1,721,056 research outputs found
20200227_WalkInAPark_FP_steps.avi
No full textCOLLECTION ITEM:20200227_WalkInAPark_FP_steps.avi (FP = Filippo Piccinini)COLLECTION TITLE:2020_PiccininiEtAl_FitnessTracking_VideoARTICLE (when using this file, please, cite the following article):Filippo Piccinini, Giovanni Martinelli, Antonella Carbonaro, "Accuracy of mobile applications versus wearable devices in long-term step measurements for analysis in an Internet of Things environment". 2020.DESCRIPTION OF THE FILES IN THE COLLECTION:Edited AVI files showing an operator walking in a park.ITEM TYPE (selected from those available):Media (i.e. ".avi" file)MAIN CONTACTS FOR THESE FILE:1) Dr. Filippo Piccinini, PhD, IRST IRCCS Meldola Italy. Email: [email protected]) Prof. Antonella Carbonaro, University of Bologna. Email: [email protected] AFFILIATIONS FOR THIS PROJECT:1) Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola (FC), Italy.2) University of Bologna, Italy.PROJECT DESCRIPTION:The Project focuses on challenges and opportunities today available to improve people’s well-being using IoT self-tracked Health Data. Recent statistics have shown that around 50% of people in developed countries make use of wearable devices to monitor fitness or physical activity (PA). Practically, people can constantly monitor their health status in an unobtrusive way at no cost and the great amount of patient-generated health data today available gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. All the modern smartphones and fitness trackers are equipped with accelerometers that record accelerations in one or more planes. These data elements are processed into more meaningful variables, such as step counts; time spent in sedentary, light, moderate, or vigorous PA; and flights of stairs climbed. Besides discussing the current limits of the fitness tracking technologies, we supported the usage of wearable devices for mHealth and in general oncology-related analysis about cancer prevention, cancer treatment, and survivorship.PROJECT CATEGORY (selected from those available):Computer VisionPROJECT KEYWORDS (selected from those available):oncology; fitness training; wearable sensors; Physical Activities; statistical inference.LICENCE (selected from those available):GPL 3.0
Colour vignetting correction for microscopy image mosaics used for quantitative analyses
Full text linkImage mosaicing permits to achieve one high resolution image, extending the visible area of the sample while keeping the same resolution. However, intensity inhomogeneity of the stitched images can alter measurements and the right perception of the original sample. The problem can be solved by flat-field correcting the images through the vignetting function. Vignetting correction has been widely addressed for grey-level images, but not for colour ones. In this work, a practical solution for the colour vignetting correction in microscopy, also facing the problem of saturated pixels, is described. In order to assess the quality of the proposed approach, five different tonal correction approaches were quantitatively compared using state-of-the-art metrics and seven pairs of partially overlapping images of seven different samples. The results obtained proved that the proposed approach allows obtaining high quality colour flat-field corrected images and seamless mosaics without employing any blending adjustment. In order to give the opportunity to easily obtain seamless mosaics ready for quantitative analysis, the described vignetting correction method has been implemented in an upgraded release of MicroMos (version 3.0, http://sourceforge.net/p/micromos), an open-source software specifically designed to automatically obtain mosaics of partially overlapped images
20200227_WalkInAPark_FF_steps.avi
No full textCOLLECTION ITEM:20200227_WalkInAPark_FF_steps.avi (FF = Fabrizia Fabbrini)COLLECTION TITLE:2020_PiccininiEtAl_FitnessTracking_VideoARTICLE (when using this file, please, cite the following article):Filippo Piccinini, Giovanni Martinelli, Antonella Carbonaro, "Accuracy of mobile applications versus wearable devices in long-term step measurements for analysis in an Internet of Things environment". 2020.DESCRIPTION OF THE FILES IN THE COLLECTION:Edited AVI files showing an operator walking in a park.ITEM TYPE (selected from those available):Media (i.e. ".avi" file)MAIN CONTACTS FOR THESE FILE:1) Dr. Filippo Piccinini, PhD, IRST IRCCS Meldola Italy. Email: [email protected]) Prof. Antonella Carbonaro, University of Bologna. Email: [email protected] AFFILIATIONS FOR THIS PROJECT:1) Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola (FC), Italy.2) University of Bologna, Italy.PROJECT DESCRIPTION:The Project focuses on challenges and opportunities today available to improve people’s well-being using IoT self-tracked Health Data. Recent statistics have shown that around 50% of people in developed countries make use of wearable devices to monitor fitness or physical activity (PA). Practically, people can constantly monitor their health status in an unobtrusive way at no cost and the great amount of patient-generated health data today available gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. All the modern smartphones and fitness trackers are equipped with accelerometers that record accelerations in one or more planes. These data elements are processed into more meaningful variables, such as step counts; time spent in sedentary, light, moderate, or vigorous PA; and flights of stairs climbed. Besides discussing the current limits of the fitness tracking technologies, we supported the usage of wearable devices for mHealth and in general oncology-related analysis about cancer prevention, cancer treatment, and survivorship.PROJECT CATEGORY (selected from those available):Computer VisionPROJECT KEYWORDS (selected from those available):oncology; fitness training; wearable sensors; Physical Activities; statistical inference.LICENCE (selected from those available):GPL 3.0
DS4H-Image-Alignment
No full text"Data Science for Health (DS4H) Image Alignment" is a user-friendly tool freely provided as an ImageJ/Fiji plugin. With DS4H Image Alignment, 2D images can be easily aligned (i.e. co-registered) by defining with a few clicks some well visible reference marks.
