scholarly journals Smart Mask – Wearable IoT Solution for Improved Protection and Personal Health

2021 ◽  
Author(s):  
Jarkko Hyysalo ◽  
Sandun Dasanayake ◽  
Jari Hannu ◽  
Christian Schuss ◽  
Mikko Rajanen ◽  
...  
Keyword(s):  
Large Scale ◽  
Personal Data ◽  
Health Data ◽  
Future Research ◽  
Personal Health ◽  
Public Data ◽  
Research Problems ◽  
Health Related ◽  
Treatment Plans ◽  
And Control

<div> <div> <div> <p>The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help gathering of health data and health-related event detection in real time from the user as well as from their environment. In larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions. </p> </div> </div> </div>

scholarly journals Smart Mask – Wearable IoT Solution for Improved Protection and Personal Health

2021 ◽  
Author(s):  
Jarkko Hyysalo ◽  
Sandun Dasanayake ◽  
Jari Hannu ◽  
Christian Schuss ◽  
Mikko Rajanen ◽  
...  
Keyword(s):  
Large Scale ◽  
Personal Data ◽  
Health Data ◽  
Future Research ◽  
Personal Health ◽  
Public Data ◽  
Research Problems ◽  
Health Related ◽  
Treatment Plans ◽  
And Control

<div> <div> <div> <p>The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help gathering of health data and health-related event detection in real time from the user as well as from their environment. In larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions. </p> </div> </div> </div>

scholarly journals The funhouse mirror: the I in personalised healthcare

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mira W. Vegter ◽  
Hub A. E. Zwart ◽  
Alain J. van Gool
Keyword(s):  
Precision Medicine ◽  
Personal Data ◽  
Health Data ◽  
Personal Health ◽  
Lifestyle Interventions ◽  
Phenotypic Data ◽  
Individual Health ◽  
Data Source ◽  
Tracking Devices ◽  
Personalised Healthcare

AbstractPrecision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to address the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a ‘data mirror’ that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided.

scholarly journals Motives of contributing personal data for health research:(Non-)participation in a Dutch biobank

2019 ◽  
Author(s):  
Reinder Broekstra ◽  
Els Maeckelberghe ◽  
Judith Aris-Meijer ◽  
Ronald Stolk ◽  
Sabine Otten
Keyword(s):  
Health Research ◽  
Large Scale ◽  
Behavioural Change ◽  
Personal Data ◽  
Population Based ◽  
Research Data ◽  
Current Data ◽  
Data Repositories ◽  
Regulatory Frameworks ◽  
Public Data

Abstract Background: Large-scale, centralized data repositories are playing a critical and unprecedented role in fostering innovative health research, leading to new opportunities as well as dilemmas for the medical sciences. Uncovering the reasons as to why citizens do or do not contribute to such repositories, for example, to population-based biobanks, is therefore crucial. We investigated and compared the views of existing participants and non-participants on contributing to large-scale, centralized health research data repositories with those of ex-participants regarding the decision to end their participation. This comparison could yield new insights into motives of participation and non-participation, in particular the behavioural change of withdrawal. Methods: We conducted 36 in-depth interviews with ex-participants, participants, and non-participants of a three-generation, population-based biobank in the Netherlands. The interviews focused on the respondents’ decision-making processes relating to their participation in a large-scale, centralized repository for health research data. Results: The decision of participants and non-participants to contribute to the biobank was motivated by a desire to help others. Whereas participants perceived only benefits relating to their participation and were unconcerned about potential risks, non­-participants and ex-participants raised concerns about the threat of large-scale, centralized public data repositories and public institutes, such as social exclusion or commercialization. Our analysis of ex-participants’ perceptions suggests that intrapersonal characteristics, such as levels of trust in society and public goods, participation conceived as a social norm, and basic societal values account for differences between participants and non-participants. Conclusions: Our findings indicate the fluidity of motives centring on helping others in decisions to participate in large-scale, centralized health research data repositories. Efforts to improve participation should focus on enhancing the trustworthiness of such data repositories and developing layered strategies for communication with participants and with the public. Accordingly, personalized approaches for recruiting participants and transmitting information along with appropriate regulatory frameworks are required, which have important implications for current data management and informed consent procedures.

