標題: Titlebook: Deep Learning in Smart eHealth Systems; Evaluation Leveragin Asma Channa,Nirvana Popescu Book 2024 The Editor(s) (if applicable) and The Au [打印本頁] 作者: interleukins 時間: 2025-3-21 18:28
書目名稱Deep Learning in Smart eHealth Systems影響因子(影響力)
書目名稱Deep Learning in Smart eHealth Systems影響因子(影響力)學科排名
書目名稱Deep Learning in Smart eHealth Systems網(wǎng)絡公開度
書目名稱Deep Learning in Smart eHealth Systems網(wǎng)絡公開度學科排名
書目名稱Deep Learning in Smart eHealth Systems被引頻次
書目名稱Deep Learning in Smart eHealth Systems被引頻次學科排名
書目名稱Deep Learning in Smart eHealth Systems年度引用
書目名稱Deep Learning in Smart eHealth Systems年度引用學科排名
書目名稱Deep Learning in Smart eHealth Systems讀者反饋
書目名稱Deep Learning in Smart eHealth Systems讀者反饋學科排名
作者: 宣誓書 時間: 2025-3-21 22:24 作者: 重力 時間: 2025-3-22 02:04
,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作者: 無思維能力 時間: 2025-3-22 04:54
,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.作者: 導師 時間: 2025-3-22 12:28
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.作者: OCTO 時間: 2025-3-22 16:54
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作者: OCTO 時間: 2025-3-22 18:11 作者: Credence 時間: 2025-3-22 21:39 作者: brachial-plexus 時間: 2025-3-23 04:32 作者: 溫和女孩 時間: 2025-3-23 05:35
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作者: ALTER 時間: 2025-3-23 11:04
Integrity and normality. The singular locus,personalized management strategies. Additionally, the chapter underscores the significance of distinguishing PD from conditions with similar symptomatology to prevent misdiagnosis, ultimately leading to improved patient outcomes and an elevated quality of life.作者: 表臉 時間: 2025-3-23 17:30 作者: 觀察 時間: 2025-3-23 18:34
https://doi.org/10.1007/978-3-531-19729-6racy. This methodology presents a promising avenue for automated, sensor-based detection of medication wearing off in PD patients, offering valuable insights into timely intervention and enhanced management strategies.作者: capsule 時間: 2025-3-24 01:34
https://doi.org/10.1007/978-3-322-92410-0 control. Another aspect introduced in this chapter outlines an innovative approach to gait assessment, employing the continuous wavelet transform method. This approach addresses the analysis of idiopathic PD severity levels as well as gait variability in age-matched individuals.作者: PAD416 時間: 2025-3-24 04:47
,Unraveling Parkinson’s Disease: Diagnostic Challenges and Severity Assessment,personalized management strategies. Additionally, the chapter underscores the significance of distinguishing PD from conditions with similar symptomatology to prevent misdiagnosis, ultimately leading to improved patient outcomes and an elevated quality of life.作者: 跳動 時間: 2025-3-24 06:38
,State-of-the-Art: Wearable Devices and Deep Learning Techniques for Parkinson’s Disease,an eHealth platform tailored for PD patients. The developed eHealth platform exemplifies the potential for advanced technologies to revolutionize the field and enhance the quality of life for individuals grappling with this debilitating neurological disorder.作者: 飛來飛去真休 時間: 2025-3-24 13:48
,Predicting Wearing-Off Episodes in Parkinson’s with Multimodal Machine Learning,racy. This methodology presents a promising avenue for automated, sensor-based detection of medication wearing off in PD patients, offering valuable insights into timely intervention and enhanced management strategies.作者: 歸功于 時間: 2025-3-24 15:59
Enhancing Gait Analysis Through Wearable Insoles and Deep Learning Techniques, control. Another aspect introduced in this chapter outlines an innovative approach to gait assessment, employing the continuous wavelet transform method. This approach addresses the analysis of idiopathic PD severity levels as well as gait variability in age-matched individuals.作者: AVOW 時間: 2025-3-24 21:16 作者: Spartan 時間: 2025-3-25 02:24 作者: terazosin 時間: 2025-3-25 03:29
Integrity and normality. The singular locus,s. This chapter delves into the multifaceted diagnostic challenges associated with PD, including the heterogeneous progression of the disease, patient-reported biases, and the influence of medication. To evaluate disease severity, various rating scales like UPDRS and the Hoehn and Yahr scale are emp作者: 歌唱隊 時間: 2025-3-25 10:17 作者: 拋媚眼 時間: 2025-3-25 14:02
Determinanten der DemokratiezufriedenheitParkinson’s Disease (PD). These devices predominantly rely on inertial sensors and computational algorithms, offering promising advancements. However, they also introduce fresh challenges, including concerns related to security, privacy, connectivity, and power efficiency. From a clinical perspectiv作者: 向前變橢圓 時間: 2025-3-25 19:30
Einleitung: Abgrenzung und Systematikd assessment of Parkinson’s Disease (PD) severity through remote and continuous monitoring. The chapter is divided into two sections, outlining the methodology and results of the study. In the first section, an A-WEAR bracelet is employed for the objective assessment of tremor and bradykinesia in PD作者: CAB 時間: 2025-3-25 21:32 作者: 壁畫 時間: 2025-3-26 01:16
https://doi.org/10.1007/978-3-531-19729-6, like levodopa, wear off, resulting in the recurrence of PD symptoms. The methodology harnesses features derived from heart rate, stress levels, sleep patterns, and step counts, combined with machine learning techniques, to forecast wearing-off in patients. Relevant features encompass mean conditio作者: CHASE 時間: 2025-3-26 05:47 作者: 裂口 時間: 2025-3-26 10:31
https://doi.org/10.1007/978-3-642-53065-4formative therapeutic advancements that have converted PD from a life-threatening condition into a manageable disease. Nevertheless, the intricate and progressive nature of PD continues to pose challenges for its effective management and the enhancement of patients’ quality of life. The chapter acce作者: 過份 時間: 2025-3-26 15:41
https://doi.org/10.1007/978-3-031-45003-7A-WEAR Bracelet; Cloud Platform; Continuous Wavelet Transform; Convolutional Neural Network; Deep Learni作者: semble 時間: 2025-3-26 17:53 作者: preservative 時間: 2025-3-27 00:28
Asma Channa,Nirvana PopescuCombines state-of-the-art technology with clinical expertise to develop a personalized and efficient evaluation.Presents a new eHealth framework using Deep Learning and IoT wearable devices to assess 作者: 半球 時間: 2025-3-27 03:12
SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/d/image/264628.jpg作者: Monotonous 時間: 2025-3-27 08:27 作者: Fibrinogen 時間: 2025-3-27 09:56
,Unraveling Parkinson’s Disease: Diagnostic Challenges and Severity Assessment,s. This chapter delves into the multifaceted diagnostic challenges associated with PD, including the heterogeneous progression of the disease, patient-reported biases, and the influence of medication. To evaluate disease severity, various rating scales like UPDRS and the Hoehn and Yahr scale are emp作者: 禁令 時間: 2025-3-27 14:46 作者: 膽小懦夫 時間: 2025-3-27 18:14
,Design and Engineering of a Medical Wearable Device for Parkinson’s Disease Management,Parkinson’s Disease (PD). These devices predominantly rely on inertial sensors and computational algorithms, offering promising advancements. However, they also introduce fresh challenges, including concerns related to security, privacy, connectivity, and power efficiency. From a clinical perspectiv作者: 最小 時間: 2025-3-27 22:05 作者: Lasting 時間: 2025-3-28 05:16 作者: callous 時間: 2025-3-28 10:19
