Loading [a11y]/accessibility-menu.js
Capturing and Analyzing Pervasive Data for SmartHealth | IEEE Conference Publication | IEEE Xplore

Capturing and Analyzing Pervasive Data for SmartHealth


Abstract:

In this paper, we study how mobile computing and wireless technologies can be explored to provide effective ubiquitous healthcare services. Instead of reinventing the whe...Show More

Abstract:

In this paper, we study how mobile computing and wireless technologies can be explored to provide effective ubiquitous healthcare services. Instead of reinventing the wheels, we make use of smartphones, off-the-shelf components, and existing technologies in ubiquitous computing (i.e. wireless and mobile positioning technologies, and data acquisition techniques and processing via sensors) to develop a middleware, and tools for the development of systems and applications to provide effective ubiquitous healthcare services. Two main tasks to be studied are: 1) Developing a framework, called SmartHealth, to provide the infrastructure and architectural support for realizing ubiquitous healthcare services, and 2) Designing and developing ubiquitous healthcare applications by utilizing the SmartHealth framework to let users experience and benefit from the provided services. We use scenarios to illustrate how mobile/wireless and sensor technologies can enable ubiquitous healthcare services in Smart Health. Some of the examples included in Smart Health are: location tracking, vital signs and well-being data acquisition and analysis, fall detection and behavior monitoring, and sleep analysis. As a start, based on the Smart Health framework, we introduce a smartphone app, called Smart Mood, for tracking the mood of patients who are suffering mood disorder (i.e., manic and depression) to demonstrate how Smart Health can effectively enable ubiquitous healthcare services.
Date of Conference: 13-16 May 2014
Date Added to IEEE Xplore: 19 June 2014
ISBN Information:

ISSN Information:

Conference Location: Victoria, BC, Canada
References is not available for this document.

I. Introduction

We are entering a new era of computing - the era of ubiquitous/pervasive computing where we can retrieve any data from any devices through any types of networks at any time anyplace. These new technologies will bring us a step closer to a more secure, convenient, and better quality of living towards a Ubiquitous Intelligent Community. For example, to materialize the benefits of ubiquitous computing, under the Government's Digital 21 Strategy, it is proposed to upgrade Hong Kong to be a smarter city to realize the slogan “Smarter Hong Kong, Smarter Living”. To provide the infrastructure for ubiquitous computing, the government is enhancing the existing wireless network infrastructure for connecting various ubiquitous computing devices and applications. However, other than the communication infrastructure, it is still lack of any efficient system framework for the development of ubiquitous computing applications.

