Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases
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 reprin...
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| Format: | Online |
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| Jezik: | angleščina |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Teme: | |
| Online dostop: | ONIX_20230511_9783036572253_32 |
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| _version_ | 1869526018256011264 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-100015 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1000152024-04-09T23:15:31Z Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases Olmo, Gabriella Demrozi, Florenc Borzì, Luigi 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues 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. 2023-05-11T17:16:46Z 2023-05-11T17:16:46Z 2023 book ONIX_20230511_9783036572253_32 9783036572253 9783036572246 https://directory.doabooks.org/handle/20.500.12854/100015 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7108 https://mdpi.com/books/pdfview/book/7108 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7224-6 10.3390/books978-3-0365-7224-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036572253 9783036572246 216 Basel open access |
| spellingShingle | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases |
| title | Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases |
| title_full | Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases |
| title_fullStr | Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases |
| title_full_unstemmed | Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases |
| title_short | Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases |
| title_sort | wearable sensors for supporting diagnosis prognosis and monitoring of neurodegenerative diseases |
| topic | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20230511_9783036572253_32 |