Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor

Proceedings - International Symposium on Wearable Computers, ISWC

Volumn , Issue , 2007, Pages 37-40

  Tapia, Emmanuel Munguia ^a   Intille, Stephen S ^a   Haskell, William ^c   Larson, Kent ^a   Wright, Julie ^b   King, Abby ^c   Friedman, Robert ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b BOSTON MEDICAL CENTER   (United States)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BIOLOGICAL ORGANS; GYMNASIUMS; REAL TIME CONTROL; WIRELESS NETWORKS;

HEART RATE MONITOR; PHYSICAL ACTIVITIES; REAL-TIME ALGORITHMS;

ACCELEROMETERS;

EID: 41149177155     PISSN: 15504816     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISWC.2007.4373774     Document Type: Conference Paper

References (9)

1. Bao, L. and S.S. Intille, Activity Recognition from User-Annotated Acceleration Data, in Proc. Int'l Conf. Pervasive Comp. 2004: p. 1-17.
2. Lester, J., et al., A Hybrid Discriminative/Generative Approach for Modeling Human Activities, in Proc. Int'l Joint Conf. on Artificial Intelligence. 2005: p. 766-772.
3. Lukowicz, P., et al., Recognizing Workshop Activity Using Body Worn Microphone and Accelerometers, in Proc. Int'l Conf. Pervasive Comp. 2004: p. 18-32.
4. Maurer, U., et al., Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions, in Proc. Int'l Workshop on Wearable and Implantable Body Sensor Networks. 2006, p. 113-116.
5. Parkka, J., et al., Activity Classification Using Realistic Data From Wearable Sensors. IEEE Transactions on inf. Tech. in Biomedicine, 2006. 10(1): p. 119-128.
6. Subramanya, A., et al., Hierarchical Models for Activity Recognition, in Proc. of the IEEE Conference in Multimedia Signal Processing. 2007: p. 233-237.
7. E. Munguia Tapia, et al., The Design of a Portable Kit of Wireless Sensors for Naturalistic Data Collection, in Proc. Int'l Conf. Pervasive Comp. 2006: p. 117-134.
8. Witten, I.H. and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. 2005: Morgan Kaufmann.
9. Quinlan, J.R., C4.5: Programs for machine learning. 1993, San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. 302.