Guest Editorial Sensor Informatics and Quantified Self

Picard, Rosalind W.; Wolf, Gary

doi:10.1109/jbhi.2015.2462372

Cited by 10 publications

(3 citation statements)

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“…In our view, the technological development of a sensing device is not part of medical informatics but the integration of such devices in medical information systems and their application in research, clinical trials, and medical care. Unobtrusive health monitoring in private spaces such as the car or the home is based on various sensors and is expeditiously growing in research and applications [ 1 2 3 ]. Witte et al see “ signal informatics ” as an advanced integrative concept in the framework of medical informatics [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the sensor's part could be seen as more device-oriented. Picard & Wolf define "sensor informatics" as new technologies and applications for medical services incorporating wearable sensors, signal processing, machine learning, and data mining techniques [1]. In our view, the technological development of a sensing device is not part of medical informatics but the integration of such devices in medical information systems papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion.…”

Section: Introductionmentioning

confidence: 99%

“…and their application in research, clinical trials, and medical care. Unobtrusive health monitoring in private spaces such as the car or the home is based on various sensors and is expeditiously growing in research and applications [1][2][3]. Witte et al see "signal informatics" as an advanced integrative concept in the framework of medical informatics [4].…”

Section: Introductionmentioning

confidence: 99%

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Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2021

Yearb Med Inform

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Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2021

Yearb Med Inform

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Wearable Devices for Health‐Related Data

Robbins

DiClemente

Duncan

et al. 2020

The International Encyclopedia of Media Psychology

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Our technological landscape is changing rapidly. These technologies have the potential to serve a variety of functions relating to individual and population‐level health, such as the ability for patients with chronic conditions to connect and share their experiences or for health care providers to reach underserved or hard to reach populations with telemedicine and other methods. There is also the opportunity for health campaign messages to reach a larger audience than ever before. This entry articulates a framework for understanding the research and industry developments pertaining to mobile technology as it relates to population health at the patient, provider, and population levels. In so doing, it discusses the potential advantages technology offers, but also address the potential pitfalls or perils imposed by technology. It concludes by articulating an agenda for future researchers interested in mobile technology and population health outcomes.

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Introduction

Przegalińska¹

2018

Wearable Technologies in Organizations

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Guest Editorial Sensor Informatics and Quantified Self

Cited by 10 publications

References 0 publications

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Wearable Devices for Health‐Related Data

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