Abstract
This article introduces MobiSense, a novel mobile health monitoring system for ambulatory patients. MobiSense resides in a mobile device, communicates with a set of body sensor devices attached to the wearer, and processes data from these sensors. MobiSense is able to detect body postures such as lying, sitting, and standing, and walking speed, by utilizing our rule-based heuristic activity classification scheme based on the extended Kalman (EK) Filtering algorithm. Furthermore, the proposed system is capable of controlling each of the sensor devices, and performing resource reconfiguration and management schemes (sensor sleep/wake-up mode). The architecture of MobiSense is highlighted and discussed in depth. The system has been implemented, and its prototype is showcased. We have also carried out rigorous performance measurements of the system including real-time and query latency as well as the power consumption of the sensor nodes. The accuracy of our activity classifier scheme has been evaluated by involving several human subjects, and we found promising results.
- Actigraph. 2009. http://www.mtiactigraph.com/(last access 8/09).Google Scholar
- Berkeley. 2009. TinyOS community forum (online). http://www.tinyos.net. (last access 8/09).Google Scholar
- Burrell, J., Brooke, T. and Beckwith, R. 2004. Vineyard computing: Sensor networks in agricultural production. IEEE Pervasive Comput., 3, 1, 38--45. Google Scholar
Digital Library
- Cardio Micro Sensor. 2009. http://www.cardiomems.com. (last access 8/09).Google Scholar
- Crossbow Technology. 2009. http://www.xbow.com/. (last access 8/09).Google Scholar
- Devaul, R. W., Sung, M., Gips, J., and Pentland, S. 2003. MIThril 2003: Applications and architecture. In Proceedings of the International Semantic Web Conference (SWC). 4--11. Google Scholar
Digital Library
- Dong, L., Wu, J. K., and Bao, S. 2006. A Hybrid HMM/Kalman filter for tracking hip angle in gait cycle. IEICE Trans. Inform. Syst., E89-D, 2319--2323. Google Scholar
Digital Library
- Embedded and Hybrid Systems II (EHSII): A*STAR. 2009. http://www.ehs-sg.org/. (last access 8/09).Google Scholar
- Estrin, D., Govindan, R., Heidemann, J., and Kumar, S. 1999. Next century challenges: scalable coordination in sensor networks. In Proceedings of the 5th Ann. ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom). 263--270. Google Scholar
Digital Library
- Gay, D., Welsh, M., Levis, P., Brewer, E., Behren, R. V., and Culler, D. 2003. The nesC language: A holistic approach to networked embedded systems. In Proceedings of the ACM Sigplan Conference on Programming Language Design and Implementation (PLDI). 1--11. Google Scholar
Digital Library
- Giancoli, D. C. 1998. Physics 5th Ed. Prentice Hall, Ch. 5.Google Scholar
- Jamison, D. T. 2006. Investing in health. In Disease Control Priorities in Developing Countries, 2nd Ed, D. T. Jamison, J. G. Breman, A. R. Measham, G. Alleyne, M. Claeson, D. B. Evans, P. Jha, A. Mills, and P. Musgrove, Eds. Oxford University Press, 1--34.Google Scholar
- Jovanov, E., Lords, A., Raskovic, D., Cox, P., Adhami, R., and Andrasik, F. 2003. Stress monitoring using a distributed wireless intelligent sensor system. IEEE Eng. Med. Engin. Biol. Mag., 22, 3, 49--55.Google Scholar
Cross Ref
- Liszka, K. J., Mackin, M. A., Lichter, M. J., York, D. W., Pillai, D., and Rosenbaum, D. S. 2004. Keeping a beat on the heart. Pervasive Comput. 3, 4, 42--49. Google Scholar
Digital Library
- Lo, B., Thiemjarus, S., King, R., and Yang, G.-Z. 2005. Body sensor network—A wireless sensor platform for pervasive healthcare monitoring. In Proceedings of the 3rd International Conference on Pervasive Computing. 77--80.Google Scholar
- Lo, B. and Yang, G. Z. 2005. Architecture for body sensor networks. In IEE Proc. Perspec. Pervasive Comput., 23--28.Google Scholar
- Lorincz, K., Malan, D. J., Fulford-Jones, T. R. F., Nawoj, A., Clavel, A., Shnayder, V., Mainland, G., Moulton, S., and Welsh, M. 2004. Sensor networks for emergency response: Challenges and opportunities. IEEE Pervasive Comput. 3, 4, 16--23. Google Scholar
Digital Library
- Malan, D., Fulford-Jones, T. R. F., Welsh, M., and Moulton, S. 2004. CodeBlue: An ad hoc sensor network infrastructure for emergency medical care. In Proceedings of the Workshop on Applications of Mobile Embedded Systems (WAMES). 12--14.Google Scholar
- Marsh, A. 2002. The E-care project—removing the wires. In Proceedings of the International Conference on Computational Science (ICCS). 1012--1018. Google Scholar
Digital Library
