Cellular morphology has recently been indicated as a powerful indicator of cellular function. The analysis of cell shape has evolved from rudimentary forms of microscopic visual inspection to more advanced methodologies that utilize high-resolution microscopy coupled with sophisticated computer hardware and software for data analysis. Despite this progress, there is still a lack of standardization in quantification of morphometric parameters. In addition, uncertainty remains as to which methodologies and parameters of cell morphology will yield meaningful data, which methods should be utilized to categorize cell shape, and the extent of reliability of measurements and the interpretation of the resulting analysis. A large range of descriptors has been employed to objectively assess the cellular morphology in two-dimensional and three-dimensional domains. Intuitively, simple and applicable morphometric descriptors are preferable and standardized protocols for cell shape analysis can be achieved with the help of computerized tools. In this review, cellular morphology is discussed as a descriptor of cellular function and the current morphometric parameters that are used quantitatively in two- and three-dimensional environments are described. Furthermore, the current problems associated with these morphometric measurements are addressed. Copyright © 2015 John Wiley & Sons, Ltd.
Telemonitoring is a tool with proven results demonstrating clinical benefits in reducing mortality and hospitalizations. In this context, heart rate and rhythm are vital signs of utmost importance for monitoring a patient's condition. Recent advances in mobile technologies have allowed smartphones to be of great usage in this scenario, given its processing power, connectivity capabilities, sensor hardware and camera quality. This paper describes the usage of a smartphone camera to detect the heart rate and rhythm of a patient. A photoplethysmogram signal is obtained with the user's fingertip placed over the smartphone camera. An evaluation has been performed on 43 subjects with heart failure. For each patient the signal obtained with the smartphone camera was compared with the ECG signal acquired in hospital environment. Results demonstrate the heart rate can be effectively estimated using that approach with an error rate of 4.75%. Atrial Fibrillation detection with our method achieved a specificity of 97% and sensitivity of 75%
The incidence of chronic diseases is increasing and monitoring patients in a home environment is recommended. Noncompliance with prescribed medication regimens is a concern, especially among older people. Heart failure is a chronic disease that requires patients to follow strict medication plans permanently. With the objective of helping these patients managing information about their medicines and increasing adherence, the personal medication advisor CARMIE was developed as a conversational agent capable of interacting, in Portuguese, with users through spoken natural language. The system architecture is based on a language parser, a dialog manager, and a language generator, integrated with already existing tools for speech recognition and synthesis. All modules work together and interact with the user through an Android application, supporting users to manage information about their prescribed medicines. The authors also present a preliminary usability study and further considerations on CARMIE.
In recent years, periocular recognition has become a popular alternative to face and iris recognition in less ideal acquisition scenarios. An interesting example of such scenarios is the usage of mobile devices for recognition purposes. With the growing popularity and easy access to such devices, the development of robust biometric recognition algorithms to work under such conditions finds strong motivation. In the present work we assess the performance of extended versions of two state-ofthe-art periocular recognition algorithms on the publicly available CSIP database, a recent dataset composed of images acquired under highly unconstrained and multi-sensor mobile scenarios. The achieved results show each algorithm is better fit to tackle different scenarios and applications of the biometric recognition problem.
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