SmartMedDev 2017 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 3
Title:

A Methodology to Reduce the Complexity of Validation Model Creation from Medical Specification Document
Authors:

Francesco Gargiulo, Stefano Silvestri, Mariarosaria Fontanella and Mario Ciampi
Abstract: In this paper we propose a novel approach to reduce the complexity of the definition and implementation of a medical document validation model. Usually the conformance requirements for specifications are contained in documents written in natural language format and it is necessary to manually translate them in a software model for validation purposes. It should be very useful to extract and group the conformance rules that have a similar pattern to reduce the manual effort needed to accomplish this task. We will show an innovative cluster approach that automatically evaluates the optimal number of groups using an iterative method based on internal cluster measures evaluation. We will show the application of this method on two case studies: i) Patient Summary (Profilo Sanitario Sintetico) and ii) Hospital Discharge Letter (Lettera di Dimissione Ospedaliera) for the Italian specification of the conformance rules.
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Paper Nr: 5
Title:

A Statistical Analysis for the Evaluation of the Use of Wearable and Wireless Sensors for Fall Risk Reduction
Authors:

Giovanna Sannino, Ivanoe De Falco and Giuseppe De Pietro
Abstract: The aim of this study is to investigate the correlation between, on the one hand, personal and life-style indicators and, on the other hand, the risk of falling. As indicators we consider here for each subject age, body mass index, and information about physical activity habits, while a subject’s risk of falling is estimated by the Mini-BES test score. Three different groups of subjects are taken into account, namely healthy, suffering from metabolic diseases and suffering from cardiovascular diseases. Firstly, we aim at finding explicit linear correlations for any pair of parameters. Secondly, we wish to pay attention to whether or not these correlations change as the health state of the subjects does. The final goal is to move the first steps towards the design of a system composed by wearable sensors, a mobile device, and an app that would be able to help people in improving their life-style so as to decrease their falling risk.
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Paper Nr: 8
Title:

Modular Health Kiosk based on Web Technologies
Authors:

João Silva, Pedro Brandão and Rui Prior
Abstract: We describe a modular and easily reconfigurable Health Kiosk based on common, off-the-shelf Personal Health Devices and a computer with a touchscreen interface. The Kiosk is implemented in standard web technologies (JavaScript, HTML and CSS) on top of the Electron platform. It is intended to be used autonomously by the patients. It is highly modular, can easily be adapted and reconfigured by health professionals with little to no computer expertise, using a graphical interface, to adapt to different groups of patients and use cases. We document our findings, identifying problems faced throughout the development and solutions to those problems.
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Paper Nr: 9
Title:

Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention
Authors:

A. Galletti, R. Montella, L. Marcellino, A. Riccio, D. Di Luccio, A. Brizius and I. Foster
Abstract: Monitoring nearshore sea water pollution using connected smart devices could be nowadays impracticable due to the aggressive saline environment, the network availability and the maintain and calibration costs. Accurate forecast of marine pollution is most needed to evaluate the adverse effects on coastal inhabitants’ health when fishes and mussels farming economically characterizes the local social background. In an operational context, numerical simulations are performed routinely on a dedicated computational infrastructure producing space and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples. In this paper we present our results in developing a community open source Lagrangian pollutant transport and dispersion model, leveraging on hierarchical parallelism implying distributed memory, shared memory and GPGPUs. Some numerical details are also discussed. This system has been used to develop an alarm system to help local authorities in making decisions regarding the collection of mussels. The model setup and the simulation results will be improved using FairWind, an under development system dedicated to coastal marine crowdsourced data gathering and sharing, based on smart devices and Internet of Things afloat.
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Short Papers
Paper Nr: 4
Title:

Discussions of a Preliminary Hand Hygiene Compliance Monitoring Application-as-a-service
Authors:

