Healthcare 4.0: recent advancements and futuristic research directions

Gupta, Aditya; Singh, Amritpal

doi:10.1007/s11277-022-10164-8

Cited by 28 publications

(10 citation statements)

References 63 publications

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“…[ 38–41 ] Based on this model, a controller can be developed to achieve more accurate IPMC control. [ 33–35,37–41 ]…”

Section: Nonphysical Identification Modelsmentioning

confidence: 99%

“…The classical identification model reveals the physical characteristics of the IPMC actuator to a low degree, and the output displacement and input voltage are the physical quantities directly modeled. These models can predict the displacement of IPMC actuator well, [ 33–41 ] but only a few models can characterize the hysteresis or creep of IPMC. [ 38–41 ] Based on this model, a controller can be developed to achieve more accurate IPMC control.…”

Section: Nonphysical Identification Modelsmentioning

confidence: 99%

“…Yun et al [ 34 ] used the Box–Jenkins (BJ) method to identify the dynamic behavior of IPMC actuators, handled the inherent nonlinear behavior of IPMCs under the linear model with skill, and provided a system identification method to enhance the accuracy and reliability of IPMC actuators and sensors. Notably, the identification model derived from simple experimental identification is extensively used in developing IPMC controllers, [ 35 ] but not all controls are based on the model. [ 36 ] Open‐loop control typically relies on the precise model of IPMCs.…”

Section: Nonphysical Identification Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Application‐Oriented Modeling of Soft Actuator Ionic Polymer–Metal Composites: A Review

Jiang,

Lin,

et al. 2023

Advanced Intelligent Systems

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Compared to conventional actuators, the soft ionic polymer–metal composite (IPMC) actuator has significant advantages in specific applications, and the mathematical model of IPMC actuators is essential to comprehending and applying IPMCs. Due to the inherent characteristics of IPMCs and the impact of the manufacturing and measurement processes, it is challenging to developa reliable model. This article provides a comprehensive overview of the developments in IPMC actuator modeling. In particular, three types of models are examined and contrasted: the nonphysical identification model, the partial‐physical model, and the physical‐based model. In order to comprehend the current state of numerous IPMC actuator models, the characteristics, evolution, and functions of each type of model are discussed. Afterward, the evolution of the IPMC actuators’ applications is discussed. Finally, promising research directions for IPMC actuator models are identified that can more effectively facilitate the development of IPMC‐based devices.

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“…[ 38–41 ] Based on this model, a controller can be developed to achieve more accurate IPMC control. [ 33–35,37–41 ]…”

Section: Nonphysical Identification Modelsmentioning

confidence: 99%

Section: Nonphysical Identification Modelsmentioning

confidence: 99%

Section: Nonphysical Identification Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Application‐Oriented Modeling of Soft Actuator Ionic Polymer–Metal Composites: A Review

Jiang,

Lin,

et al. 2023

Advanced Intelligent Systems

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“…Regarding maintenance system procedures, we recommend, as a minimum, the following procedures:  Internal quality control of medical equipment,  Maintenance of medical equipment,  Management of spare parts storage [27] This is especially true for the health domain, where the Internet of Things, Cloud (and Fog) Computing, and Big Data technologies are revolutionizing eHealth and its whole ecosystem, moving it towards Healthcare 4.0 [30,31].…”

Section: Information System Specificationsmentioning

confidence: 99%

Specifications of the modern information system for maintenance of medical equipment in healthcare

Polak,

Jakovljević,

Marković

et al. 2023

Tehnika

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Effective management of the maintenance of medical equipment (ME), especially high-tech one, is one of the main issues for the quality of health care, for the provision of cost-effective health services and for saving , even in developed countries, insufficient resources. Medical equipment maintenance (EM) includes all activities related to ensuring an adequate level of service and limiting the downtime of medical equipment in a healthcare organization. Traditionally, EM is categorized as preventive maintenance (PM) and corrective maintenance (CM). The principle of PM is prediction. PM can be planned and predictive, ie. Just-In-Time (JIT) maintenance. Predictive maintenance can only be applied to new high-tech medical devices that have built-in self-testing. Planned preventive maintenance is carried out in accordance with the maintenance plan for a certain period of time. As a rule, when creating the plan, we follow the manufacturer's recommendations for preventive maintenance of ME given in the technical documentation of the ME, especially in the instructions for the maintenance of the ME. Predictive or JIT maintenance makes it possible to detect and solve the problem before problems and contingencies actually occur, and this is done on the basis of intelligent monitoring and analysis of the state of ME at the right moment, which ensures the optimal time for intervention, so that maintenance performed only when necessary. The information system for the management of the maintenance of ME in healthcare institutions would interconnect healthcare institutions and enable the fast and safe exchange of data and information related to the maintenance of ME and enable the optimal maintenance of ME in healthcare institutions, increasing the availability of this equipment, especially high-tech equipment, with significant reduction of the total cost of maintaining ME (of the order of 30%). One of the main reasons for the unsustainability of the existing approach to the maintenance of ME in the healthcare institutions of Serbia is that each healthcare institution independently takes care of the maintenance of its own ME. This approach is expensive and significantly affects high maintenance costs. This approach needs to change.

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“…Modern healthcare is currently poised for a revolution in the form of Healthcare 4.0 [1], [2], [3], [4], which represents a transformative approach to advancing medical innovations, and thus patient care, by leveraging the latest advances in information processing systems. This approach promises to deliver real-time personalized healthcare to patients, physicians, and caregivers by shifting from hospital-centric to a patient-centered model.…”

Section: Introductionmentioning

confidence: 99%

Advanced Cardiovascular Health in a Quantum AI-driven Healthcare Framework

Tripathi,

Upadhyay,

Soni

2024

Preprint

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With the advent of Healthcare 4.0, there is increased interest from researchers the world over in the application of modern, cutting-edge Artificial Intelligence (AI) and Quantum Artificial Intelligence (QAI) algorithms in solving healthcare challenges. The era of Quantum Computing (QC) promises to bring significant advancements in several areas of healthcare such that it may be sensible to give this hybrid Quantum/Classical paradigm its own name -Healthcare4Q. The potential of QC will extend the reach of Healthcare4Q with the help of diverse technologies such as quantum-enabled wearables, quantum-secure transfer and storage of data, and quantum computing at edge, fog, and cloud. All of these technologies promise to catapult Healthcare4Q to become the most capable healthcare framework in the advancement of medical innovations and improvement of patient care.An integral part of a person's health lies in cardiovascular health, and thus prioritizing and optimizing cardiovascular health remains vital to the broader goals of public health and healthcare sustainability. In this study, under the paradigm of Healthcare4Q, we propose a framework called the Quantum AIdriven Heart Health Framework (QAIHHF) that can provide advanced predictive intelligence to healthcare providers by utilizing historical and real-time data and processing capabilities proposed in Healthcare4Q. We show that when applied to various diagnostics and health indicators such as ECG data, the Quantum AI provides accuracy at a level equal to or higher as compared to the classical methods thus proving itself to be the critical component that will herald the era of Healthcare4Q.

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Healthcare 4.0: recent advancements and futuristic research directions

Cited by 28 publications

References 63 publications

Application‐Oriented Modeling of Soft Actuator Ionic Polymer–Metal Composites: A Review

Application‐Oriented Modeling of Soft Actuator Ionic Polymer–Metal Composites: A Review

Specifications of the modern information system for maintenance of medical equipment in healthcare

Advanced Cardiovascular Health in a Quantum AI-driven Healthcare Framework

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