Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Life-Logging Data Aggregation Solution for Interdisciplinary Healthcare Research and Collaboration

Yang, P, Deng, ZK, Zhao, YB, Dong, F and Zhao, X (2015) Life-Logging Data Aggregation Solution for Interdisciplinary Healthcare Research and Collaboration. In: Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on . pp. 2315-2320. (The 13th IEEE International Conference on Pervasive Intelligence and Computing, 26th - 28th October 2015, Liverpool).

IoTBDH-2015_paper_6-2.pdf - Accepted Version

Download (320kB) | Preview


The 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 state-of-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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 10 Mar 2016 11:08
Last Modified: 13 Apr 2022 15:14
URI: https://researchonline.ljmu.ac.uk/id/eprint/3146
View Item View Item