A New Dual Channel Pulse Wave Velocity Measurement System
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
A Wireless Physiological Multi-parameter Monitoring System Based on Mobile Communication Networks
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Health Maintenance Embedded Systems in Home Care Applications
ICONS '07 Proceedings of the Second International Conference on Systems
A wireless PDA-based physiological monitoring system for patient transport
IEEE Transactions on Information Technology in Biomedicine
A wearable point-of-care system for home use that incorporates plug-and-play and wireless standards
IEEE Transactions on Information Technology in Biomedicine
Journal of Medical Systems
Accurate cirrhosis identification with wrist-pulse data for mobile healthcare
Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare
An efficient patient monitoring system
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Many countries have already become aging societies, as evidenced by annually decreasing fertility rates. Elderly individuals often live independently because their families cannot look after them. Therefore, computer-assisted nursing has received increasing attention in modern society, explaining why intelligent systems with physiology signal monitoring for e-health care is an emerging area of development, owing to the urgent needs of homecare for elderly people suffering chronic or sudden diseases at home. Importantly, a physiology signal monitoring system can help medical staff to monitor and analyze physiology signal effectively, such that they can not only monitor the patients' physiology states immediately, but also reduce medical cost and avoid having to visit doctors in hospital. Therefore, this study adopts system on chip (SOC) techniques to develop an embedded human pulse monitoring system with intelligent data analysis mechanism for disease detection and long-term health care. The proposed system can be applied to monitor and analyze pulse signal in daily life. The proposed system also has a friendly web-based interface for medical staff to observe immediate pulse signals for remote treatment. Hence, the proposed system provides aids long-distance medical treatment, exploring trends of potential chronic diseases, and urgent situations informing for sudden diseases. Moreover, this study also presents an intelligent data analysis scheme based on the modified cosine similarity measure to diagnose abnormal pulses for exploring potential chronic diseases.