The implemented least-squares method automatically approximates the solution of the mathematical overdetermined system, so to define the registration matrix then used for aligning the different images. It also considers rotations and scale changes in case of object dilation/shrink. Finally, it provides an iterative subroutine for a fine alignment, to easily reach a very good image co-registration quality.
DS4H Image Alignment has been implemented in Java as a plugin for ImageJ/Fiji. It works with “.svs” files, but also all the medical imaging formats included in the [[Bio-formats]] library
20200227_WalkInAPark_LP_steps.avi
No full textCOLLECTION ITEM:20200227_WalkInAPark_LP_steps.avi (LP = Lamberto Piccinini)COLLECTION TITLE:2020_PiccininiEtAl_FitnessTracking_VideoARTICLE (when using this file, please, cite the following article):Filippo Piccinini, Giovanni Martinelli, Antonella Carbonaro, "Accuracy of mobile applications versus wearable devices in long-term step measurements for analysis in an Internet of Things environment". 2020.DESCRIPTION OF THE FILES IN THE COLLECTION:Edited AVI files showing an operator walking in a park.ITEM TYPE (selected from those available):Media (i.e. ".avi" file)MAIN CONTACTS FOR THESE FILE:1) Dr. Filippo Piccinini, PhD, IRST IRCCS Meldola Italy. Email: [email protected]) Prof. Antonella Carbonaro, University of Bologna. Email: [email protected] AFFILIATIONS FOR THIS PROJECT:1) Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola (FC), Italy.2) University of Bologna, Italy.PROJECT DESCRIPTION:The Project focuses on challenges and opportunities today available to improve people’s well-being using IoT self-tracked Health Data. Recent statistics have shown that around 50% of people in developed countries make use of wearable devices to monitor fitness or physical activity (PA). Practically, people can constantly monitor their health status in an unobtrusive way at no cost and the great amount of patient-generated health data today available gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. All the modern smartphones and fitness trackers are equipped with accelerometers that record accelerations in one or more planes. These data elements are processed into more meaningful variables, such as step counts; time spent in sedentary, light, moderate, or vigorous PA; and flights of stairs climbed. Besides discussing the current limits of the fitness tracking technologies, we supported the usage of wearable devices for mHealth and in general oncology-related analysis about cancer prevention, cancer treatment, and survivorship.PROJECT CATEGORY (selected from those available):Computer VisionPROJECT KEYWORDS (selected from those available):oncology; fitness training; wearable sensors; Physical Activities; statistical inference.LICENCE (selected from those available):GPL 3.0
Reliability of Body Temperature Measurements Obtained with Contactless Infrared Point Thermometers Commonly Used during the COVID-19 Pandemic
Full text linkDuring the COVID-19 pandemic, there has been a significant increase in the use of non-contact infrared devices for screening the body temperatures of people at the entrances of hospitals, airports, train stations, churches, schools, shops, sports centres, offices, and public places in general. The strong correlation between a high body temperature and SARS-CoV-2 infection has motivated the governments of several countries to restrict access to public indoor places simply based on a person’s body temperature. Negating/allowing entrance to a public place can have a strong impact on people. For example, a cancer patient could be refused access to a cancer centre because of an incorrect high temperature measurement. On the other hand, underestimating an individual’s body temperature may allow infected patients to enter indoor public places where it is much easier for the virus to spread to other people. Accordingly, during the COVID-19 pandemic, the reliability of body temperature measurements has become fundamental. In particular, a debated issue is the reliability of remote temperature measurements, especially when these are aimed at identifying in a quick and reliable way infected subjects. Working distance, body–device angle, and light conditions and many other metrological and subjective issues significantly affect the data acquired via common contactless infrared point thermometers, making the acquisition of reliable measurements at the entrance to public places a challenging task. The main objective of this work is to sensitize the community to the typical incorrect uses of infrared point thermometers, as well as the resulting drifts in measurements of body temperature. Using several commercial contactless infrared point thermometers, we performed four different experiments to simulate common scenarios in a triage emergency room. In the first experiment, we acquired several measurements for each thermometer without measuring the working distance or angle of inclination to show that, for some instruments, the values obtained can differ by 1 °C. In the second and third experiments, we analysed the impacts of the working distance and angle of inclination of the thermometers, respectively, to prove that only a few cm/degrees can cause drifts higher than 1 °C. Finally, in the fourth experiment, we showed that the light in the environment can also cause changes in temperature up to 0.5 °C. Ultimately, in this study, we quantitatively demonstrated that the working distance, angle of inclination, and light conditions can strongly impact temperature measurements, which could invalidate the screening result
Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements
Full text linkFitness sensors and health systems are paving the way toward improving the quality
of medical care by exploiting the benefits of new technology. For example, the great amount of
patient-generated health data available today gives new opportunities to measure life parameters
in real time and create a revolution in communication for professionals and patients. In this
work, we concentrated on the basic parameter typically measured by fitness applications and
devices—the number of steps taken daily. In particular, the main goal of this study was to compare
the accuracy and precision of smartphone applications versus those of wearable devices to give users
an idea about what can be expected regarding the relative difference in measurements achieved using
different system typologies. In particular, the data obtained showed a difference of approximately
30%, proving that smartphone applications provide inaccurate measurements in long-term analysis,
while wearable devices are precise and accurate. Accordingly, we challenge the reliability of previous
studies reporting data collected with phone-based applications, and besides discussing the current
limitations, we support the use of wearable devices for mHealth
Integrating Heterogeneous Data of Healthcare Devices to enable Domain Data Management
Full text linkThe growth of data produced for example by IoT devices has playing a major role in developing healthcare applications able to effectiveness handle the vast amount of information. The challenge lies in representing volumes of data, integrating and understanding their various formats and sources. Cognitive computing systems offer promise for analysing, accessing, integrating, and investigating data in order to improve outcomes across many domains, including healthcare. This paper presents an ontology-based system for the eHealth domain. It provides semantic interoperability among heterogeneous IoT fitness and wellness devices and facilitates data integration and sharing. The novelty of the proposed approach lies in exploiting semantic web technologies to explicitly describe the meaning of sensor data and define a common communication strategy for information representation and exchange
Advances in cancer modeling: fluidic systems for increasing representativeness of large 3D multicellular spheroids
Full text linkThe representativeness of a cellular model is fundamental in pre-clinical cancer studies. Size, heterogeneity and perfusion are three key aspects characterizing the behavior of the tumor and driving its progression. In vitro resemblance of in vivo tumor conditions can be maximized by: (a) using heterogeneous large-sized three-dimensional (3D) multicellular models; (b) utilizing fluidic systems to modulate the culture microenvironment. This work discusses the benefits of using large-sized spheroids as 3D pre-clinical culture models, besides analyzing the microfluidic systems that permit their cultivation and manipulation in dynamic controlled conditions
Semantic Modelling of Smart Healthcare Data
No full textNowadays, healthcare is becoming increasingly connected
and increasingly complex. These changes provide opportunities
and challenges to the research community. For instance,
the enormous volume of data gathered from IoT wearable fitness
devices and wellness appliances, if effectively analysed and understood,
can be exploited to improve people’s well-being and identify
predictive markers of future diseases. However, due to the lack of
devices interoperability and heterogeneity of data representation
formats, the IoT healthcare landscape is characterised by a
pervasive presence of ”data silos” which prevents users and health
practitioners from obtaining an overall view of whole knowledge.
Semantic web technologies, such as ontologies and inference rules,
have been shown as a promising way for the integration and
exploitation of data from heterogeneous sources. In this paper, we
present a semantic data model useful to: (a) analyse information
from unstructured data sources along with generic or domain
specific datasets; (b) unify them in an interlinked data processing
area. The proposed semantic eHealth system enables automatic
inferences and logical reasoning, and can significantly facilitate
reuse, exploitation and possible extension of IoT health data
sources.. Nowadays, healthcare is becoming increasingly connected and increasingly complex. These changes provide opportunities and challenges to the research community. For instance, the enormous volume of data gathered from IoT wearable fitness devices and wellness appliances, if effectively analysed and understood, can be exploited to improve peo-ple’s well-being and identify predictive markers of future diseases. However, due to the lack of devices interoperability and heterogeneity of data representation formats, the IoT healthcare landscape is characterised by a pervasive presence of “data silos” which prevents users and health practitioners from obtaining an overall view of whole knowledge. Semantic web technologies, such as ontologies and inference rules have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (a) analyse information from unstructured data sources along with generic or domain specific datasets; (b) unify them in an interlinked data processing area. The proposed semantic eHealth system enables automatic inferences and logical reasoning, and can significantly facilitate reuse, exploitation and possible extension of IoT health data sources
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