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Nuzzi A, Latorre V, Semisa D, Scozzi B. Improving the mental health care process in response to Covid-19 pandemic: The case of a penitentiary mental health division. PLoS One 2023; 18:e0293492. [PMID: 37903102 PMCID: PMC10615294 DOI: 10.1371/journal.pone.0293492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/29/2023] [Indexed: 11/01/2023] Open
Abstract
Covid-19 outbreak led all organizations to reorganize their processes to prevent infection and contagion risk. All healthcare facilities, included penitentiary mental health services, had to redesign their processes to safely deliver care services. In this paper, the case of a Penitentiary Mental Health Division located in southern Italy is presented. Soft System Methodology and Business process management principles and techniques are adopted to analyse and redesign the detainees' mental health care process. The process, characterized by direct, close and prolonged contact with patients, exposes detainees and healthcare staff to a high Covid-19 infection risk. Through document analysis, interviews with the actors involved in the process and direct observation, the process's inefficiencies and criticalities are identified. The process is redesigned to make it compliant with Covid-19 prevention provisions and national penitentiary regulations and address the other criticalities. The proposed methodological approach-which innovatively combines Soft System Methodology and Business Process Management-constitutes a human-centered process-based redesign approach that can be used both in healthcare and other organizational settings.
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Affiliation(s)
- Angela Nuzzi
- Department of Mechanics, Mathematics, and Management, Polytechnic University of Bari, Bari, Italy
| | - Valeria Latorre
- Complex Organization Unit Psychiatric Diagnosis and Care Service UO San Paolo, ASL Bari, Bari, Italy
- Penitentiary Mental Health Service, Department of Mental Health, ASL Bari, Bari, Italy
| | - Domenico Semisa
- Complex Organization Unit Psychiatric Diagnosis and Care Service UO San Paolo, ASL Bari, Bari, Italy
| | - Barbara Scozzi
- Department of Mechanics, Mathematics, and Management, Polytechnic University of Bari, Bari, Italy
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2
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Iglesias N, Juarez JM, Campos M. Business Process Model and Notation and openEHR Task Planning for Clinical Pathway Standards in Infections: Critical Analysis. J Med Internet Res 2022; 24:e29927. [PMID: 36107480 PMCID: PMC9523526 DOI: 10.2196/29927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/23/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Background Clinical pathways (CPs) are usually expressed by means of workflow formalisms, providing health care personnel with an easy-to-understand, high-level conceptual model of medical steps in specific patient conditions, thereby improving overall health care process quality in clinical practice. From a standardized perspective, the business process model and notation (BPMN), a widely spread general-purpose process formalism, has been used for conceptual modeling in clinical domains, mainly because of its easy-to-use graphical notation, facilitating the common understanding and communication of the parties involved in health care. However, BPMN is not particularly oriented toward the peculiarities of complex clinical processes such as infection diagnosis and treatment, in which time plays a critical role, which is why much of the BPMN clinical-oriented research has revolved around how to extend the standard to address these special needs. The shift from an agnostic, general-purpose BPMN notation to a natively clinical-oriented notation such as openEHR Task Planning (TP) could constitute a major step toward clinical process improvement, enhancing the representation of CPs for infection treatment and other complex scenarios. Objective Our work aimed to analyze the suitability of a clinical-oriented formalism (TP) to successfully represent typical process patterns in infection treatment, identifying domain-specific improvements to the standard that could help enhance its modeling capabilities, thereby promoting the widespread adoption of CPs to improve medical practice and overall health care quality. Methods Our methodology consisted of 4 major steps: identification of key features of infection CPs through literature review, clinical guideline analysis, and BPMN extensions; analysis of the presence of key features in TP; modeling of relevant process patterns of catheter-related bloodstream infection as a case study; and analysis and proposal of extensions in view of the results. Results We were able to easily represent the same logic applied in the extended BPMN-based process models in our case study using out-of-the-box standard TP primitives. However, we identified possible improvements to the current version of TP to allow for simpler conceptual models of infection CPs and possibly of other complex clinical scenarios. Conclusions Our study showed that the clinical-oriented TP specification is able to successfully represent the most complex catheter-related bloodstream infection process patterns depicted in our case study and identified possible extensions that can help increase its adequacy for modeling infection CPs and possibly other complex clinical conditions.
