Methods And Applications In Aging Neuroscience

Methods and applications in aging neuroscience

When I'm 64

By 2030 there will be about 70 million people in the United States who are older than 64. Approximately 26 percent of these will be racial and ethnic minorities. Overall, the older population will be more diverse and better educated than their earlier cohorts. The range of late-life outcomes is very dramatic with old age being a significantly different experience for financially secure and well-educated people than for poor and uneducated people. The early mission of behavioral science research focused on identifying problems of older adults, such as isolation, caregiving, and dementia. Today, the field of gerontology is more interdisciplinary. When I'm 64 examines how individual and social behavior play a role in understanding diverse outcomes in old age. It also explores the implications of an aging workforce on the economy. The book recommends that the National Institute on Aging focus its research support in social, personality, and life-span psychology in four areas: motivation and behavioral change; socioemotional influences on decision-making; the influence of social engagement on cognition; and the effects of stereotypes on self and others. When I'm 64 is a useful resource for policymakers, researchers and medical professionals.

Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications

Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. This book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries. Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control. - Incorporates artificial intelligence techniques into neuro-rehabilitation and presents novel ideas for this process - Provides in-depth case studies and state-of-the-art methods, along with the experimental study - Presents a block diagram based complete set-up in each chapter to help in real-time implementation