ABSTRACT
This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson's Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and application specific policies.
Mercury is designed to overcome the core challenges of long battery lifetime and high data fidelity for long-term studies where patients wear sensors continuously 12 to 18 hours a day. This requires tuning sensor operation and data transfers based on energy consumption of each node and processing data under severe computational constraints. Mercury provides a high-level programming interface that allows a clinical researcher to rapidly build up different policies for driving data collection and tuning sensor lifetime. We present the Mercury architecture and a detailed evaluation of two applications of the system for monitoring patients with Parkinson's Disease and epilepsy.
- A. Ahmadi, D. Rowlands, and D. James. Investigating the translational and rotational motion of the swing using accelerometers for athlete skill assessment. Sensors, 2006. 5th IEEE Conference on, pages 980--983, Oct. 2006.Google ScholarCross Ref
- F. R. Allen, E. Ambikairajah, N. H. Lovell, and B. G. Celler. Classification of a known sequence of motions and postures from accelerometry data using adapted gaussian mixture models. PHYSIOLOGICAL MEASUREMENT, 2006.Google ScholarCross Ref
- R. Aylward and J. A. Paradiso. A compact, high-speed, wearable sensor network for biomotion capture and interactive media. In IPSN '07, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- J. Boyle, M. Karunanithi, T. Wark, W. Chan, and C. Colavitti. Quantifying functional mobility progress for chronic disease management. EMBS '06, pages 5916--5919, 2006.Google ScholarCross Ref
- T. R. Burchfield and S. Venkatesan. Accelerometer-based human abnormal movement detection in wireless sensor networks. In HealthNet '07, pages 67--69, New York, NY, 2007. Google ScholarDigital Library
- B. Ceulemans, B. Vanrumste, P. Colleman, S. Omloop, and K. Cuppens. Detection of nocturnal epileptic seizures of pediatric patients using accelerometers. In Belgian Day on Biomedical Engineering, 2007.Google Scholar
- J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy. Wearable sensors for reliable fall detection. IEEE-EMBS'05, pages 3551--3554, 2005.Google ScholarCross Ref
- T. Choudhury, G. Borriello, S. Consolvo, D. Haehnel, B. Harrison, B. Hemingway, J. Hightower, P. Klasnja, K. Koscher, A. LaMarca, J. A. Landay, L. LeGrand, J. Lester, A. Rahimi, A. Rea, and D. Wyatt. The mobile sensing platform: An embedded system for capturing and recognizing activities. IEEE Pervasive Magazine, April 2008.Google ScholarDigital Library
- D. W. Curtis, E. J. Pino, J. M. Bailey, E. I. Shih, J. Waterman, S. A. Vinterbo, T. O. Stair, J. V. Guttag, R. A. Greenes, and L. Ohno-Machado. Smart: An integrated wireless system for monitoring unattended patients. Journal of the American Medical Informatics Association, 15(1):44--53, 2008.Google ScholarCross Ref
- R. DeVaul, M. Sung, J. Gips, and A. Pentland. Mithril 2003: applications and architecture. IEEE International Symposium of Wearable Computing, 2003. Google ScholarDigital Library
- O. Devinsky. Tonic-clonic seizures. http://www.epilepsy.com/epilepsy/seizure_tonicclonic, 2004.Google Scholar
- C. Doukas and I. Maglogiannis. Advanced patient or elder fall detection based on movement and sound data. Pervasive-Health'08, pages 103--107, 30 2008-Feb. 1 2008.Google ScholarCross Ref
- E. Farella, L. Benini, B. Riccò and A. Acquaviva. Moca: A low-power, low-cost motion capture system based on integrated accelerometers. Advances in Multimedia, 2007(82638), 2007. Google ScholarDigital Library