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Affiliation(s)
- Natalia Iglesias
- Instituto de Investigación de Tecnologías de la Información y las Comunicaciones Orientadas, University of Murcia, Murcia, Spain
| | - Jose M Juarez
- Instituto de Investigación de Tecnologías de la Información y las Comunicaciones Orientadas, University of Murcia, Murcia, Spain
| | - Manuel Campos
- Instituto de Investigación de Tecnologías de la Información y las Comunicaciones Orientadas, University of Murcia, Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria - Arrixaca, Murcia, Spain
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The Hitchhiker’s Guide to Fused Twins: A Review of Access to Digital Twins In Situ in Smart Cities. REMOTE SENSING 2022. [DOI: 10.3390/rs14133095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.
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Baratta J, Brown-Johnson C, Safaeinili N, Goldman Rosas L, Palaniappan L, Winget M, Mahoney M. Patient and Health Professional Perceptions of Telemonitoring for Hypertension Management: Qualitative Study. JMIR Form Res 2022; 6:e32874. [PMID: 35687380 PMCID: PMC9233257 DOI: 10.2196/32874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 03/07/2022] [Accepted: 04/13/2022] [Indexed: 12/04/2022] Open
Abstract
Background Hypertension is the most prevalent and important risk factor for cardiovascular disease, affecting nearly 50% of the US adult population; however, only 30% of these patients achieve controlled blood pressure (BP). Incorporating strategies into primary care that take into consideration individual patient needs, such as remote BP monitoring, may improve hypertension management. Objective From March 2018 to December 2018, Stanford implemented a precision health pilot called Humanwide, which aimed to leverage high-technology and high-touch medicine to tailor individualized care for conditions such as hypertension. We examined multi-stakeholder perceptions of hypertension management in Humanwide to evaluate the program’s acceptability, appropriateness, feasibility, and sustainability. Methods We conducted semistructured interviews with 16 patients and 15 health professionals to assess their experiences with hypertension management in Humanwide. We transcribed and analyzed the interviews using a hybrid approach of inductive and deductive analysis to identify common themes around hypertension management and consensus methods to ensure reliability and validity. Results A total of 63% (10/16) of the patients and 40% (6/15) of the health professionals mentioned hypertension in the context of Humanwide. These participants reported that remote BP monitoring improved motivation, BP control, and overall clinic efficiency. The health professionals discussed feasibility challenges, including the time needed to analyze BP data and provide individualized feedback, integration of BP data, technological difficulties with the BP cuff, and decreased patient use of remote BP monitoring over time. Conclusions Remote BP monitoring for hypertension management in Humanwide was acceptable to patients and health professionals and appropriate for care. Important challenges need to be addressed to improve the feasibility and sustainability of this approach by leveraging team-based care, engaging patients to sustain remote BP monitoring, standardizing electronic medical record integration of BP measurements, and finding more user-friendly BP cuffs.
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Affiliation(s)
- Juliana Baratta
- Division of Primary Care and Population Health, Stanford School of Medicine, Palo Alto, CA, United States
| | - Cati Brown-Johnson
- Division of Primary Care and Population Health, Stanford School of Medicine, Palo Alto, CA, United States
| | - Nadia Safaeinili
- Division of Primary Care and Population Health, Stanford School of Medicine, Palo Alto, CA, United States
| | - Lisa Goldman Rosas
- Division of Primary Care and Population Health, Stanford School of Medicine, Palo Alto, CA, United States
| | - Latha Palaniappan
- Division of Primary Care and Population Health, Stanford School of Medicine, Palo Alto, CA, United States
| | - Marcy Winget
- Division of Primary Care and Population Health, Stanford School of Medicine, Palo Alto, CA, United States
| | - Megan Mahoney
- Division of Primary Care and Population Health, Stanford School of Medicine, Palo Alto, CA, United States
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Di Credico A, Perpetuini D, Izzicupo P, Gaggi G, Cardone D, Filippini C, Merla A, Ghinassi B, Di Baldassarre A. Estimation of Heart Rate Variability Parameters by Machine Learning Approaches Applied to Facial Infrared Thermal Imaging. Front Cardiovasc Med 2022; 9:893374. [PMID: 35656402 PMCID: PMC9152459 DOI: 10.3389/fcvm.2022.893374] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/04/2022] [Indexed: 01/18/2023] Open
Abstract
Heart rate variability (HRV) is a reliable tool for the evaluation of several physiological factors modulating the heart rate (HR). Importantly, variations of HRV parameters may be indicative of cardiac diseases and altered psychophysiological conditions. Recently, several studies focused on procedures for contactless HR measurements from facial videos. However, the performances of these methods decrease when illumination is poor. Infrared thermography (IRT) could be useful to overcome this limitation. In fact, IRT can measure the infrared radiations emitted by the skin, working properly even in no visible light illumination conditions. This study investigated the capability of facial IRT to estimate HRV parameters through a face tracking algorithm and a cross-validated machine learning approach, employing photoplethysmography (PPG) as the gold standard for the HR evaluation. The results demonstrated a good capability of facial IRT in estimating HRV parameters. Particularly, strong correlations between the estimated and measured HR (r = 0.7), RR intervals (r = 0.67), TINN (r = 0.71), and pNN50 (%) (r = 0.70) were found, whereas moderate correlations for RMSSD (r = 0.58), SDNN (r = 0.44), and LF/HF (r = 0.48) were discovered. The proposed procedure allows for a contactless estimation of the HRV that could be beneficial for evaluating both cardiac and general health status in subjects or conditions where contact probe sensors cannot be used.
