A System for Stress Information Collection and Personalized Recommendation
Stress is an increasing challenge to the health, well-being, and productivity of the urban dwellers. World Health Organization, as well as numerous other organizations and researchers have pointed out that stress-related disorders are on the rise and will be one of the most challenging public health threats in the decades to follow. Although the awareness of stress and its negative impact on health and productivity is growing, there is a lack of cost-efficient and easily assessable tools for the public to deal with stress. Medical and psychological approaches available are costly, lack sustained effects or are hampered due to limited availability of trained professionals to deliver the stress treatments.
In order to fundamentally change the way we assess and manage stress, we have launched the StressBar project. StressBar will dramatically improve the assessment of stress in people's daily life as well as offering cost-efficient and easy-to-use effective treatment. Furthermore, the StressBar project will provide stress researchers with an entire new and rapid way to study stress as well as assess the impact from various stress interventions. We believe that participatory sensing is an important method to the research. StressBar will allow us to collect information about individuals and in the future make personalized recommendations. The collective data also allow us to analyze the stress status of a region, such as Metro-Detroit. This will help the government to make more accurate decisions. For example, in more stressed areas, there should be more medical services.
system follows the typical client-server design mode.The server is
currently running on a Linux machine,and the client is running on the
Android platform, which is responsible for data collection, including
heart beat measure, stress and location information collecting. The
communication protocol between them is HTTP, which provides fast and
reliable solution to the system implementation. Four modules are
presented in the following: data collection module,
and data store module.
is designed to collect possible user stress information while at the
same time making the collection process cost-effective and easy
accessible, so we choose android as the mobile client platform. The
information we collected includes heart beat, location, and user
interactive questions. Heart rate information is collected through
Bluetooth from a Nonin 4100 sensor which is attached to the users'
finger. After two minutes, when the heart beat measure process is
finished, the program pops up questions for users to fill. The heart
rate and answers for those questions together with location information
are then sent to a remote server through our communication model.
Collecting location information is time consuming, so we implement it
as background service updating location every 5 minutes.
The communication module
hides the complexity of client server communication. It is implemented
as background service on mobile clients. The protocol we use is
XML-RPC, which is a remote procedure call using HTTP as the transport
and XML as the encoding. It is flexible and easy to implement, but one
disadvantage is that it increases our phone's network traffic, which
may increase our cost on using StressBar.
The service module
targets receiving client side requests and giving responses back to the
client. We implement the service module as a web service since it is a
fully developed technology, which will greatly reduce our efforts on
building a reliable, high performance and scalable server. We can
easily reconfigure it to add more services without changing the written
code. StressMap, which displays the stress information on the map based
on the location information collected by clients, can be easily
deployed on our web server by just adding several lines on web.xml file.
The data store module
we use currently is built on MySQL database. We use the JDBC pool as
the connection between the web service and database access since it
automatically help us handle concurrent database requests. MySQL
database has disadvantages on constructing large scale, distributed
data storage system. To solve its disadvantages, we are building a
highly distributed NoSQL data storage system named Woodward, which will
be used for health data storing.
Dr. Weisong Shi
Dr. Bengt B. Arnetz, Department of Family Medicine and Public
- Dajun Lu, Guoxing Zhan, Shinan Wang, Weisong Shi, Clairy Wiholm and Bengt B. Arnetz, StressBar: A System for Stress Information Collection, in Proceedings of the Wireless Health 2011, La Jolla, CA, Oct 11-13, 2011.
- Shinan Wang, Weisong Shi, Bengt B. Arnetz and Clairy Wiholm, SPARTAN: A Framework for Smart Phone Assisted Real-Time Healthcare Network Design, in Proceedings of CollaborateCom 2010, Chicago, Oct 2010.
- Guoxing Zhan, Weisong Shi and Julia Deng, Design and Implementation of TARF: A Trust-Aware Routing Framework for WSNs, accepted by IEEE Transactions on Secure and Dependable Computing, Sep. 2011.