- J. Flinn and M. Satyanarayanan. Managing battery lifetime with energy-aware adaptation. ACM Transactions on Computer Systems (TOCS), 22(2), May 2004. Google ScholarDigital Library
- R. Ganti, P. Jayachandran, T. Abdelzaher, and J. Stankovic. SATIRE: A Software Architecture for Smart AtTIRE. In Proc. ACM Mobisys, Uppsala, Sweden, June 2006. Google ScholarDigital Library
- D. Gay, P. Levis, R. von Behren, M. Welsh, E. Brewer, and D. Culler. The nesC language: A holistic approach to networked embedded systems. In Proc. Programming Language Design and Implementation (PLDI), June 2003. Google ScholarDigital Library
- B. Greenstein, C. Mar, A. Pesterev, S. Farshchi, E. Kohler, J. Judy, and D. Estrin. Capturing high-frequency phenomena using a bandwidth-limited sensor network. In Proc. Sensys 2006, Boulder, CO, November 2006. Google ScholarDigital Library
- J. He, H. Li, and J. Tan. Real-time daily activity classification with wireless sensor networks using hidden markov model. Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pages 3192--3195, Aug. 2007.Google ScholarCross Ref
- T. Hester, R. Hughes, D. Sherrill, B. Knorr, M. Akay, J. Stein, and P. Bonato. Using wearable sensors to measure motor abilities following stroke. In BSN '06, April 2006. Google ScholarDigital Library
- Intel Corporation. The SHIMMER Sensor Node Platform. 2006.Google Scholar
- S. Kim, R. Fonseca, P. Dutta, A. Tavakoli, D. Culler, P. Levis, S. Shenker, and I. Stoica. Flush: A Reliable Bulk Transport Protocol for Multihop Wireless Networks. In Proc. Sen-Sys'07, 2007. Google ScholarDigital Library
- D. Y. Kwon and M. Gross. Combining body sensors and visual sensors for motion training. In ACE '05: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, pages 94--101, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- A. Lachenmann, P. J. Marron, D. Minder, and K. Rothermer. Meeting lifetime goals with energy levels. In Proc. ACM Sen-Sys, November 2007. Google ScholarDigital Library
- E. D. Lara, D. S. Wallach, and W. Zwaenepoel. Puppeteer: Component-based adaptation for mobile computing. In USITS'01: Proceedings of the 3rd conference on USENIX Symposium on Internet Technologies and Systems, pages 14--14, San Francisco, CA, 2001. Google ScholarDigital Library
- Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach, and G. Zhou. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. BSN'09, pages 138--143, 2009. Google ScholarDigital Library
- X. Liu, P. Shenoy, and M. D. Corner. Chameleon: Application level power management. IEEE Transactions on Mobile Computing, 2008. Google ScholarDigital Library
- B. Lo and G.-Z. Yang. Architecture for Body Sensor Networks. In Perspective in Pervasive Computing, pages 23--28, September 2005.Google ScholarCross Ref
- K. Lorincz, D. Malan, T. R. F. Fulford-Jones, A. Nawoj, A. Clavel, V. Shnayder, G. Mainland, S. Moulton, and M. Welsh. Sensor Networks for Emergency Response: Challenges and Opportunities. IEEE Pervasive Computing, Oct--Dec 2004. Google ScholarDigital Library
- K. Lorincz, B. rong Chen, J. Waterman, G. Werner-Allen, and M. Welsh. Resource aware programming in the pixie os. In SenSys '08: Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 211--224, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- M. Maroti, B. Kusy, G. Simon, and A. Ledeczi. The flooding time synchronization protocol. In Second ACM Conference on Embedded Networked Sensor Systems, November 2004. Google ScholarDigital Library
- M. J. Mathie, B. G. Celler, N. H. Lovell, and A. C. F. Coster. Classification of basic daily movements using a triaxial accelerometer. Medical and Biological Engineering and Computing, 2004.Google ScholarCross Ref
