Ubiquitous Healthcare System for Analysis of Chronic Patients’ Biological and Lifelog Data

Kim, Seongjung; Yeom, Seongkyu; Kwon, Oh-Jin; Shin, Dongkyoo

doi:10.1109/access.2018.2805304

Cited by 23 publications

(10 citation statements)

References 13 publications

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“…The current era has witnessed revolutionary advancements in different fields including eye tracking thanks to the hardware and software advances. These progresses arise the idea of ubiquitous healthcare, in which portable devices play key roles in the acquisition of biosignals [259], which can be beneficial for active aging and to prevent age-related defects. The internet of medical (or health) things [260][261][262][263][264], quantum computing [265][266][267] and the fifth-generation cellular network technology in healthcare [268,269] are the areas, where eye tracking can be thought of as an important modality for active aging.…”

Section: Future Perspectivementioning

confidence: 99%

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Marandi

Gazerani

2019

Future Neurol.

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Recent applications of eye tracking for diagnosis, prognosis and follow-up of therapy in age-related neurological or psychological deficits have been reviewed. The review is focused on active aging, neurodegeneration and cognitive impairments. The potential impacts and current limitations of using characterizing features of eye movements and pupillary responses (oculometrics) as objective biomarkers in the context of aging are discussed. A closer look into the findings, especially with respect to cognitive impairments, suggests that eye tracking is an invaluable technique to study hidden aspects of aging that have not been revealed using any other noninvasive tool. Future research should involve a wider variety of oculometrics, in addition to saccadic metrics and pupillary responses, including nonlinear and combinatorial features as well as blink- and fixation-related metrics to develop biomarkers to trace age-related irregularities associated with cognitive and neural deficits.

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Section: Future Perspectivementioning

confidence: 99%

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Marandi

Gazerani

2019

Future Neurol.

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“…Five of the articles were authored by medical doctors (Balaguera et al, ; Bruyneel, Libert, & Ninane, ; Lewis, ; Weenk et al, ; Wirz, Conrad, Shtrichman, Schimo, & Hoffmann, ), three by nurse scientists (Hall & Clark, ; Hand et al, ; Hilbe, Schulc, Linder, & Them, ), ten by engineers (Charlon, Fourty, Bourennane, & Campo, ; Faudzi, Nasir, Fadzil, Mukri, & Satar, ; Iwamura, Yamaguchi, Tanaka, & Fujishima, ; Kim, Yeom, Kwon, Shin, & Shin, ; Kuroda et al, ; Liu, Huang, et al, ; Liu, Hsu, & Chang, ; Otero, Apalkov, Fernandez, & Armada, ; Wai et al, ; Wolf, Hetzer, zu Schwabedissen, Wiese, & Marschollek, ) and one by expert in ergonomics (Nodooshan, Choobineh, Razeghi, & Khales, ). Included articles were mainly published in technology‐oriented journals and authored by technological experts.…”

Section: Resultsmentioning

confidence: 99%

“…Four of the included articles concerned devices developed for patient monitoring. These devices were focused on monitoring vital signs (Kim et al, ; Kuroda et al, ; Weenk et al, ) and respiratory function (Liu, Huang, et al, ). Three devices focused on pressure ulcer prevention (Faudzi et al, ; Hall & Clark, ; Hand et al, ).…”

Section: Resultsmentioning

confidence: 99%

Health and medical device development for fundamental care: Scoping review

Matinolli

Mieronkoski

Salanterä

2019

Journal of Clinical Nursing

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Background: The use of technology and health and medical devices as a part of fundamental nursing care is increasing. Although involving users in the device development process is essential, the role of nurses in the process has not yet been discussed. Objectives: To examine and map what kind of health and medical devices have been developed specifically for fundamental nursing care and to examine the design and development of the devices, particularly focusing on the role of nurses in the process. Design: Scoping review. Review methods: The databases were searched to identify studies describing health and medical devices developed for fundamental nursing care published between the years 2008-2018 in English language. References of included articles were reviewed for additional eligible studies. Two research team members screened the abstracts and full articles against the predefined inclusion and exclusion criteria. The PRISMA-ScR checklist was used.Results: Of the 7223 reports identified, a total of 19 were chosen for the scoping review. Of these, five were further analysed regarding the development process. Main focus areas of the included reports were patient monitoring, pressure ulcer prevention and patient transfer and mobility. Device development process, divided into three phases, was mainly driven by technological expertise and healthcare personnel were mainly involved in the evaluation phases.Conclusions: Health and medical devices are a crucial part of the healthcare today and nurses are increasingly involved with their use. Most of the devices have been developed mainly by using technological expertise although they are directly aimed at fundamental aspects of nursing care. The results of our review suggest that the expertise of the nurses as the end-users of the devices could be much more exploited. Relevance to clinical practice:A combination of expertise of device development from both nursing professionals and technical experts is necessary to disentangle the requirements of increased quality in nursing care combined with the ever-growing technological requirements. K E Y W O R D S delivery of health care, health personnel, medical device, nursing, patient-centered nursing, technology development | 1823 MATINOLLI eT AL.

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“…have been analyzed for this purpose. In another research [15][16] a healthcare system for critical patients and this system is to be operated by medical attendant/nurse anywhere. System has a smart phone application developed to operate with bio -medical sensors for acquiring the required data, and dedicated health server.…”

Section: Related Workmentioning

confidence: 99%

“…The data received from these sensors (which are mostly bio-medical sensors) is stored in cloud for analysis and monitoring done by medical professionals or attendants. Integrating the IoT structures into medical devices advances the quality and service of care for aged patients and also for children [7]. The on-body non-invasive sensors monitors the physiological activities of human body and the information is stored and analysed for the realization, detection and potential treatment of diseases.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework

Nawaz

Ahmed

2021

Preprint

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Physiological signals retrieve the information from sensors implanted or attached to the human body. These signals are vital data sources that can assist in predicting the disease well before time and thus proper treatment can be made possible. With the addition of Internet of Things in healthcare, real-time data collection and pre-processing for signal analysis has reduced burden of in-person appointments and decision making on healthcare. Recently, Deep learning-based algorithms have been implemented by researchers for recognition, realization and prediction of diseases by extracting and analyzing the important features. In this research real-time 1-D timeseries data of on-body non-invasive bio-medical sensors have been acquired and pre-processed and analyzed for anomaly detection. Feature engineered parameters of large and diverse dataset have been used to train the data to make the anomaly detection system more reliable. For comprehensive real-time monitoring the implemented system uses wavelet time scattering features for classification and deep learning based autoencoder for anomaly detection of time series signals for assisting the clinical diagnosis of cardiovascular and muscular activity. In this research, an implementation of IoT based healthcare system using bio-medical sensors has been presented. This paper also aims to provide the analysis of cloud data acquired through bio-medical sensors using signal analysis techniques for anomaly detection and timeseries classification has been done for the disease prognosis in real-time. Wavelet time scattering based signals classification accuracy of 99.88% is achieved. In real time signals anomaly detection, 98% accuracy is achieved. The average Mean Absolute Error loss of 0.0072 for normal signals and 0.078 is achieved for anomaly signals.

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Ubiquitous Healthcare System for Analysis of Chronic Patients’ Biological and Lifelog Data

Cited by 23 publications

References 13 publications

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Health and medical device development for fundamental care: Scoping review

Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework

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