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Issue No.02 - April-June (2008 vol.7)
pp: 32-41
Tanzeem Choudhury , Dartmouth College
Gaetano Borriello , University of Washington
Sunny Consolvo , Intel Research
Dirk Haehnel , Stanford University
Beverly Harrison , Intel Research
Bruce Hemingway , University of Washington
Jeffrey Hightower , Intel Research
Predrag "Pedja" Klasnja , University of Washington
Karl Koscher , University of Washington
Anthony LaMarca , Intel Research
James A. Landay , University of Washington
Louis LeGrand , Intel Research
Jonathan Lester , University of Washington
Ali Rahimi , Intel Research
Adam Rea , Intel Research
Danny Wyatt , University of Washington
ABSTRACT
The Mobile Sensing Platform (MSP) is a small-form-factor wearable device designed for embedded activity recognition. The MSP aims broadly to support context-aware ubiquitous computing applications. It incorporates multimodal sensing, data processing and inference, storage, all-day battery life, and wireless connectivity into a single 4 oz (115 g) wearable unit. Several design iterations and real-world deployments over the last four years have identified a set of core hardware and software requirements for a mobile inference system. This article presents findings and lessons learned in the course of designing, improving and using this system. This article is part of a special issue on activity-based computing.
INDEX TERMS
activity recognition, embedded systems, machine learning, wearable computers
CITATION
Tanzeem Choudhury, Gaetano Borriello, Sunny Consolvo, Dirk Haehnel, Beverly Harrison, Bruce Hemingway, Jeffrey Hightower, Predrag "Pedja" Klasnja, Karl Koscher, Anthony LaMarca, James A. Landay, Louis LeGrand, Jonathan Lester, Ali Rahimi, Adam Rea, Danny Wyatt, "The Mobile Sensing Platform: An Embedded Activity Recognition System", IEEE Pervasive Computing, vol.7, no. 2, pp. 32-41, April-June 2008, doi:10.1109/MPRV.2008.39
REFERENCES
1. L. Bao and S. Intille, "Activity Recognition from User-Annotated Acceleration Data," Proc. 2nd Int'l Conf. Pervasive Computing (Pervasive 04), LNCS 3001, Springer, 2004, pp. 1–17.
2. N. Kern, B. Schiele, and A. Schmidt, "Multi-sensor Activity Context Detection for Wearable Computing," Proc. 1st European Symp. Ambient Intelligence (EUSAI03), LNCS 2875, Springer, 2003, pp. 220–232.
3. A. Schmidt et al., "Advanced Interaction in Context," Proc. 1st Int'l Symp. Handheld and Ubiquitous Computing (HUC 99), LNCS 1707, Springer, 1999, pp. 89–101.
4. U. Maurer et al., "Location and Activity Recognition Using eWatch: A Wearable Sensor Platform," Ambient Intelligence in Every Day Life, LNCS 3864, Springer, 2006, pp. 86–100.
5. J. Lester, T. Choudhury, and G. Borriello, "A Practical Approach to Recognizing Physical Activity," Proc. 4th Int'l Conf. Pervasive Computing (Pervasive 06), LNCS 3968, Springer, 2006, pp. 1–16.
6. P. Lukowicz et al., "WearNET: A Distributed Multi-sensor System for Context Aware Wearables," Proc. 4th Int'l Conf. Ubiquitous Computing (Ubicomp 02), LNCS 2498, Springer, 2002, pp. 361–370.
7. S. Park et al., "Design of a Wearable Sensor Badge for Smart Kindergarten," Proc. 6th Int'l Symp. Wearable Computers (ISWC 02), IEEE CS Press, 2002, pp. 231–238.
8. L. Liao et al., "Training Conditional Random Fields Using Virtual Evidence Boosting," Proc. Int'l Joint Conf. Artificial Intelligence (IJCAI07), 2007; www.ijcai.org/papers07/PapersIJCAI07-407.pdf .
9. M. Mahdaviani and T. Choudhury, "Fast and Scalable Training of Semi-supervised Conditional Random Fields with Application to Activity Recognition," Proc. Neural Information Processing Systems (NIPS 07), 2007; http://books.nips.cc/papers/files/nips20 NIPS2007_0863.pdf.
10. D.T. Wyatt, T. Choudhury, and H. Kautz, "Capturing Spontaneous Conversation and Social Dynamics: A Privacy-Sensitive Data Collection Effort," Proc. Int'l Conf. Acoustics, Speech, and Signal Processing (ICASSP07), IEEE Press, 2007, pp. IV-213–IV-216.
11. L. Liao, D. Fox, and H. Kautz, "Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields," Int'l J. Robotics Research, vol. 26, no. 1, 2007, pp. 119–134.
12. R.E. Schapire, "A Brief Introduction to Boosting," Proc. 16th Int'l Joint Conf. Artificial Intelligence (IJCAI99), Morgan Kaufmann, 1999, pp. 1401–1406.
13. A. Krause et al., "Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing," Proc. 7th IEEE Int'l Symp. Wearable Computers (ISWC 03), IEEE CS Press, 2003, pp. 88–97.
14. S. Consolvo et al., "Activity Sensing in the Wild: A Field Trial of UbiFit Garden," to be published in Proc. ACM SIGCHIConf. Human Factors and Computing Systems (CHI 08), ACM Press, 2008.
15. J.F. Sallis and B.E. Saelens, "Assessment of Physical Activity by Self-Report: Status, Limitations, and Future Directions," Research Quarterly for Exercise and Sport, vol. 71, no. 2, 2000, p. 1–14.
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