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Affiliation(s)
- Andrea Di Credico
- Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy.,Reprogramming and Cell Differentiation Lab, Center for Advanced Studies and Technology, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - David Perpetuini
- Department of Neurosciences, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - Pascal Izzicupo
- Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - Giulia Gaggi
- Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy.,Reprogramming and Cell Differentiation Lab, Center for Advanced Studies and Technology, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - Daniela Cardone
- Department of Neurosciences, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - Chiara Filippini
- Department of Neurosciences, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - Barbara Ghinassi
- Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy.,Reprogramming and Cell Differentiation Lab, Center for Advanced Studies and Technology, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
| | - Angela Di Baldassarre
- Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy.,Reprogramming and Cell Differentiation Lab, Center for Advanced Studies and Technology, University "G. d'Annunzio" of Chieti - Pescara, Chieti, Italy
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Verzani RH, Serapião ABDS. [Technological contributions for health: outlook on physical activity]. CIENCIA & SAUDE COLETIVA 2021; 25:3227-3238. [PMID: 32785556 DOI: 10.1590/1413-81232020258.19742018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 11/15/2018] [Indexed: 11/21/2022] Open
Abstract
The scope of this paper sought to analyze the potential of using Internet technologies of wearable accessories and devices and the possible interventions in physical activities, seeking improvements with respect to physical inactivity and Chronic Non-Communicable Diseases (CNCDs). By means of a bibliographical review, it was revealed that there is great concern regarding physical inactivity and CNCDs as well as the increasing research focus on these technological strategies. The amount of data collected in real time is one of the strengths of the devices, which can assist in longitudinal research, interventions in patients and also in physical activities performed, revolutionizing relationships and interventions in the area.
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Affiliation(s)
- Renato Henrique Verzani
- Departamento de Educação Física, Instituto de Biociências, Universidade Estadual Paulista Júlio de Mesquita Filho. Av. 24A 1515, Bela Vista. 13500-060 Rio Claro SP Brasil.
| | - Adriane Beatriz de Souza Serapião
- Departamento de Estatística, Matemática Aplicada e Computação, Instituto de Geociências e Ciências Exatas. UNESP Rio Claro SP Brasil
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Javaid M, Khan IH. Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. J Oral Biol Craniofac Res 2021; 11:209-214. [PMID: 33665069 PMCID: PMC7897999 DOI: 10.1016/j.jobcr.2021.01.015] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 01/23/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND/OBJECTIVES The Internet of Things (IoT) can create disruptive innovation in healthcare. Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare. For this, a brief study is required for research directions. METHODS Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology's capabilities. This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic. RESULTS Briefly studied the significant achievements of IoT with the help of a process chart. Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic. Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them. CONCLUSIONS In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives. Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases. IoT's sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic. In the medical field, IoT's focus is to help perform the treatment of different COVID-19 cases precisely. It makes the surgeon job easier by minimising risks and increasing the overall performance. By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient. This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital. Medical students can now be better trained for disease detection and well guided for the future course of action. IoT's proper usage can help correctly resolve different medical challenges like speed, price, and complexity. It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient. This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days.
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Affiliation(s)
- Mohd Javaid
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
| | - Ibrahim Haleem Khan
- School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
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8
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Deep learning-based ambient assisted living for self-management of cardiovascular conditions. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05678-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
AbstractAccording to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.
