Wireless Machine-to-Machine Healthcare Solution Using Android Mobile Devices in Global Networks Sensors Journal, IEEE, Issue Date: May 2013, Written by: Jung, S.-J.; Myllyla, R.; Chung, W.-Y. Report：Cheng-Ze Lyu

Outline Introduction System design Experimental results Conclusion M2M Devices 6LoWPAN Server and Android Mobile Device Experimental results Conclusion Personal remark

Introduction(1/2) This paper presents a healthcare solution that combines mobile and IPv6 techniques in a wireless sensor network. A low-power embedded wearable sensor measures the health parameters dynamically. A visualization module of the server program graphically displays the recorded biomedical signals on Android mobile devices.

Introduction(2/2) This is particularly important in developed countries with a significant aging population. It would increase the health observability, collaboration among doctors, and doctor-to- patient efficiency, and thereby decrease healthcare costs.

System Design(1/7) M2M(Machine to Machine) PPG(Photoplethysmography) M2M Devices Block diagram of the overall system architecture. PPG sensor M2M Node M2M Gateway Server PC Mobile PPG(Photoplethysmography)

System Design(2/7)

System Design(3/7)

System Design(4/7) 6LoWPAN The 6LoWPAN is a new attempt at extending an IP- based sensor network environment. The external hosts directly communicate with the M2M nodes. The server for the IPv6 communication. As the IP network can be generally accessed by IPv4 addresses.

System Design(5/7)

System Design(6/7) Server and Android Mobile Device The measured biomedical signals are sent to the server PC. The monitoring and analysis program, written in the C# programming language, monitors, stores, and processes the received data in the server PC.

System Design(7/7)

Experimental Results(1/5)

Experimental Results(2/5) All packets transmitted through the internet are verified in the server monitoring program Receiving all the transmitted packets via user datagram protocol (UDP) communication in the server

Experimental Results(3/5) The monitoring program stores its value in a database and plots all the measured biomedical signals dynamically. Such as a PPG waveform display and heart rate.

Experimental Results(4/5)

Experimental Results(5/5)

CONCLUSION(1/2) A wireless M2M healthcare solution using the Android mobile devices is successfully implemented in a global network. With the use of comfortable wearable sensors in global areas, the proposed M2M healthcare system promises to improve the flexibility and scalability of healthcare applications.

CONCLUSION(2/2) This field of research has the potential to significantly change and improve the efficiency of the healthcare system. With the evolution of network integration and the management of embedded devices operating multimodal tasks, a more precise and universal healthcare service scheme can be realized.

Personal remark The system of the security. More convenience.

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