A new sensor device capable of measuring blood-flow volume (BFV) and blood pressure (BP) is successfully designed and prototyped. With this device, the measurement on BFV and BP is non-invasively, and cam be continuously collected over more than 24 hours, resulting in valuable long-time monitoring data for effective medical diagnosis. The long-time BVF measurements are particularly suited to assess quality of arteriovenous fistula in hemodialysis patients. Of particular interests is that BFV is nowadays in clinic practices evaluated using an ultrasound Doppler monitor, which is expensive, bulky, and can only be operated by well-trained medical personnels. The sensor device developed by this study is a low-cost, small-sized, portable, and easy-to-use PPG sensor that is capable of continuous measurement of BFV and BP. New designs of front-end analog circuits, signal processing, and an intelligent neural network calibration method are employed to finally achieve high correlations of R2 = 0.88 for BFV and R2 = 0.85 for BP, as opposed to their gold standard counterpart monitors.
In this talk, computer vision is proposed as a way to facilitate the interpretation of phenomena in medical imaging, and to make measurements or inferences based on models of such phenomena. Actually, this is an ill-posed problem that humans can learn to solve effortlessly, but computer algorithms often are prone to errors. Nevertheless, in some cases computers can surpass humans and help interpret medical imagery more accurately, as we will discuss in this talk.
Medical imaging measurements often are indirect and involve errors. For example, estimating tumor growth (or shrinkage) in response to treatment requires measuring the tumor size, modeling the tumor shape, and making accurate predictions to evaluate the treatment effectiveness, which can be challenging in practice. These issues are closely related to machine learning and pattern recognition, and in this talk we discuss some cases that illustrate how techniques of these areas can be adapted to solve problems in medical imaging measurements.
In order to illustrate this presentation, several issues in medical imaging and measurements are discussed and illustrated using case studies and examples.
With the advancement in technology, newer and newer sensors and systems are being developed, day by day, for various engineering, scientific and biomedical applications. However, new sensor systems are still required to be developed further for reliable diagnosis of a particular disease/abnormality and for its therapeutic treatment well in time.
Here, advanced nano-sensors for diagnostic imaging, point-of-care-devices and nao-instrumentation systems for therapeutic treatment of different types of abnormalities and diseases are discussed for health care applications. Main emphasis is placed on the development of bio-chip based sensors and other biologically inspired systems. Design and fabrication aspects of these devices are described in detail.
The sensors and instrumentation systems developed here have direct applications in ubiquitous health care, particularly in monitoring and control of health of old age patients living in isolated/hilly areas. WSN (Wireless Sensor Networking) technology is used as an integral part of u-health care systems. The case study of nano-cancer technology and lithotripsy is presented here as a practical clinical example of the present research.
This research would contribute to scientific advancement of biomedical engineering for better health care, at low cost in an effective and reliable manner.
Adaptability and advanced services for ambient intelligence require an intelligent technological support for understanding the current needs and the desires of users in the interactions with the environment for their daily use, as well as for understanding the current status of the environment also in complex situations. This infrastructure constitutes an essential base for smart living. Various technologies are nowadays converging to support the creation of efficient and effective infrastructures for ambient intelligence.
Artificial intelligence can provide flexible techniques for designing and implementing monitoring and control systems, which can be configured from behavioral examples or by mimicking approximate reasoning processes to achieve adaptable systems. Machine learning can be effective in extracting knowledge form data and learn the actual and desired behaviors and needs of individuals as well as the environment to support informed decisions in managing the environment itself and its adaptation to the people’s needs.
Biometrics can help in identifying individuals or groups: their profiles can be used for adjusting the behavior of the environment. Machine learning can be exploited for dynamically learning the preferences and needs of individuals and enrich/update the profile associated either to such individual or to the group. Biometrics can also be used to create advanced human-computer interaction frameworks.
Cloud computing environments will be instrumental in allowing for world-wide availability of knowledge about the preferences and needs of individuals as well as services for ambient intelligence to build applications easily.
This talk will analyze the opportunities offered by these technologies to support the realization of adaptable operations and intelligent services for smart living in an ambient intelligent infrastructures.
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