The Design of a Portable Kit of Wireless Sensors for Naturalistic Data Collection

  • Emmanuel Munguia Tapia
  • Stephen S. Intille
  • Louis Lopez
  • Kent Larson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3968)


In this paper, we introduce MITes, a flexible kit of wireless sensing devices for pervasive computing research in natural settings. The sensors have been optimized for ease of use, ease of installation, affordability, and robustness to environmental conditions in complex spaces such as homes. The kit includes six environmental sensors: movement, movement tuned for object-usage-detection, light, temperature, proximity, and current sensing in electric appliances. The kit also includes five wearable sensors: onbody acceleration, heart rate, ultra-violet radiation exposure, RFID reader wristband, and location beacons. The sensors can be used simultaneously with a single receiver in the same environment. This paper describes our design goals and results of the evaluation of some of the sensors and their performance characteristics. Also described is how the kit is being used for acquisition of data in non-laboratory settings where real-time multi-modal sensor information is acquired simultaneously from several sensors worn on the body and up to several hundred sensors distributed in an environment.


Sensor Node Medium Access Control Pervasive Computing Time Division Multiple Access Receiver Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity Recognition in the Home Using Simple and Ubiquitous Sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Wilson, D.H., Atkeson, C.G.: Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Hahnel, D., Fox, D., Kautz, H.: Inferring Activities from Interactions with Objects. IEEE Pervasive Computing Magazine 3, 4 (2004)CrossRefGoogle Scholar
  4. 4.
    Perkowitz, M., Philipose, M., Patterson, D.J., Fishkin, K.: Mining Models of Human Activities from the Web. In: Proceedings of The Thirteenth International World Wide Web Conference (WWW 2004), New York, USA (2004)Google Scholar
  5. 5.
    Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Mantyjarvi, J.H.J., Seppanen, T.: Recognizing Human Motion with Multiple Acceleration Sensors. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 747–752 (2001)Google Scholar
  7. 7.
    Lee, S.-W., Mase, K.: Activity and location recognition using wearable sensors. IEEE Pervasive Computing 1(3), 24–32 (2002)CrossRefGoogle Scholar
  8. 8.
    Cook, D.J., Youngblood, M., Heierman, E.O., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: MavHome: An Agent-Based Smart Home. In: Proceedings of The First IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), Fort Worth,Texas. PerCom, pp. 521–524 (2003)Google Scholar
  9. 9.
    Mozer, M.: The Neural Network House: An Environment that Adapts to its Inhabitants. In: Proceedings of the AAAI Spring Symposium on Intelligent Environments. Technical Report SS-98-02, pp. 110–114. AAAI Press, Menlo Park, CA (1998)Google Scholar
  10. 10.
    University of Rochester Center for Future Health. The Smart Medical Home (cited March 11, 2005), available from
  11. 11.
    Barger, T., Brown, D., Alwan, M.: Health Status Monitoring through Analysis of Behavioral Patterns. In: Proceedings of The 8th National Congress of Italian Association for Artificial Intelligence: Workshop on Ambient Intelligence (AI*IA 2003), Polo didattico L. Fibonacci, University of Pisa (2003)Google Scholar
  12. 12.
    Beigl, M., Krohn, A., Zimmer, T., Decker, C.: Typical Sensors Needed in Ubiquitous and Pervasive Computing. In: Proceedings of the First International Workshop on Networked Sensing Systems (INSS 2004), Tokyo, Japan, pp. 153–158 (2004)Google Scholar
  13. 13.