- Martin, T., Jovanov, E., and Raskovic, D. 2000. Issues in wearable computing for medical monitoring applications: A case study of a wearable ECG monitoring device. In Proceedings of the International Symposium on Wearable Computers (ISWC). 43--50. Google Scholar
Digital Library
- Mathie, M. J. and Celler, B. G. 2001. A system for monitoring posture and physical activity using accelerometers. In Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 3654--3657.Google Scholar
- Med-Electronics Inc. 2009. http://med-electronics.com/. (last accessed 8/09).Google Scholar
- Negi, R. and Rajeswaran, A. 2004. Capacity of power constrained ad hoc networks. In Proceedings of IEEE INFOCOM. 1, 443--453.Google Scholar
- Pentland, S. 2004. Healthwear: Medical technology becomes wearable, IEEE Comput. 37, 5, 34--41. Google Scholar
Digital Library
- Polar. 2009. http://www.polarusa.com/. (last accessed 8/09).Google Scholar
- Pro Medicus Limited. 2009a. http://www.promedicus.com.au. (last accessed 8/09).Google Scholar
- Ross, P. E. 2004. Managing care through the air. IEEE Spectr. 14--19. Google Scholar
Digital Library
- Silva, B. D., Natarajan, A., Motani, M., and Chua, K.-C. 2008. Design considerations of body sensor networks. In Proceedings of the Tenth IEEE International Conference on e-Health Networking, Applications & Services (IEEE Healthcom). 323--328.Google Scholar
- Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., and Estrin, D. 2004. Habitat monitoring with sensor networks. Comm. ACM, 47, 6, 34--40. Google Scholar
Digital Library
- Texas Instruments. 2009. http://www.ortodoxism.ro/datasheets2/9/0oe5ltue2s77tejsk7j5xs0j72fy. pdf. (last accessed 8/09).Google Scholar
- Texas Instruments Chipcon CC2420. 2009. http://focus.ti.com/docs/prod/folders/print/cc2420. html. (last accessed 8/09).Google Scholar
- U.S. Census Bureau. 2009. http://www.census.gov/ipc/www/usinterimproj/. (last accessed 8/09).Google Scholar
- Waluyo, A. B., Pek, I., Ying, S. Jiankang, W., Chen, X., and Yeoh, W.-S. 2008. LiteMWBAN: A lightweight middleware for wireless body area networks. In Proceedings of the 5th International Workshop on Wearable and Implantable Body Sensor Networks (BSN'08). 141--144.Google Scholar
- Wang, C. C., Chiang, C. Y., Huang, C. N., and Chan, C. T. 2008. Development of a fall detecting system for the elderly residents. In Proceedings of the 2nd IEEE International Conference of Bioinformatics and Biomedical Engineering. 1359--1362.Google Scholar
- Welch, J., Guilak, F., and Baker, S. D. 2004. A wireless ECG smart sensor for broad application in life threatening event detection. In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 3447--3449.Google Scholar
- Yang, G.-Z., Lo, B., Wang, J., Rans, M., Thiemjarus, S., Ng, J., Garner, P., Brown, S., Majeed, B., and Neid, I. 2004. From sensor networks to behaviour profiling: A homecare perspective of intelligent building. In Proceedings of the IEE Seminar for Intelligent Buildings.Google Scholar
- Yeoh, W. S., Wu, J. K., Pek, I., Yong, Y. H., Chen, X., and Waluyo, A. B. 2008. Real-time tracking of flexion angle by using wearable accelerometer sensors. In Proceedings of the 5th International Workshop on Wearable and Implantable Body Sensor Networks (BSN'08). 125--128.Google Scholar
- Zhou, H. Y. and Hu, H. S. 2004. A survey—human movement tracking and stroke rehabilitation. Tech. rep., CSM-420 ISSN 1744--8050.Google Scholar
Index Terms
MobiSense: Mobile body sensor network for ambulatory monitoring
Recommendations
SVM based context awareness using body area sensor network for pervasive healthcare monitoring
IITM '10: Proceedings of the First International Conference on Intelligent Interactive Technologies and MultimediaIn the present growing era advancement of computer processing power, data communication capabilities, low power micro electronics devices and micro sensors increases the popularity of wireless sensor network in real life. Body area sensor network is a ...
A Wireless Physiological Sensor Area Network
NBIS '15: Proceedings of the 2015 18th International Conference on Network-Based Information SystemsGenerally, physiological data as the health indicators of an elderly can effectively reflect his/her health status which in turn shows whether he/she has fully satisfied with his/her everyday life or not. A healthcare sensor system for elderly is a ...
Authenticated health monitoring scheme for wireless body sensor networks
BodyNets '12: Proceedings of the 7th International Conference on Body Area NetworksSecurity and privacy are the main concern for patients to seek wireless body sensor network monitoring their health. Considering the limitations of power, computation capability and storage resources, it is a big challenge to find out suitable secure ...






Comments