Paper Info
Title
A Context-Aware Mhealth System For Online Physiological Monitoring In Remote Healthcare
Abstract
Physiological or biological stress is an organism's response to a stressor such as an environmental condition or a stimulus. The identification of physiological stress while performing the activities of daily living is an important field of health research in preventive medicine. Activities initiate a dynamic physiological response that can be used as an indicator of the overall health status. This is especially relevant to high risk groups; the assessment of the physical state of patients with cardiovascular diseases in daily activities is still very difficult. This paper presents a context-aware telemonitoring platform, IPM-mHealth, that receives vital parameters from multiple sensors for online, real-time analysis. IPM-mHealth provides the technical basis for effectively evaluating patients' physiological conditions, whether inpatient or at home, through the relevance between physical function and daily activities. The two core modules in the platform include: 1) online activity recognition algorithms based on 3-axis acceleration sensors and 2) a knowledge-based, conditional-reasoning decision module which uses context information to improve the accuracy of determining the occurrence of a potentially dangerous abnormal heart rate. Finally, we present relevant experiments to collect cardiac information and upper-body acceleration data from the human subjects. The test results show that this platform has enormous potential for use in long-term health observation, and can help us define an optimal patient activity profile through the automatic activity analysis.
Year
DOI
Venue
2016
10.15837/ijccc.2016.1.1333
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
Keywords
Field
DocType
decision support systems, telemonitoring, Context-Aware application
Data mining,Computer science,mHealth,Human–computer interaction,Artificial intelligence,Stressor,Health care,Activities of daily living,Activity recognition,Physiological monitoring,Decision support system,Preventive healthcare,Machine learning
Journal
Volume
Issue
ISSN
11
1
1841-9836
Citations 
PageRank 
References 
10
0.91
14
Authors
3
Name
Order
Citations
PageRank
Weiping Zhang1498.30
Kerstin Thurow211026.83
R. Stoll331825.58