to Reveal the Brain's Changes During Alzheimer's Disease Dementia.

Background: It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia.

Objective: This study introduces a new CADS for deep learning of magnetic resonance image (MRI) data to identify changes in the brain during Alzheimer's disease (AD) dementia.

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

Objective: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both.

Methods: For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10-20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database.