Embedded systems are emerging as one of the most critical elements in health & wellness and healthcare technologies. As special-purpose computer systems contained in devices, they control and enable advanced intelligent functionality such as multisensor health-monitoring devices, intelligent biosensors, real-time data distillation, health and health care imaging systems, and electronic medical and health records. Embedded systems are critical across a broad range of health applications, from handheld systems and intelligent monitoring devices for the home and acute healthcare environments to information processing for healthcare providers. Embedded applications for health face tough limitations on size, weight, and power, while requiring maximum performance, fault tolerant operation, information security, and reliability. Currently, developed embedded healthcare computing systems are independent solutions, tailored to a specific, static set of healthcare objectives. Typically such implementations are one-of-a-kind devices unable to adapt to changing requirements.
In this project, we are pursuing technologies to overcome such limitations. This includes technologies for efficiently designing and simulating embedded systems for high-performance, flexible, fault-tolerant, embedded processing; for advanced information security; and for recovering and maintaining software. These technologies are essential to providing healthcare embedded systems that can be efficiently implemented, are flexible, and adapt to changing needs of individuals, patients, care givers, and health professionals.
Current Research Project
Health monitoring has gained significant traction over the last couple of years. The DEPEND group, with its expertise in designing reliable and secure computer systems, plans to develop a dependable framework for health monitoring. This research project spans several domains of computer systems design and incorporates cutting-edge technologies used in embedded system design, reconfigurable hardware systems, and general-purpose graphics processing units (GPGPUs).
The overarching goal of the project is to be able to carry out monitoring of vitals of a person in several different ways to facilitate an automated decision-making process regarding the health status of the individual. In particular, we are focusing on two scenarios:
- The person’s motion is unrestricted and is fitted with a device that is capable of carrying out the necessary monitoring and decision-making process.
- The person is stationary and his vitals are recorded and analyzed.
The two scenarios described here require two different types of devices with different processing abilities to achieve the same goal.
The first is suitable for a cyber-physical system or embedded system, whereas the second can take advantage of the stationarity of the individual and use a more powerful computer to perform the analysis.
Several different areas of health monitoring are being focused on as part of the project. The main ones are:
- Vitals/Biomedical Signals
Several different vitals can be monitored simultaneously in real time to achieve the goals mentioned earlier. The underlying question here is: which signals have to be monitored to achieve the end goal with minimal overhead?
The quality of the decision-making process lies in the ability of the underlying algorithm to utilize the information inherent in the signals to make coherent and reliable decisions. These algorithms also have to be tailored to suit the underlying processing elements.
Once the signals to be monitored have been identified, it is important to use appropriate sensors to collect the same. Cheaper commercial ones and custom-built sensors are being focused on.
- Processing Platform
This forms one of the major components of the system architecture. Several different platforms are being focused on using commercial-off-the-shelf components, reconfigurable hardware, and GPGPUs.
As part of the project, a first-generation prototype device was built using microcontrollers and sensors to collect brain activity signals, blood oxygen levels, heart rate, and motion information of the person. The device is capable of accurately detecting a subclass of abnormal brain activity called seizures. A new generation device will incorporate the same principles but use more flexible hardware, which allows for custom configuration and ease of final algorithmic implementation. Novel algorithms for detection of various kinds of abnormalities are being focused on.