    Philipose, M., Fishkin, K., Fox, D., Kautz, H., Patterson, D., Perkowitz, M.: Guide: Towards Understanding Daily Life via Auto-Identification and Statistical Analysis. In: Proc. The 2nd International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications (UbiHealth 2003), Seattle, WA (2003)Google Scholar
  14. 14.
    Crossbow Technology Inc.: MICA2DOT Wireless Microsensor Mote (2005) (cited October 3, 2005), available from
  15. 15.
    Crossbow Technology Inc.: MICAz Wireless Measurement System (2005) (cited October 3, 2005), available from
  16. 16.
    Kling, R.M.: Intel Mote: An Enhanced Sensor Network Node. In: Proceedings of The International Workshop on Advanced Sensors, Structural Health Monitoring and Smart Structures, Keio University, Japan (2003)Google Scholar
  17. 17.
    Moteiv: tmote Sky: Ultra Low Power IEEE 802.15.4 Compliant Wireless Sensor Module (2005) (cited October 3, 2005), available from
  18. 18.
    Beigl, M., Decker, C., Krohn, A., Riedel, T., Zimmer, T.: uParts: Low Cost Sensor Networks at Scale. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Beigl, M., Gellersen, H.: Smart-Its: An Embedded Platform for Smart Objects. In: Smart Objects Converence (sOc 2003), Grenoble, France, pp. 15–17 (2003)Google Scholar
  20. 20.
    Park, C., Liu, J., Chou, P.H.: Eco: an Ultra-Compact Low-Power Wireless Sensor Node for Real-Time Motion Monitoring. In: Proceedings of The Fourth International Conference on Information Processing in Sensor Networks (IPSN 2005), Sunset Village, UCLA, Los Angeles, CA, pp. 398–403 (2005)Google Scholar
  21. 21.
    Beutel, J., Kasten, O., Mattern, F., Römer, K., Siegemund, F., Thiele, L.: Prototyping wireless sensor network applications with bTnodes. In: Karl, H., Wolisz, A., Willig, A. (eds.) EWSN 2004. LNCS, vol. 2920, pp. 323–338. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    M. Net: MeshScape 2.4GHz Modules and Assemblies (2005) (cited October 3, 2005), available from
  23. 23.
    Crossbow Technology Inc.: MSP-SYS MSP Mote Developer’s System (2005) (cited October 3, 2005), available from
  24. 24.
    DeVaul, R., Sung, M., Gips, J., Pentland, A.: MIThril 2003: Applications and Architecture. In: Proceedings of the 7th International Symposium on Wearable Computers (ISWC 2003), White Plains, NY (2003)Google Scholar
  25. 25.
    Kling, R., Adler, R., Huang, J., Hummel, V., Nachman, L.: The Intel iMote: Using Bluetooth in Sensor Networks. In: Proceedings of The 2nd International Conference on Embedded Networked Sensor Systems, p. 318. ACM, Baltimore, MD, USA (2003)Google Scholar
  26. 26.
    MTI Actigraph: GT1M Actigraph (2005) (cited October 3, 2005), available from
  27. 27.
    Electronic Educational Devices: Watts Up? Pro KWH Meter Review (2005) (cited October 3, 2005), available from
  28. 28.
    Fern, D.G., Tietsworth, S.C.: Automatic Wireless Communications. Sensors Magazine 16, 9 (1999)Google Scholar
  29. 29.
    Feldmeier, M., Paradiso, J.A.: Giveaway Wireless Sensors for Large-Group Interaction. In: Proceedings of the ACM Conference on Human Factors and Computing Systems (CHI 2004), Vienna, Austria, pp. 1291–1292 (2004)Google Scholar
  30. 30.
    Paradiso, J.A.: Wearable Wireless Sensing for Interactive Media. In: First International Workshop on Wearable and Implantable Body Sensor Networks, Imperial College, London (2004)Google Scholar
  31. 31.
    Kochenderfer, M.J., Gupta, R.: Common Sense Data Acquisition for Indoor Mobile Robots. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI 2004), San Jose, California (2004)Google Scholar
  32. 32.
    Munguia-Tapia, E., Intille, S.S.: MITes: MIT Environmental Sensors Hardware and Software Specifications (2006) (cited February 1, 2006), available from

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Emmanuel Munguia Tapia
    • 1
  • Stephen S. Intille
    • 1
  • Louis Lopez
    • 1
  • Kent Larson
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations