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<p>Collaborating with intelligent health monitoring in the clinical care of elderly patients can enhance understanding of changes in their body functions, thereby improving their quality of life and preventing complications. This paper focuses on the user characteristics and needs of geriatric patients, builds an intelligent health monitoring system based on C/S architecture, and develops a health data collection process for geriatric patients using smart wearables. We use the VMD algorithm to reduce the noise in the physiological signal data of elderly patients, then input it into a time-sequence convolutional network to extract the corresponding ECG signal features. We then combine this data with the LSTM model to classify the ECG signals, enabling us to diagnose the health of elderly patients. We established a continuity of care program based on the intelligent health monitoring system and designed a comparison experiment to evaluate the impact of the application. The VMD algorithm can recognize the insignificant signal peaks between 1.5s and 5.5s, and the health diagnosis model has the highest classification accuracy of 99.24% for ECG beats. In the continuity of care model, the elderly patients’ physiological function score was 68.42±4.76, and their serum ALB level was 35.79±6.72 g/L, which was 17.42% higher than the control group’s level after the intervention. It also helped the elderly patients’ mental health. The intelligent health monitoring system can dynamically acquire the physiological characteristics of elderly patients in real time and generate visualization results to help medical staff customize personalized care plans.</p>
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Collaborating with intelligent health monitoring in the clinical care of elderly patients can enhance understanding of changes in their body functions, thereby improving their quality of life and preventing complications. This paper focuses on the user characteristics and needs of geriatric patients, builds an intelligent health monitoring system based on C/S architecture, and develops a health data collection process for geriatric patients using smart wearables. We use the VMD algorithm to reduce the noise in the physiological signal data of elderly patients, then input it into a time-sequence convolutional network to extract the corresponding ECG signal features. We then combine this data with the LSTM model to classify the ECG signals, enabling us to diagnose the health of elderly patients. We established a continuity of care program based on the intelligent health monitoring system and designed a comparison experiment to evaluate the impact of the application. The VMD algorithm can recognize the insignificant signal peaks between 1.5s and 5.5s, and the health diagnosis model has the highest classification accuracy of 99.24% for ECG beats. In the continuity of care model, the elderly patients’ physiological function score was 68.42±4.76, and their serum ALB level was 35.79±6.72 g/L, which was 17.42% higher than the control group’s level after the intervention. It also helped the elderly patients’ mental health. The intelligent health monitoring system can dynamically acquire the physiological characteristics of elderly patients in real time and generate visualization results to help medical staff customize personalized care plans.

Evaluation of the Benefits of an Intelligent Health Monitoring System in the Care of Geriatric Patients

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Collaborating with intelligent health monitoring in the clinical care of elderly patients can enhance understanding of changes in their body functions, thereby...

Evaluation of the Benefits of an Intelligent Health Monitoring System in the Care of Geriatric Patients

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 04. Apr. 2024

Akzeptiert: 02. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2218

Schlüsselwörter
C/S architecture, Intelligent health monitoring system, VMD algorithm, Temporal convolution, LSTM model, Geriatric patient care

© 2024 Yue Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Evaluation of the Benefits of an Intelligent Health Monitoring System in the Care of Geriatric Patients

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 04. Apr. 2024

Akzeptiert: 02. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2218

SchlüsselwörterC/S architecture, Intelligent health monitoring system, VMD algorithm, Temporal convolution, LSTM model, Geriatric patient care

© 2024 Yue Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
C/S architecture, Intelligent health monitoring system, VMD algorithm, Temporal convolution, LSTM model, Geriatric patient care