scholarly journals Motives of contributing personal data for health research: (Non-)participation in a Dutch biobank

2020 ◽  
Author(s):  
Reinder Broekstra ◽  
Els Maeckelberghe ◽  
Judith Aris-Meijer ◽  
Ronald Stolk ◽  
Sabine Otten
Keyword(s):  
Health Research ◽  
Large Scale ◽  
Behavioural Change ◽  
Personal Data ◽  
Population Based ◽  
Research Data ◽  
Current Data ◽  
Data Repositories ◽  
Regulatory Frameworks ◽  
Public Data

Abstract Background: Large-scale, centralized data repositories are playing a critical and unprecedented role in fostering innovative health research, leading to new opportunities as well as dilemmas for the medical sciences. Uncovering the reasons as to why citizens do or do not contribute to such repositories, for example, to population-based biobanks, is therefore crucial. We investigated and compared the views of existing participants and non-participants on contributing to large-scale, centralized health research data repositories with those of ex-participants regarding the decision to end their participation. This comparison could yield new insights into motives of participation and non-participation, in particular the behavioural change of withdrawal. Methods: We conducted 36 in-depth interviews with ex-participants, participants, and non-participants of a three-generation, population-based biobank in the Netherlands. The interviews focused on the respondents’ decision-making processes relating to their participation in a large-scale, centralized repository for health research data. Results: The decision of participants and non-participants to contribute to the biobank was motivated by a desire to help others. Whereas participants perceived only benefits relating to their participation and were unconcerned about potential risks, non­-participants and ex-participants raised concerns about the threat of large-scale, centralized public data repositories and public institutes, such as social exclusion or commercialization. Our analysis of ex-participants’ perceptions suggests that intrapersonal characteristics, such as levels of trust in society and public goods, participation conceived as a social norm, and basic societal values account for differences between participants and non-participants.Conclusions: Our findings indicate the fluidity of motives centring on helping others in decisions to participate in large-scale, centralized health research data repositories. Efforts to improve participation should focus on enhancing the trustworthiness of such data repositories and developing layered strategies for communication with participants and with the public. Accordingly, personalized approaches for recruiting participants and transmitting information along with appropriate regulatory frameworks are required, which have important implications for current data management and informed consent procedures.

scholarly journals Towards Principles–Based Approaches to Governance of Health–Related Research Using Personal Data

2013 ◽  
Vol 4 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Graeme Laurie ◽  
Nayha Sethi
Keyword(s):  
Data Linkage ◽  
Good Governance ◽  
Personal Data ◽  
Health Data ◽  
Rule Based ◽  
Legal Reforms ◽  
Technological Advances ◽  
Information Governance ◽  
Health Related Research ◽  
Health Related

Technological advances in the quality, availability and linkage potential of health data for research make the need to develop robust and effective information governance mechanisms more pressing than ever before; they also lead us to question the utility of governance devices used hitherto such as consent and anonymisation. This article assesses and advocates a principles–based approach, contrasting this with traditional rule–based approaches, and proposes a model of principled proportionate governance. It is suggested that the approach not only serves as the basis for good governance in contemporary data linkage but also that it provides a platform to assess legal reforms such as the draft Data Protection Regulation.

scholarly journals A literature review of personalization transparency and control: Introducing the Transparency-Awareness-Control Framework

2021 ◽  
Vol 9 (4) ◽  
Author(s):  
Claire Segijn ◽  
Joanna Strycharz ◽  
Amy Riegelman ◽  
Cody Hennesy
Keyword(s):  
Literature Review ◽  
Academic Research ◽  
Reliability And Validity ◽  
Personal Data ◽  
Future Research ◽  
Control Paradigm ◽  
Control Framework ◽  
Definition Of ◽  
The Individual ◽  
And Control