Peng Zhang, Marcelino Rodriguez-Cancio, Douglas Schmidt, Jules White and Tom Dennis
Abstract: Hospital Acquired Infections (HAIs) are a global concern as they impose significant economic consequences on the healthcare systems. In the U.S. alone, HAIs have cost hospitals an estimated $9.8 billion a year. An effective measure to reduce the spread of HAIs is for Health Care Workers (HCWs) to comply with recommended hand hygiene (HH) guidelines. Unfortunately, HH guideline compliance is currently poor, forcing hospitals to implement controls. The current standard for monitoring compliance is overt direct observation of hand sanitation of HCWs by trained observers, which can be time-consuming, costly, biased, and sporadic. Our research describes a hand hygiene compliance monitoring app, Hygiene Police (HyPo), that can be deployed as a service to alleviate the manual effort, reduce errors, and improve existing compliance monitoring practice. HyPo exploits machine learning analyses of handwashing compliance data from a 30-bed intensive care unit to predict future compliance characteristics. Based on the results, HyPo then provides HWCs with timely feedback and augments the current monitoring approach to improve compliance.
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Paper Nr: 6
Title:

A Real-time m-Health Monitoring System: An Integrated Solution Combining the Use of Several Wearable Sensors and Mobile Devices
Authors:

Salvatore Naddeo, Laura Verde, Manolo Forastiere, Giuseppe De Pietro and Giovanna Sannino
Abstract: Nowadays the upsurge in the prevalence of chronic diseases represents an increasing burden on individuals and society. Chronic diseases can require repeated and frequent hospital treatment to control vital parameters of interest. The use of automatic instruments for a real-time monitoring of biological parameters constitutes a valid instrument to improve the patient’s quality of life. The integration of mobile communications with wearable devices has facilitated the shift of healthcare assistance from clinic-centric to patient-centric monitoring. In this paper, a real-time monitoring system is proposed. The system is conceptualized to provide an instrument for patients, by means of which they can easily monitor, analyse and save their own vital signs using wearable sensors and an Android device such as a smartphone or tablet, offering an efficient solution in terms of a decrease in time, human error and cost.
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Paper Nr: 7
Title:

PerDMCS: Weighted Fusion of PPG Signal Features for Robust and Efficient Diabetes Mellitus Classification
Authors:

V. Ramu Reddy, Anirban Dutta Choudhury, Srinivasan Jayaraman, Naveen Kumar Thokala, Parijat Deshpande and Venkatesh Kaliaperumal
Abstract: Non-invasive detection of Diabetes Mellitus (DM) has attracted a lot of interest in the recent years in pervasive health care. In this paper, we explore features related to heart rate variability (HRV) and signal pattern of the waveform from photoplethysmogram (PPG) signal for classifying DM (Type 2). HRV features includes timedomain (F1), frequency domain (F2), non-linear features (F3) where as waveform features (F4) are one set of features such as height, width, slope and durations of pulse. The study was carried out on 50 healthy subjects and 50 DM patients. Support Vector Machines (SVM) are used to capture the discriminative information between the above mentioned healthy and DM categories, from the proposed features. The SVM models are developed separately using different sets of features F1, F2, F3,and F4, respectively. The classification performance of the developed SVM models using time-domain, frequency domain, non-linear and waveform features is observed to be 73%, 78%, 80% and 77%. The performance of the system using combination of all features is 82%. In this work, the performance of the DM classification system by combining the above mentioned feature sets with different percentage of discriminate features from each set is also examined. Furthermore weight based fusion is performed using confidence values obtained from each model to find the optimal set of features from each set with optimal weights for each set. The best performance accuracy of 89% is obtained by scores fusion where combinations of mixture of 90% features from the feature sets F1 and F2 and mixture of 100% features from the feature sets F3 and F4, with fusion optimal weights of 0.3 and 0.7, respectively.
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Paper Nr: 10
Title:

Patient-centric Handling of Diverse Signals in the mHealth Environment
Authors:

Jan Sliwa
Abstract: In the context of the Mobile Health (or Telemedicine) many signals (data items) are exchanged between the medical devices, data storage units and involved persons. They are often treated as a uniform mass of “medical data”, especially in the Big Data community. At a closer look, they unveil various characteristics, like type (single / continuous), required quality and tolerance for missing values. As in medical care time is crucial, realtime characteristics are important, like the sampling rate and the overall reaction time of the emergency system. The handling of data depends also on the severity of the medical case. Data are transferred and processed by external systems, therefore the overall function depends on the environment and the persons involved: from the user/patient to a dedicated medical emergency team. The collected data can be anonymously reused to gain or verify knowledge, what calls for a fair trade-off between the interests of the individual patient and the community. This paper discusses the semantics of mHealth data, like medical requirements, physical constraints and human aspects. This analysis leads to a more precise mathematical definition of the required data handling that helps to construct mHealth systems that better fulfill the health support function.
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