On Curating Multimodal Sensory Data for Health and Wellness Platforms

On Curating Multimodal Sensory Data for Health and Wellness Platforms

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 activit...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 16; no. 7; p. 980
Main Authors: Amin, Muhammad Bilal, Banos, Oresti, Khan, Wajahat Ali, Muhammad Bilal, Hafiz Syed, Gong, Jinhyuk, Bui, Dinh-Mao, Cho, Soung Ho, Hussain, Shujaat, Ali, Taqdir, Akhtar, Usman, Chung, Tae Choong, Lee, Sungyoung
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01-07-2016
MDPI
Subjects:
Anomalies
Big Data
Cloud computing
data acquisition
data curation
Data Mining
Data sources
Decisions
Devices
Health Promotion
healthcare
Humans
lifelog
Monitoring, Physiologic
multimodal sensory data
Platforms
Real time
Recommendations
Sensors
Smartphones
Smartwatches
Time Factors
Wearable computers
wellness platform
lifelog
data acquisition
wellness platform
multimodal sensory data
data curation
healthcare
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s16070980