Consumer Health Care: Current Trends in Consumer Health Monitoring

Garge, Gopi Krishna S; Balakrishna, Chitra; Datta, Soumya Kanti

doi:10.1109/mce.2017.2743238

Cited by 54 publications

(30 citation statements)

References 21 publications

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“…The use of non-medical devices to maintain or restore health [4], [5], reinforces the importance of patient-driven health care models [6] and consumer health care models [7] that include mobile health (mHealth) applications to track lifestyle habits, wrist-band trackers, or other kind of IoT/wearable devices. There is plethora of scientific peerreview pervasive computing systems for health care [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Estrada-Galiñanes¹,

Wac²

2018

2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

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Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.

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Section: Introductionmentioning

confidence: 99%

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Estrada-Galiñanes¹,

Wac²

2018

2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

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“…At the same time, the use of non-medical devices to maintain or restore health [7,8], reinforces the importance of patient-driven health care models [9] and consumer health care models [10] that include mobile health (mHealth) applications to track lifestyle habits, wrist-band trackers, or other kind of Internet of Things (IoT)/wearable devices. There is a plethora of scientific peer-review pervasive computing systems for health care [11][12][13][14].…”

Section: Introductionmentioning

confidence: 93%

“…For instance, the Health app 8 from Apple is a hub that integrates data from multiple devices and offers limited functionality for accessing electronic health record databases from partner providers. Researchers can make use of open development resources, e.g., Apple ResearchKit 9 and Google Fit SDK, 10 to create data connectors to access EHR and TechO+ data from study participants. Hubs are single-points of data integration that include multiple aspects of life and facilitate data exploration.…”

Section: Technological Issuesmentioning

confidence: 99%

Collecting, exploring and sharing personal data: Why, how and where

Estrada-Galiñanes

Wac

2020

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New, multi-channel personal data sources (like heart rate, sleep patterns, travel patterns, or social activities) are enabled by ever increased availability of miniaturised technologies embedded within smartphones and wearables. These data sources enable personal self-management of lifestyle choices (e.g., exercise, move to a bike-friendly area) and, on a large scale, scientific discoveries to improve health and quality of life. However, there are no simple and reliable ways for individuals to securely collect, explore and share these sources. Additionally, much data is also wasted, especially when the technology provider ceases to exist, leaving the users without any opportunity to retrieve own datasets from "dead" devices or systems. Our research reveals evidence of what we term human data bleeding and offers guidance on how to address current issues by reasoning upon five core aspects, namely technological, financial, legal, institutional and cultural factors. To this end, we present preliminary specifications of an open platform for personal data storage and quality of life research. The Open Health Archive (OHA) is a platform that would support individual, community and societal needs by facilitating collecting, exploring and sharing personal health and QoL data.

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“…The impact is not restricted to reputation, financial loss, and customer dissatisfaction but can affect patient safety making this a safety-critical issue. Finally, authentication and identity management of the devices to ensure encryption of data in transit is an existing challenge [2].…”

Section: Figure 2: Generic Iot Data Security Healthcare Architecturementioning

confidence: 99%

Challenges of Wearable Health Monitors : A Case study of Foetal ECG Monitor

Balakrishna

Rendon-Morales

Aviles-Espinosa

et al. 2019

2019 Global IoT Summit (GIoTS)

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IoT-based wearable health monitoring devices promise multiple benefits such as remote diagnosis, early-warnings, continuous home-care monitoring etc. These technologies will potentially impact people's quality-of-life while reducing costs to healthcare service providers. The challenges for the development of these technologies are manifold, particularly for monitoring bio-signals. In this paper, we discuss the challenges associated with wearable health monitors through a case study of a fetal ECG wearable garment. This is focused on three main areas: a) sensor design and signal conditioning, b) data processing, and communications and c) printed sensors integrated into IoT garments. We discuss in detail the current design challenges to be tackled for achieving a precise signal retrieval.

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Consumer Health Care: Current Trends in Consumer Health Monitoring

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Cited by 54 publications

References 21 publications

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Collecting, exploring and sharing personal data: Why, how and where

Challenges of Wearable Health Monitors : A Case study of Foetal ECG Monitor

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