- Sheng Gao, Jianfeng Ma, Weisong Shi and Guoxing Zhan, Towards Location and Trajectory Privacy Protection in Participatory Sensing, Proceedings of MobiCASE 2011 (6 pages poster), Los Angles, USA, Oct 24-27, 2011.
- Guoxing Zhan, Weisong Shi and Julia Deng, SensorTrust: A Resilient Trust Model for Wireless Sensing Systems, Elsevier Pervasive and Mobile Computing, Vol. 7, No. 4, August 2011, pp. 509-522.
- Safwan Al-Omari and Weisong Shi, Incremental Sensor Node Deployment for Low Cost and Highly Available WSNs, in Proceedings of MSN 2010, December 20-22, 2010, Hangzhou, China.
- Guoxing Zhan, Weisong Shi and Julia Deng, TARF: A Trust-Aware Routing Framework for Wireless Sensor Networks, in Proceedings of 7th European Conference on Wireless Sensor Networks (EWSN), Feb. 17-19, 2010.
- Guoxing Zhan, Weisong Shi and Julia Deng, SensorTrust: A Resilient Trust Model for Wireless Sensing Systems, in Proceedings of the ACM SenSys 2009 (poster abstract), Berkeley, CA, November 4-6, 2009.
- Shinan Wang, Kewei Sha and Weisong Shi, Role-based Deceptive Data Detection and Filtering in WSNs, in Proceedings of the 8th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2009) (two page summary), April 13-16, 2009, San/Francisco. .
- Kewei Sha and Weisong Shi, Consistency-Driven Data Quality
Management in Wireless Sensor Networks, Journal of Parallel and Distributed Computing, Vol. 68, No. 9, pp. 1207-1221, September 2008.
- Junzhao Du and Weisong Shi, App-MAC: An Application-Aware Event-Oriented MAC
Protocol for Multimodality Wireless Sensor Networks,
accepted by IEEE Transactions on Vehicular Technology.
- Kewei Sha, Guoxing Zhan, Weisong Shi,
Mark Lumley, Clairy Wiholm and Bengt Arnetz, SPA: A Smart Phone Assisted Chromic Illness
Self-Management System with Participatory Sensing, in Proceedings of
2008, in conjunction with ACM/USENIX MobiSys 2008, Breckenridge,
Colorado, June 17, 2008.
Weisong Shi, Availability
Modeling and Analysis of Autonomous In-Door WSNs, in Proceedings of the
4th IEEE International Conference on Mobile
Ad-Hoc and Sensor Systems (MASS), Pisa,
Itlay, Oct 8-11, 2007. (Accept rate: 25%, 67 out of 265).
Al-Omari and Weisong Shi, Towards
Highly-Available WSNs for Assisted Living, in Proceedings of HealthNet 2007,
in conjunction with USENIX/ACM MobiSys 2007, June 11-14, San Juan.
(Accept rate: 24%, 12 out of 50).
Sha and Weisong Shi, Modeling Data Consistency in
Wireless Sensor Networks, in Workshop
Proceedings of ICDCS 2007 (WWASN 2007), Toronto, June 25-29, 2007.
(Accept rate: 30%)
- John P. Walters,
Zhengqiang Liang, Weisong Shi, and Vipin Chaudhary, Wireless
Sensor Networks Security: A Survey, book chapter of
Security in Distributed, Grid, and Pervasive Computing, Yang
Xiao (Eds.), Auerbach Publications, pp. 367-410, April
- Kewei Sha
and Weisong Shi, Consistency-Driven Data Quality Management in WSNs,
MIST-TR-2006-013, December, 2006. submitted.
Al-Omari, Junzhao Du, and Weisong Shi, Score:
A Sensor Core Framework for Cross-Layer Design (extended
Proceedings of the 3rd International Conference on Quality-of-Service
in Wired/Wireless Networks (QShine 2006), Waterloo, Canada, August 7-9,
Sha and Weisong Shi, On the Effects of Consistency in
Data Operations in Wireless Sensor Networks, in
Proceedings of the IEEE 12th International Conference on Parallel and
Distributed Systems (ICPADS '2006), Minneapolis, USA, July 12-15, 2006.