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dc.contributor.authorDeng, Zhikunen
dc.contributor.authorYang, Poen
dc.contributor.authorZhao, Youbingen
dc.contributor.authorZhao, Xiaen
dc.contributor.authorDong, Fengen
dc.date.accessioned2020-01-14T11:12:11Z
dc.date.available2020-01-14T11:12:11Z
dc.date.issued2015-12-28
dc.identifier.citationDeng Z, Yang P, Zhao Y, Zhao X, Dong F (2015) 'Life-logging data aggregation solution for interdisciplinary healthcare research and collaboration', IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing - Liverpool, Institute of Electrical and Electronics Engineers Inc..en
dc.identifier.isbn9781509001545
dc.identifier.doi10.1109/CIT/IUCC/DASC/PICOM.2015.342
dc.identifier.urihttp://hdl.handle.net/10547/623786
dc.description.abstractThe wide-spread use of wearable devices and mobile apps in the Internet of Things (IoT) environments makes effectively capture of life-logging personal health data come true. A long-term collection of these health data will benefit to interdisciplinary healthcare research and collaboration. But most wearable devices and mobile apps in the market focus on personal fitness plan and lack of compatibility and extensibility to each other. Existing IoT based platforms rarely achieve a successful heterogeneous life-logging data aggregation. Also, the demand on high security increases difficulties of designing reliable platform for integrating and managing multi-resource life-logging health data. This paper investigates the possibility of collecting and aggregating life-logging data with the use of wearable devices, mobile apps and social media. It compares existing personal health data collection solutions and identifies essential needs of designing a life-logging data aggregator in the IoT environments. An integrated data collection solution with high secure standard is proposed and deployed on a stateof-the-art interdisciplinary healthcare platform: MHA [15] by integrating five life-logging resources: Fitbit, Moves, Facbook, Twitter, etc. The preliminary experiment demonstrates that it successfully record, store and reuse the unified and structured personal health information in a long term, including activities, location, exercise, sleep, food, heat rate and mood.
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.relation.urlhttps://ieeexplore.ieee.org/document/7363388en
dc.subjectwearable computingen
dc.subjectInternet of Thingsen
dc.subjectinterdisciplinaryen
dc.subjectlife-logging dataen
dc.subjectB800 Medical Technologyen
dc.titleLife-logging data aggregation solution for interdisciplinary healthcare research and collaborationen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
dc.date.updated2020-01-14T11:07:43Z
html.description.abstractThe wide-spread use of wearable devices and mobile apps in the Internet of Things (IoT) environments makes effectively capture of life-logging personal health data come true. A long-term collection of these health data will benefit to interdisciplinary healthcare research and collaboration. But most wearable devices and mobile apps in the market focus on personal fitness plan and lack of compatibility and extensibility to each other. Existing IoT based platforms rarely achieve a successful heterogeneous life-logging data aggregation. Also, the demand on high security increases difficulties of designing reliable platform for integrating and managing multi-resource life-logging health data. This paper investigates the possibility of collecting and aggregating life-logging data with the use of wearable devices, mobile apps and social media. It compares existing personal health data collection solutions and identifies essential needs of designing a life-logging data aggregator in the IoT environments. An integrated data collection solution with high secure standard is proposed and deployed on a stateof-the-art interdisciplinary healthcare platform: MHA [15] by integrating five life-logging resources: Fitbit, Moves, Facbook, Twitter, etc. The preliminary experiment demonstrates that it successfully record, store and reuse the unified and structured personal health information in a long term, including activities, location, exercise, sleep, food, heat rate and mood.


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