Applications of CPS technologies used in health care.
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The design of bug-free and safe medical device software is challenging, especially in complex implantable devices that control and actuate organs who's response is not fully understood. Safety recalls of pacemakers and implantable cardioverter defibrillators between 1990 and 2000 affected over 600,000 devices. Of these, 200,000 or 41%, were due to firmware issues (i.e. software) that continue to increase in frequency [1].
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This project develops an integrated design and simulation environment for the creation of miniature capsule robots (MCRs). An MCR is a biocompatible Cyber-Physical System (CPS) designed to operate in the human body to accomplish diagnostic or therapeutic tasks. A typical MCR has to fulfill three main constraints: safety, low power operation and small size. Advances in miniaturization of electronic devices have made MCRs a reality.
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In the coming decade, microfluidic biochips, or labs--on--a--chip (LoCs), will automate and miniaturize repetitive laboratory experiments that are today performed by humans in domains such as enzymatic, proteomic, and DNA analysis, drug discovery, biomolecular recognition, molecular imaging, toxicity monitoring, and clinical diagnostics.
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Intensity modulated radiation therapy (IMRT) requires tight coordination between computational systems and the physical devices that deliver the prescribed treatment plan, making it a perfect example of cyber-physical system. The current approach to addressing tumor motion in radiation therapy is to treat it as a problem and not as a therapeutic opportunity. Existing treatment planning methods attempt to create dose distributions that are at best dosimetrically equivalent to the static case.
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This project is developing methods for human context awareness in smart buildings, aging-in-place, smart authorization, and emergency response. We enable this using inference using radio frequency (RF) sensing networks, in which channel measurements are made by deployed wireless networking devices. In RF sensing networks, the network is the sensor. We are developing methods to learn human context for smart facilities and elder care, including:
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We are developing a new computational framework and physical platform for modeling, analyzing, and designing dense networks of micro-robotic swarms. The physical platform is based on a bio-hybrid micro-robotic approach, in which bacteria serve as on- board actuators. The micro-robots are controlled through passive (e.g. chemical gradients) and active (e.g. magnetic fields) steering mechanisms. Here, we present the first step towards passive control by characterizing the chemotactic behavior of free- swimming bacteria.
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The interdisciplinary eldertech team at the University of Missouri is dedicated to developing and evaluating technology to keep older adults functioning at high levels and living independently. We are leveraging ongoing research at a unique local eldercare facility (TigerPlace) to study active sensing and fusion using vision and acoustic sensors for the continuous assessment of a resident's risk of falling as well as the reliable detection of falls in the home environment.
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Robotic devices are excellent candidates for delivering repetitive and intensive practice that can restore functional use of the upper limbs, even years after a stroke. Rehabilitation of the wrist and hand in particular are critical for recovery of function, since hands are the primary interface with the world. However, robotic devices that focus on hand rehabilitation are limited due to excessive cost, complexity, or limited functionality.
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The objective of this research is the development of a framework for assessing the reliability and safety of robotic surgery systems during development, field testing, and general deployment. The framework uses accurate simulations to assess pre-clinical reliability before deployment. After deployment, the framework uses data collection through online monitoring of the system as it is being used in the field, followed by analysis to obtain assessments of operational reliability and safety.
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The goal of this project is to integrate digital microfluidics systems with thin-film photodetectors in the top plate to realize biochemical target sensing using fluorescence. System control, adaptation, and reconfiguration through software will lead to a general-purpose lab-on-chip computing platform, in the same way as programmable computing devices allow multifunctional capabilities via software on a hardware platform.
