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Sensors 2016, 16(7), 980; doi:10.3390/s16070980

On Curating Multimodal Sensory Data for Health and Wellness Platforms

1
Ubiquitous Computing Lab, Department of Computing Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, Seoul 446-701, Korea
2
Telemedicine Group, Center for Telematics and Information Technology, University of Twente, Enschede 7500AE, The Netherlands
3
Intelligent System Laboratory, Department of Computer Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, Seoul 446-701, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 4 February 2016 / Revised: 14 June 2016 / Accepted: 21 June 2016 / Published: 27 June 2016
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)

Abstract

In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user’s lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user’s sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user’s lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data. View Full-Text
Keywords: data curation; multimodal sensory data; data acquisition; lifelog; healthcare; wellness platform data curation; multimodal sensory data; data acquisition; lifelog; healthcare; wellness platform
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Amin, M.B.; Banos, O.; Khan, W.A.; Muhammad Bilal, H.S.; Gong, J.; Bui, D.-M.; Cho, S.H.; Hussain, S.; Ali, T.; Akhtar, U.; Chung, T.C.; Lee, S. On Curating Multimodal Sensory Data for Health and Wellness Platforms. Sensors 2016, 16, 980.

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