AIMC Topic: Aged

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Realizing the Potential of Robotics for Aged Care Through Co-Creation.

Journal of Alzheimer's disease : JAD
Socially assistive robots have the potential to improve aged care by providing assistance through social interaction. While some evidence suggests a positive impact of social robots on measures of well-being, the adoption of robotic technology remain...

Addressing the Ethics of Telepresence Applications Through End-User Engagement.

Journal of Alzheimer's disease : JAD
Portacolone et al.'s Ethics Review highlights the ethical challenges associated with the implementation of telepresence devices and applications in the context of aging and dementia. In this response, we review ethical considerations as they relate t...

Effects of innovative hip-knee-ankle interlimb coordinated robot training on ambulation, cardiopulmonary function, depression, and fall confidence in acute hemiplegia.

NeuroRehabilitation
BACKGROUND: While Walkbot-assisted locomotor training (WLT) provided ample evidence on balance and gait improvements, the therapeutic effects on cardiopulmonary and psychological elements as well as fall confidence are unknown in stroke survivors.

Robot-assisted gait training promotes brain reorganization after stroke: A randomized controlled pilot study.

NeuroRehabilitation
BACKGROUND: Robot-assisted gait training (RAGT) can improve walking ability after stroke but the underlying mechanisms are unknown.

Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

Journal of Alzheimer's disease : JAD
BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls ...

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.

Journal of Alzheimer's disease : JAD
BACKGROUND: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable a...

Diagnostic Performance of the Support Vector Machine Model for Breast Cancer on Ring-Shaped Dedicated Breast Positron Emission Tomography Images.

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to evaluate the diagnostic ability of support vector machine (SVM) for early breast cancer (BC) using dedicated breast positron emission tomography (dbPET).

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach.

Journal of Alzheimer's disease : JAD
BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking...

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data.

Journal of Alzheimer's disease : JAD
BACKGROUND: Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used ...

Ethical Issues Raised by the Introduction of Artificial Companions to Older Adults with Cognitive Impairment: A Call for Interdisciplinary Collaborations.

Journal of Alzheimer's disease : JAD
Due to the high costs of providing long-term care to older adults with cognitive impairment, artificial companions are increasingly considered as a cost-efficient way to provide support. Artificial companions can comfort, entertain, and inform, and e...