Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods

Woensel, William Van; Roy, Patrice; Abidi, Samina Raza

doi:10.1016/j.artmed.2020.101931

Cited by 18 publications

(15 citation statements)

References 27 publications

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“…In Reference [9], the distortion of the emitter modulator and the nonlinear characteristics of the power amplifier are introduced into the classifier construction model, and the test results show that the spectrum distortion of the emitter signal exists. In Reference [10], the transient sparse feature of signal is used as the basis of evaluation mode to realize the identification of emitter signal feature. In Reference [11], the relevant features are extracted by analyzing the timefrequency characteristics of the emitter signal to realize the identification of transient characteristics.…”

Section: Related Workmentioning

confidence: 99%

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

Chen

Liu

Fan

2021

Advances in Mathematical Physics

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In order to solve the problem of large signal acquisition error caused by radio wave multipath effect in indoor environment, firstly, the signal source carried on the motion platform is collected for spectrum signal, and the signal processed by wavelet threshold denoising algorithms extracted and stored for spectrum feature extraction. Then, after data training and identification, the signal source is input into the system in random mode for identification. The experimental results show that the improved fuzzy clustering algorithm (FCA) is 12.7% higher than the spectrum envelope extraction method (SEEM) in the recognition rate of spectrum characteristics of different modes of signal source.

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Section: Related Workmentioning

confidence: 99%

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

Chen

Liu

Fan

2021

Advances in Mathematical Physics

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“…The articles in learning-based reasoning category deal with the support of ontological reasoning via machine learning techniques (Hohenecker and Lukasiewicz, 2020;Makni and Hendler, 2019;Woensel et al, 2020;Bock et al, 2012;Pan et al, 2018;Mehri et al, 2021;Chung et al, 2020). Indeed, this is an important bottleneck in the use of ontologies : logical deduction reasoning often involves extremely slow processing times when the ontology is used in a real case.…”

Section: Learning-based Reasoningmentioning

confidence: 99%

Mixed artificial reasoning, closer to human?

Ghidalia

Narsis

Bertaux

et al. 2022

Preprint

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AI is now used in many areas of our daily life, however we still have difficulties to make machines interact with our world as we perceive it. Humans mainly use two types of reasoning to grasp reality: inductive and deductive reasoning. In AI, inductive reasoning is possible thanks to the use of machine learning models, while deductive reasoning can be provided by the use of ontologies. Based on these statements, we have conducted a review of articles combining the use of machine learning and ontologies in order to identify the most recent techniques that combine induction and deduction within an AI. This systematic literature review allowed us, after reading 128 studies, to identify three main groups of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining and learning and reasoning system. The combination of these two types of reasoning allows machines to be better informed about the specificity of our world.

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“…For instance, ML was applied to estimate the indoor location of patients for properly applying the virtual care of patients [23]. Nevertheless, in this work, ML was not applied to directly obtain customized self-care programs based on the information of patients.…”

Section: Inmentioning

confidence: 99%

Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

Kumar

García‐Magariño

Nasralla

et al. 2021

Mobile Information Systems

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E-health sustainable systems can be optimized by empowering patients in self-care programs through artificial intelligence ecosystems in which both doctors and patients interact in an agile way. This work proposes agent-based simulators as a mechanism for predicting the repercussions of certain self-care programs in certain patients for finding the most appropriate ones. In order to make this easy for both doctors and patients, mobile agents are used to configure an app for each patient, and this app provides the resources to each self-care program. Mobile agents include a machine-learning module for learning which programs are the most appropriate for each patient. This approach is illustrated with two agent-based simulators for respectively reducing negative emotions such as depression and controlling heart rate variability extreme values related to stress. The resulting app was evaluated with a group of users with the Usefulness, Satisfaction and Ease of use (USE) scale and obtained 73% in usefulness, 77% in satisfaction, and 68% in ease of use. This trial is registered with According to the recommendations of the International Committee of Medical Journal Editors (ICMJE), this manuscript states that all experiments have been approved with the ethical committee CEICA from Community of Aragon (Spain) with registration number C.I.PI18/099.

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Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods

Cited by 18 publications

References 27 publications

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

Mixed artificial reasoning, closer to human?

Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

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