- F. Michahelles and B. Schiele. Sensing and monitoring professional skiers. Pervasive Computing, IEEE, 2005. Google ScholarDigital Library
- E. Miluzzo, N. D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S. B. Eisenman, X. Zheng, and A. T. Campbell. Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In SenSys '08, pages 337--350, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- D. Narayanan and M. Satyanarayanan. Predictive resource management for wearable computing. In Proc. ACM MobiSys 2003, San Francisco, CA, May 2003. Google ScholarDigital Library
- B. D. Noble, M. Satyanarayanan, D. Narayanan, J. E. Tilton, J. Flinn, and K. R. Walker. Agile application-aware adaptation for mobility. In SOSP '97: Proceedings of the sixteenth ACM symposium on Operating systems principles, pages 276--287, Saint Malo, France, 1997. Google ScholarDigital Library
- C. A. Otto, E. Jovanov, and A. Milenkovic. A wban-based system for health monitoring at home. In Proceedings of the 3rd IEEE EMBS International Summer School and Symposium on Medical Devices and Biosensors (ISSS-MDBS 2006), Boston, MA, 2006.Google ScholarCross Ref
- S. Patel, K. Lorincz, R. Hughes, N. Huggins, J. H. Growdon, M. Welsh, and P. Bonato. Analysis of feature space for monitoring persons with Parkinson's Disease with application to a wireless wearable sensor system. In Proc. 29th IEEE EMBS Annual International Conference, August 2007.Google ScholarCross Ref
- A. Pentland. Healthwear: medical technology becomes wearable. Computer, 37(5):42--49, May 2004. Google ScholarDigital Library
- J. Polastre, J. Hill, and D. Culler. Versatile low power media access for wireless sensor networks. In Proc. Second ACM Conference on Embedded Networked Sensor Systems (Sen-Sys), November 2004. Google ScholarDigital Library
- A. Salarian, H. Russmann, F. Vingerhoets, C. Dehollain, Y. Blanc, P. Burkhard, and K. Aminian. Gait assessment in parkinson's disease: toward an ambulatory system for longterm monitoring. Biomedical Engineering, IEEE Transactions on, 51(8):1434--1443, Aug. 2004.Google Scholar
- Sentilla Tmote Sky. http://www.sentilla.com/pdf/eol/tmote-sky-datasheet.pdf.Google Scholar
- J. Sorber, A. Kostadinov, M. Brennan, M. Garber, M. Corner, and E. D. Berger. Eon: A Language and Runtime System for Perpetual Systems. In Proc. ACM SenSys, November 2007. Google ScholarDigital Library
- M. Sung, C. Marci, and A. Pentland. Wearable feedback systems for rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2(1):17, 2005.Google ScholarCross Ref
- M. Visintin, H. Barbeau, N. Korner-Bitensky, and N. E. Mayo. A new approach to retrain gait in stroke patients through body weight support and treadmill stimulation. Stroke, 29(6):1122--1128, June 1998.Google ScholarCross Ref
- G. Werner-Allen, S. Dawson-Haggerty, and M. Welsh. Lance: optimizing high-resolution signal collection in wireless sensor networks. In SenSys '08: Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 169--182, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, and J. Stankovic. Alarm-net: Wireless sensor networks for assisted-living and residential monitoring. Technical Report CS-2006-11, University of Virginia, 2006.Google Scholar
- H. Zeng, X. Fan, C. S. Ellis, A. Lebeck, and A. Vahdat. ECOSystem: Managing Energy as a First Class Operating System Resource. In ASPLOS'02, San Jose, CA, 2002. Google ScholarDigital Library
- G. Zhou, J. Lu, C.-Y. Wan, M. D. Yarvis, and J. A. Stankovic. BodyQoS: Adaptive and Radio-Agnostic QoS for Body Sensor Networks. In Proc. IEEE INFOCOM 2008, Phoenix, AZ, April 2008.Google ScholarCross Ref
Index Terms
- Mercury: a wearable sensor network platform for high-fidelity motion analysis
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