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Remote Monitoring Using Smartphone Based Plantar Pressure Sensors: Unimodal and Multimodal Activity Detection

  • Ferdaus KawsarEmail author
  • Sheikh Ahamed
  • Richard Love
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8456)

Abstract

Automatic activity detection is important for remote monitoring of elderly people or patients, for context-aware applications, or simply to measure one’s activity level. Recent studies have started to use accelerometers of smart phones. Such systems require users to carry smart phones with them which limit the practical usability of these systems as people place their phones in various locations depending on situation, activity, location, culture and gender. We developed a prototype for shoe based activity detection system that uses pressure data of shoe and showed how this can be used for remote monitoring. We also developed a multimodal system where we used pressure sensor data from shoes along with accelerometers and gyroscope data from smart phones to make a robust system. We present the details of our novel activity detection system, its architecture, algorithm and evaluation.

Keywords

Algorithm Measurement Performance Design Remote monitoring 

Notes

Acknowledgments

This work was partially supported by grant from IBCRF.

References

  1. 1.
    Caspersen, C.J., Powell, K.E., Christenson, G.M.: Physical activity, exercise and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 110, 126–131 (1985)Google Scholar
  2. 2.
    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
  3. 3.
    Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Borriello, G., Hemingway, B., Klasnja, P., Koscher, K., Landay, J.A., Lester, J., Wyatt, D., Haehnel, D.: The Mobile Sensing Platform: an Embedded System for Capturing and Recognizing Human Activities, In IEEE Pervasive Computing Magazine. Spec, Issue on Activity-Based Computing, April-June (2008)Google Scholar
  4. 4.
    Foerster, F., Smeja, M., Fahrenberg, J.: Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. Comput. Hum. Beh. 15(5), 571–583 (1999)CrossRefGoogle Scholar
  5. 5.
    Randell, C., Muller, H.: Context awareness by analyzing accelerometer data. The Fourth Int’l Symposium on Wearable Computers, pp. 175–176. Atlanta, Georgia (2000)CrossRefGoogle Scholar
  6. 6.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. In: Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data, pp. 10–18 (2010)Google Scholar
  7. 7.
    Yang, J.: Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones. In: Proceedings First International Workshop on Interactive Multimedia for Consumer Electronics, pp. 1–10. ACM, New York (2009)Google Scholar
  8. 8.
    Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Eisenman, S., Lu, H., Musolesi, M., Zheng, X., Campbell, A.: Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys ’09), Raleigh, NC (2008)Google Scholar
  9. 9.
    Subramanya, A., Raj, A., Bilmes, J., Fox, D.: Recognizing activity and spatial context using wearable sensors. In: Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06), pp. 494–502 (2006)Google Scholar
  10. 10.
    Cho, Y., Nam, Y., Choi, Y-J., Cho, W.-D.: Smart-Buckle: human activity recognition using a 3-axis accelerometer and a wearable camera. In: Proceedings of the 2nd International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments (Healthnet ’08) (2008)Google Scholar
  11. 11.
    Györbıró, N., Fábián, A.: An activity recognition system for mobile phones. Mob. Netw. Appl. 14(1), 82–91 (2009)CrossRefGoogle Scholar
  12. 12.
    Cui, Y., Chipchase, J., Ichikawa, F.: A Cross Culture Study on Phone Carrying and Physical Personalization. In: Aykin, N. (ed.) HCII 2007. LNCS, vol. 4559, pp. 483–492. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Shu, L., Hua, T., Wang, Y., Li, Q., Feng, D.D., Tao, X.: In-shoe plantar pressure measurement and analysis system based on fabric pressure sensing array. IEEE Trans. Inf Technol. Biomed. 14(3), 767–775 (2010)CrossRefGoogle Scholar
  14. 14.
    Sun, L., Zhang, D., Li, B.: Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. In: Aykin, Nuray (ed.) UIC 2010. LNCS, vol. 6406, pp. 548–562. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics, Statistics and Computer ScienceMarquette UniversityMilwaukeeUSA
  2. 2.International Breast Cancer Research FoundationMadisonUSA

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