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Patro SP, Padhy N, Chiranjevi D. Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00484-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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10
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Habibzadeh H, Dinesh K, Shishvan OR, Boggio-Dandry A, Sharma G, Soyata T. A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective. IEEE INTERNET OF THINGS JOURNAL 2020; 7:53-71. [PMID: 33748312 PMCID: PMC7970885 DOI: 10.1109/jiot.2019.2946359] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.
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Affiliation(s)
- Hadi Habibzadeh
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627
| | - Omid Rajabi Shishvan
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Andrew Boggio-Dandry
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627
| | - Tolga Soyata
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
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Multi-Sensor-Fusion Approach for a Data-Science-Oriented Preventive Health Management System: Concept and Development of a Decentralized Data Collection Approach for Heterogeneous Data Sources. Int J Telemed Appl 2019; 2019:9864246. [PMID: 31687017 PMCID: PMC6800927 DOI: 10.1155/2019/9864246] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/09/2019] [Accepted: 07/21/2019] [Indexed: 11/17/2022] Open
Abstract
Investigations in preventive and occupational medicine are often based on the acquisition of data in the customer's daily routine. This requires convenient measurement solutions including physiological, psychological, physical, and sometimes emotional parameters. In this paper, the introduction of a decentralized multi-sensor-fusion approach for a preventive health-management system is described. The aim is the provision of a flexible mobile data-collection platform, which can be used in many different health-care related applications. Different heterogeneous data sources can be integrated and measured data are prepared and transferred to a superordinated data-science-oriented cloud-solution. The presented novel approach focuses on the integration and fusion of different mobile data sources on a mobile data collection system (mDCS). This includes directly coupled wireless sensor devices, indirectly coupled devices offering the datasets via vendor-specific cloud solutions (as e.g., Fitbit, San Francisco, USA and Nokia, Espoo, Finland) and questionnaires to acquire subjective and objective parameters. The mDCS functions as a user-specific interface adapter and data concentrator decentralized from a data-science-oriented processing cloud. A low-level data fusion in the mDCS includes the synchronization of the data sources, the individual selection of required data sets and the execution of pre-processing procedures. Thus, the mDCS increases the availability of the processing cloud and in consequence also of the higher level data-fusion procedures. The developed system can be easily adapted to changing health-care applications by using different sensor combinations. The complex processing for data analysis can be supported and intervention measures can be provided.
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12
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Modeling and Deploying IoT-Aware Business Process Applications in Sensor Networks. SENSORS 2018; 19:s19010111. [PMID: 30598038 PMCID: PMC6338957 DOI: 10.3390/s19010111] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 12/21/2018] [Accepted: 12/24/2018] [Indexed: 11/17/2022]
Abstract
The concept of the Internet of Things (IoT) is an important part of the next generation of information. Wireless sensor networks are composed of independent distributed smart sensor nodes and gateways. These discrete sensors constantly gather external physical information, such as temperature, sound, and vibration. Owing to the diversity of sensor devices and the complexity of the sensor sensing environment, the direct modeling of an IoT-aware business process application is particularly difficult. In addition, how to effectively deploy those designed applications to discrete servers in the heterogeneous sensor networks is also a pressing problem. In this paper, we propose a resource-oriented modeling approach and a dynamic consistent hashing (DCH)-based deploying algorithm to solve the above problems. Initially, we extended the graphic and machine-readable model of Business Process Model Notation (BPMN) 2.0 specification, making it able to support the direct modeling of an IoT-aware business process application. Furthermore, we proposed the DCH-based deploying algorithm to solve the problem of dynamic load balancing and access efficiency in the distributed execution environment. Finally, we designed an actual extended BPMN plugin in Eclipse. The approach presented in this paper has been validated to be effective.
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DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks. COMPUTATION 2018. [DOI: 10.3390/computation6040062] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is trending. In traditional setups, the cloud backend receives the healthcare data and performs monitoring and prediction for diseases, diagnosis, and wellness prediction. Fog computing (FC) is a distributed computing paradigm that leverages low-power embedded processors in an intermediary node between the client layer and cloud layer. The diagnosis for wellness and fitness monitoring could be transferred to the fog layer from the cloud layer. Such a paradigm leads to a reduction in latency at an increased throughput. This paper processes a fog-based deep learning model, DeepFog that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data. The three important abnormalities in wellness namely, (i) diabetes; (ii) hypertension attacks and (iii) stress type classification were chosen for experimental studies. We performed a detailed analysis of proposed models’ accuracy on standard datasets. The results validated the efficacy of the proposed system and architecture for accurate monitoring of these critical wellness and fitness criteria. We used standard datasets and open source software tools for our experiments.
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