Empowerment of Patients with Hypertension through BPM, IoT and Remote Sensing

Ruiz-Fernández, Daniel; Marcos-Jorquera, Diego; Gilart-Iglesias, Virgilio; Vives-Boix, Víctor; Ramírez-Navarro, Javier

doi:10.3390/s17102273

Cited by 37 publications

(18 citation statements)

References 28 publications

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“…It also utilizes the concept of gamification to drive user engagement with immediate personalized feedback, reward systems to maximize user interaction, positive reinforcement, progress reports, medication adherence and weight control promoting ownership of care. In [52], the authors discussed the importance of wearable devices in patient empowerment to ensure participation and decision-making in his health journey. Models with top-down approaches have been discussed to help achieve the objectives of a peoplefriendly cost-effective system.…”

Section: Labelmentioning

confidence: 99%

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

Qureshi

Jeon

et al. 2021

Neural Comput & Applic

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According 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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Section: Labelmentioning

confidence: 99%

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

Qureshi

Jeon

et al. 2021

Neural Comput & Applic

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“…The configurator component we developed (see Fig. 4) applies process management technologies in a broader scope [58] as well as techniques known from model-driven development to empower healthcare experts to create flexible data collection instruments on their own. This paper only sketches the most important aspects of the configurator component, more details can be found in [10]:…”

Section: A Developing a Computation Independent Modelmentioning

confidence: 99%

Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications

et al. 2019

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The widespread dissemination of smart mobile devices enables new ways of collecting longitudinal data sets in a multitude of healthcare scenarios. On the one hand, mobile data collection can be accomplished more effectively and quicker compared with validated paper-based instruments. On the other hand, it can increase the data quality significantly and enable data collection in scenarios not covered by existing approaches so far. Previous attempts to utilize smart mobile devices for collecting data in these scenarios, however, often struggle with high costs for developing and maintaining mobile applications, which need to run on a multitude of mobile operating systems. Therefore, in the QuestionSys project, we are developing a generic (i.e., platform-independent) framework for enabling mobile data collection and sensor data integration in healthcare scenarios. The latter, in turn, is addressed by a model-driven approach, which is shown this paper along with the core components of the QuestionSys framework. In particular, it is shown how healthcare experts are empowered to create mobile data collection and sensing applications on their own and with reasonable efforts.INDEX TERMS End-user programming, mobile data collection, model-driven development.

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“…resource awareness, interoperability). The link between BPM and IoT is also widely used in health-care sectors, and it is seen as a means to assist patient with any chronic diseases such as hypertension [16]. This synergy between BPM and IoT is used to identify weaknesses in the interdisciplinary health-care processes in order to redesign those processes in a way that ensure a real-time and direct interaction between the patient and the medical staff.…”

Section: Related Workmentioning

confidence: 99%

Towards Smart Incident Management Under Human Resource Constraints for an IoT-BPM Hybrid Architecture

Ismaili-Alaoui

Bäına

Benali

et al. 2018

Web Services – ICWS 2018

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The Internet of Things (IoT) is exploding, and this new technology affects all the layers in any enterprise architecture, from infrastructure to business. To survive this new evolution and make the most out of this paradigm shift, a communication channel must be created between Business Process Management (BPM) domain and IoT domain in order to bridge the gap between the business layer and the IoT physical layer. The allocation of business process resources to IoT events is an important step towards an end-to-end IoT-BPM integration approach to assist organizations in their scheduling and incident management journey. In this paper, we propose a combination approach which is based on i)unsupervised machine learning algorithms to generate clusters of priorities, used to estimate incoming events priority, and to ensure a learning feedback loop that feeds forward insight to continuously adjust decisions made at each layer, and ii) genetic algorithm (GA) to guarantee the assignment of the most critical IoT generated event to the qualified human resource while respecting several constraints such as resource availability and reliability, and taking into consideration the priority of each event that launch process instances. A case study is presented and the obtained results from our experimentations demonstrate the benefit of our approach and allowed us to confirm the efficiency of our assumptions.

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Empowerment of Patients with Hypertension through BPM, IoT and Remote Sensing

Cited by 37 publications

References 28 publications

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications

Towards Smart Incident Management Under Human Resource Constraints for an IoT-BPM Hybrid Architecture

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