Custom biomedical sensors for application in wireless body area networks and medical device integration frameworks



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Kansas State University


The U.S. health care system is one of the most advanced and costly systems in the world. The health services supply/demand gap is being enlarged by the aging population coupled with shortages in the traditional health care workforce and new information technology workers. This will not change if the current medical system adheres to the traditional hospital-centered model. One promising solution is to incorporate patient-centered, point-of-care test systems that promote proactive and preventive care by utilizing technology advancements in sensors, devices, communication standards, engineering systems, and information infrastructures.

Biomedical devices optimized for home and mobile health care environments will drive this transition. This dissertation documents research and development focused on biomedical device design for this purpose (including a wearable wireless pulse oximeter, motion sensor, and two-thumb electrocardiograph) and, more importantly, their interactions with other medical components, their supporting information infrastructures, and processing tools that illustrate the effectiveness of their data. The GumPack concept and prototype introduced in Chapter 2 addresses these aspects, as it is a sensor-laden device, a host for a local body area network (BAN), a portal to external integration frameworks, and a data processing platform. GumPack sensor-component design (Chapters 3 and 4) is oriented toward surface applications (e.g., touch and measure), an everyday-carry form factor, and reconfigurability. Onboard tagging technology (Chapters 5 and 6) enhances sensor functionality by providing, e.g., a signal quality index and confidence coefficient for itself and/or next-tier medical components (e.g., a hub).

Sensor interaction and integration work includes applications based on the GumPack design (Chapters 7 through 9) and the Medical Device Coordination Framework (Chapters 10 through 12). A high-resolution, wireless BAN is presented in Chapter 8, followed by a new physiological use case for pulse wave velocity estimation in Chapter 9. The collaborative MDCF work is transitioned to a web-based Hospital Information Integration System (Chapter 11) by employing database, AJAX, and Java Servlet technology. Given the preceding sensor designs and the availability of information infrastructures like the MDCF, medical platform-oriented devices (Chapter 12) could be an innovative and efficient way to design medical devices for hospital and home health care applications.



Biomedical Sensor, Sensor Network

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Doctor of Philosophy


Department of Electrical & Computer Engineering

Major Professor

Steven Warren