Select All
1.
L.-S. Low, M. Maddage, M. Lech, L. Sheeber, and N. Allen, "Detection of clinical depression in adolescents speech during family interactions," IEEE Transactions on Biomedical Engineering, Vol. 58, no. 3, pp. 574-586, 2011.
2.
C. Seeger, A. Buchmann, and K. Van Laerhoven, "My health assistant: a phone-based body sensor network that captures the wearers exercises throughout the day," in International Conference on Body Area Networks, 2011, pp. 1-7.
3.
R. F. Dickerson, E. I. Gorlin, and J. A. Stankovic, "Empath: a continuous remote emotional health monitoring system for depressive illness," in ACM Conference on Wireless Health, 2011.
4.
Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association, 2013.
5.
A. K. Dey and D. Estrin, "Perspectives on pervasive health from some of the fields leading researchers," IEEE Pervasive Computing, Vol. 10, no. 2, pp. 4-7, 2011.
6.
C. M. Madan A. and P. A., "Social sensing to model epidemiological behavior change," in Proceedings of ACM Ubicomp, 2010, pp. 291-300.
7.
L. D. Madan A., Moturu S. and P. A., "Social sensing: obesity, unhealthy eating and exercise in face-to-face networks," in ACM Conference on Wireless Health, 2010, pp. 104-110.
8.
S. T. Moturu, I. Khayal, N. Aharony, W. Pan, and A. Pentland, "Sleep, mood and sociability in a healthy population," in International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 5267-5270.
9.
S. Intille and W. Haskell, "Wockets: Open source accelerometers for phones." [Online], Available: http://web.mit.edu/wockets
10.
K. Patrick, "Personal activity and location measurement system (palms)." [Online], Available: http://ucsd-palms-project.wikispaces.com/
11.
-, "Smart: A social and mobile weight control program for young adults." [Online], Available: http://clinicaltrials.gov/ct2/show/ NCT01200459
12.
M. S. William J. Kaiser, "Introduction to special issue on wireless health," ACM Transactions in Embedded Computing Systems, Vol. 10, no. 1, 2010.
13.
A. Gaggioli, G. Pioggia, G. Tartarisco, G. Baldus, D. Corda, P. Cipresso, and G. Riva, "A mobile data collection platform for mental health research," Personal Ubiquitous Comput., Vol. 17, no. 2, pp. 241-251, 2013.
14.
M.-Z. Poh, K. Kim, A. Goessling, N. Swenson, and R. Picard, "Cardiovascular monitoring using earphones and a mobile device," IEEE Pervasive Computing, Vol. 11, no. 4, pp. 18-26, 2012.
15.
S. Nirjon, R. F. Dickerson, Q. Li, P. Asare, J. A. Stankovic, D. Hong, B. Zhang, X. Jiang, G. Shen, and F. Zhao, "Musicalheart: a hearty way of listening to music," in ACM Conference on Embedded Network Sensor Systems, 2012, pp. 43-56.
16.
J. T. Reyes, E. E. Hernandez, and J. S. Garcia, "DSP-based oversampling adaptive noise canceller for background noise reduction for mobile phones," in International Conference on Electrical Communications and Computers, 2012, pp. 327-332.
17.
F. Sufi and I. Khalil, "Diagnosis of cardiovascular abnormalities from compressed ecg: A data mining-based approach," IEEE Transactions on Information Technology in Biomedicine, Vol. 15, no. 1, pp. 33-39, 2011.
18.
M. Swangnetr and D. Kaber, "Emotional state classification in patient-robot interaction using wavelet analysis and statistics-based feature selection," IEEE Transactions on Human-Machine Systems, Vol. 43, no. 1, pp. 63-75, 2013.
19.
A. Madan, M. Cebrian, S. Moturu, K. Farrahi, and A. S. Pentland, "Sensing the "health state" of a community," IEEE Pervasive Computing, Vol. 11, no. 4, pp. 36-45, 2012.
20.
Y. Ma, B. Xu, Y. Bai, G. Sun, and R. Zhu, "Daily mood assessment based on mobile phone sensing," in International Conference on Wearable and Implantable Body Sensor Networks, 2012, pp. 142-147.
21.
J. E. Bardram, M. Frost, K. Szântô, M. Faurholt-Jepsen, M. Vinberg, and L. V. Kessing, "Designing mobile health technology for bipolar disorder: a field trial of the monarca system," in SIGCHI Conference on Human Factors in Computing Systems, 2013, pp. 2627-2636.
22.
R. Paradiso, A. Bianchi, K. Lau, and E. Scilingo, "Psyche: Personalised monitoring systems for care in mental health," in International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, pp. 3602-3605.
23.
W. M. Yeung and J. K. Ng, "An enhanced wireless LAN positioning algorithm based on the fingerprint approach," in TENCON - IEEE Region 10 Conference, 2006, pp. 1-4.
24.
-, "Wireless Ian positioning based on received signal strength from mobile device and access points," in IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, 2007, pp. 131-137.
25.
W. M. Yeung, J. Zhou, and J. K. Ng, "Enhanced fingerprint-based location estimation system in wireless Ian environment," in Emerging Directions in Embedded and Ubiquitous Computing, 2007, pp. 273-284.
26.
K. C. Shum and J. K. Ng, "Detecting, locating, and tracking hacker activities within a wlan network," in International Conference on Embedded and Real-Time Computing Systems and Applications, 2010, pp. 53-58.
27.
K. C. Y Shum, Q. J. Cheng, J. K.-Y Ng, and D. Ng, "A signal strength based location estimation algorithm within a wireless network," in IEEE International Conference on Advanced Information Networking and Applications, 2011, pp. 509-516.
28.
K. C. Y Shum, J.-Y Ng, and Q. J. Cheng, "The design and implementation of a wireless location estimation system in a wireless local area network," in International Conference on Advanced Information Networking and Applications Workshops, 2012, pp. 623-628.
29.
Z.-l. Wu, C.-h. Li, J. K.-Y. Ng, and K. R. Leung, "Location estimation via support vector regression," IEEE Transactions on Mobile Computing, Vol. 6, no. 3, pp. 311-321, 2007.
30.
K. M. Chu, K. R. Leung, J. K.-Y. Ng, and C. H. Li, "A directional propagation model for locating mobile stations within a mobile phone network," International Journal of Wireless and Mobile Computing, Vol. 3, no. 1, pp. 12-21, 2008.

Contact IEEE to Subscribe

References

References is not available for this document.