Wearable accelerometer based extendable activity recognition system

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2010, Pages 3641-3647

  Yang, Jie ^a   Wang, Shuangquan ^a   Chen, Ningjiang ^b   Chen, Xin ^b   Shi, Pengfei ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b PHILIPS RESEARCH LABORATORIES   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY RECOGNITION; CLASSIFICATION ACCURACY; HUMAN ACTIVITIES; MULTI-CLASS CLASSIFIER; ONE-CLASS CLASSIFICATION; PERVASIVE ENVIRONMENTS; RECOGNITION SYSTEMS; RESEARCH ISSUES; SEGMENTATION ALGORITHMS; TEST SAMPLES; TRAINING SAMPLE;

CLASSIFIERS;

ROBOTICS;

EID: 77955787264     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2010.5509783     Document Type: Conference Paper

References (13)

1. Lukowica P., Ward J. A. et al., Recognizing workshop activity using body worn microphones and accelerometers, In 2nd International Conference on Pervasive Computing, Vienna, Austria, Springer, 2004, pp.18-22.
2. Ohgi Y., Yasumura M., Ichikawa H. and Miyaji C., Analysis of stroke technique using acceleration sensor IC in freestyle swimming, The Engineering of SPORT, Blackwell Publishing, 2000, pp.503-511.
3. Mantyla J., Himberg J. and Seppanen T., Recognizing human motion with multiple acceleration sensors, In IEEE International Conference on Systems, Man and Cybernetics, Tucson, USA, IEEE Press, 2001, pp.747-752.
4. Ravi N., Dandekar N., Mysore P. and Littman M., Activity recognition from accelerometer data, In proceedings of the 17th Annual Conference on Innovative Applications of Artificial Intelligence, Pittsburgh, USA, AAAI Press, 2005, pp.141-1546.
5. Devaul R. W. and Dunn S., Real-Time Motion Classification for Wearable Computing Applications, Technical report, MIT Media Laboratory, 2001.
6. Bao L. and Intille S. S., Activity recognition from user-annotated acceleration data, In 2nd International Conference on Pervasive Computing, Vienna, Austria, Springer, 2004, pp.1-17.
7. Han J., Kamber M., Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher, 2000.
8. Tax D. M. J., One-class classification, [Dissertation], Delft University of Technology, 1999.
9. Duda R. O., Hart P. E. and Strok D. G., Pattern classification (2nd ed.), New York: Wiley, 2001.
10. Wang M., Yang J., Liu G. P., Xu Z. J. and Chou K. C., Weighted SVM for predicting membrane protein types based on pseudo amino acid composition, Protein Engineering, Desigan & Selection, 2004, 17(4), pp.1-8.
11. Abonyi J., Feil B., Nemeth S. and Arva P., Modified gath-geva clustering for fuzzy segmentation of multivariate time-series, Fuzzy Sets and Systems, 2005, 149(1), pp.39-56.
12. KXP74 Series datasheet. www.kionix.com/Product%20Sheets/ KXP74%20Series.pdf.
13. Kern N., Schiele B. and Schmidt A., Multi-sensor activity context detection for wearable computing, In European Symposium on Wearable Computers, Eindhoven, The Netherlands, IEEE Press, 2003, pp.220-234.