Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases

doi:10.3390/books978-3-0365-7224-6

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https://mdpi.com/books/pdfview/book/7108

Contributor(s)

Olmo, Gabriella (editor)

Demrozi, Florenc (editor)

Borzì, Luigi (editor)

Language

English

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Abstract

Neurodegenerative disorders (NDs) are becoming more prevalent in our aging population, and traditional methods of monitoring ND symptoms can be challenging. Wearable technology offers several advantages, such as continuous monitoring, objective measurements, and remote monitoring. The present reprint includes a collection of eleven research and review articles that propose wearable solutions and explore signal processing, machine learning, and deep learning approaches for the computerized diagnosis and monitoring of NDs. Topics covered include using wearable technology to measure blood pressure, movement, sleep patterns, and brain activity, and developing predictive models to support clinicians in making informed decisions about treatment and care. This reprint is a valuable resource for anyone interested in the potential of wearable technology to improve the diagnosis and management of NDs.

URI

https://directory.doabooks.org/handle/20.500.12854/100015

Keywords

gait analysis; Parkinson’s disease; convolutional neural networks; gate recurrent units; deep learning; cardiovascular diseases; blood pressure; hypertension; photoplethysmography; artificial neural networks; neurodegenerative disease; remote monitoring; telemonitoring; wearable sensor; Parkinson’s Disease; image processing; hypomimia; FE; classic techniques; machine learning; ANN; KNN; machine learning (ML); naïve Bayes classification; SVM; bradykinesia; wearables; inertial sensors; artificial intelligence; Internet of Things; trust management; healthcare; digital revolution; edge clouds; security; privacy preservation; neurological disorders; wearable sensors; frequency harmonics; gait impairments; gait; harmonic ratio (HR); smoothness; symmetry; older adults; inertial sensor; biofeedback; neurodegenerative diseases; movement anticipation; rs-fMRI; classifications; high-order neuro-dynamic functional network; Alzheimer’s disease; sleep monitoring; sleep disorders; Parkinson disease; dementia; Alzheimer Disease; video analysis; n/a