Titlebook: Deep Learning in Smart eHealth Systems; Evaluation Leveragin Asma Channa,Nirvana Popescu Book 2024 The Editor(s) (if applicable) and The Au

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書目名稱Deep Learning in Smart eHealth Systems影響因子(影響力)學(xué)科排名




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宣誓書

重力

,Design and Engineering of a Medical Wearable Device for Parkinson’s Disease Management,of PD stages and disease progression, especially concerning tremor and bradykinesia. This chapter endeavors to craft a holistic ecosystem capable of capturing motion data associated with PD and securely transmitting it to the cloud for storage, data processing, and severity estimation, all facilitat

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,Deep Learning Models for Parkinson’s Disease Severity Evaluation,ignals are analyzed using the CatBoost classifier. This approach yields an impressive accuracy rate of 96%. The findings of this study substantially contribute to the development of a reliable and accurate framework for assessing PD severity.

導(dǎo)師

Conclusion and Prospects for Further Development,nd support offered to prevailing theories and models in the field of PD monitoring. It highlights the creation of an objective monitoring system for assessing PD symptoms, which employs meticulous preprocessing, feature extraction, and model evaluation techniques.

OCTO

Determinanten der Demokratiezufriedenheitof PD stages and disease progression, especially concerning tremor and bradykinesia. This chapter endeavors to craft a holistic ecosystem capable of capturing motion data associated with PD and securely transmitting it to the cloud for storage, data processing, and severity estimation, all facilitat

OCTO

Credence

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溫和女孩

2191-5768 work using Deep Learning and IoT wearable devices to assess .One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson‘s disease patients. This framework offers a ne