<p>Through various online activities, individuals produce large amounts of data that are collected by companies for the purpose of providing users with personalized communication. In the light of this mass collection of personal data, the transparency and control paradigm for personalized communication has led to increased attention of legislators and academics. However, in the scientific literature no clear definition of personalization transparency and control exists, which could lead to reliability and validity issues, impeding knowledge accumulation in academic research. In a literature review, we analyzed 31 articles and we observed that 1) no clear definitions of personalization transparency or control exist, 2) they are used interchangeably in the literature, 3) collection, processing, and sharing of data are the three objects of transparency and control, and 4) increased transparency does not automatically increase control because first awareness needs to be raised in the individual. Also, the relationship between awareness and control depends on the ability and the desire to control. This study contributes to the field of algorithmic communication by creating a common understanding of the transparency and control paradigm and thus improves validity of the results. Further, it progresses research on the issue by synthesizing existing studies on the topic, presenting the Transparency-Awareness-Control framework, and formulating propositions to guide future research.</p>

scholarly journals Big Data Analytics & Artificial Intelligence in Healthcare

2021 ◽  
Author(s):  
PRANJAL KUMAR ◽  
Siddhartha Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Large Scale ◽  
Health Sector ◽  
Big Data Analytics ◽  
Personal Data ◽  
Related Data ◽  
Large Scale Data ◽  
Ethical Policy ◽  
Health Related

Abstract Big data analysis and Artificial Intelligence have received significant attention recently in creating more opportunities in the health sector for aggregating or collecting large-scale data. Today, our genomes and microbiomes can be sequenced i.e., all information exchanged between physicians and patients in Electronic Health Records (EHR) can be collected and traced at least theoretically. Social media and mobile devices today obviously provide many health-related data regarding activity, diets, social contacts, and so on. However, it is increasingly difficult to use this information to answer health questions and, in particular, because the data comes from various domains and lives in different infrastructures and of course it also is very variable quality. The massive collection and aggregation of personal data come with a number of ethical policy, methodological, technological challenges. It should be acknowledged that large-scale clinical evidence remains to confirm the promise of Big Data and Artificial Intelligence (AI) in health care. This paper explores the complexities of big data & artificial intelligence in healthcare as well as the benefits and prospects.

scholarly journals A systematic literature review of machine learning in online personal health data

2019 ◽  
Vol 26 (6) ◽  
pp. 561-576 ◽  
Author(s):  
Zhijun Yin ◽  
Lina M Sulieman ◽  
Bradley A Malin
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Future Research ◽  
Support Vector ◽  
Personal Health ◽  
Free Text ◽  
Clinical Settings ◽  
Gold Standard Dataset ◽  
Vector Machines ◽  
Health Related

Abstract Objective User-generated content (UGC) in online environments provides opportunities to learn an individual’s health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations. Materials and Methods We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review. Results We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support. Conclusions The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.

scholarly journals Data Control and Surveillance in the Global TB Response: A Human Rights Analysis

2020 ◽  
Vol 2 (1) ◽  
pp. 107-123
Author(s):  
Kat Albrecht ◽  
Brian Citro
Keyword(s):  
Human Rights ◽  
Personal Data ◽  
Health Data ◽  
Case Finding ◽  
Personal Health ◽  
Active Case Finding ◽  
Public And Private ◽  
Active Case ◽  
Data Control ◽  
Colonial Histories

The global response to the tuberculosis (TB) epidemic is generating copious amounts of personal health data. The emerging emphasis on the use of active case finding and digital adherence technologies in the TB response will increase the amount and expand the kind of data produced and used by public and private health officials. The production of personal data in high TB burden countries, in particular, must be considered in light of their colonial histories. In doing so, we argue that interventions to eliminate TB at global and national levels are ushering in a new era of data colonisation and surveillance in the name of public health. This, in turn, raises critical concerns for the human rights of people affected by TB, many of whom belong to vulnerable or marginalised groups. We examine the normative and legal content for a set of international human rights critical to the TB response, highlighting how each right implicates the production and use of personal health data. We also demonstrate that these rights are, by and large, enshrined in the constitutions of each high TB burden country. Finally, we use these rights to analyse active case finding and digital adherence technologies to pinpoint their unique data risks and the threats they pose to the human rights of people affected by TB.

scholarly journals Visualizing the Provenance of Personal Data using Comics

2017 ◽  
Author(s):  
Andreas Schreiber ◽  
Regina Struminski
Keyword(s):  
Personal Data ◽  
Cloud Service ◽  
Smartphone Application ◽  
Health Data ◽  
Personal Health ◽  
Data Provenance ◽  
Visualization Technique ◽  
Comic Strips

Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comics strips. Each strip of the comic represents a certain activity, such as entering data using a smartphone application, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to notice crucial points regarding their data such as, for example, privacy violations.

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