Design methodology for context-aware wearable sensor systems

Lecture Notes in Computer Science

Volumn 3468, Issue , 2005, Pages 220-236

  Anliker, Urs ^a   Junker, Holger ^a   Lukowicz, Paul ^a   Tröster, Gerhard ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CONTEXT SENSITIVE GRAMMARS; OPTIMAL CONTROL SYSTEMS; OPTIMIZATION; PARETO PRINCIPLE; PROBLEM SOLVING; PRODUCT DESIGN; SYSTEMS ANALYSIS;

OPTIMIZATION PROBLEMS; PARETO OPTIMAL SYSTEMS; POWER CONSUMPTION; WEARABLE SYSTEMS;

SENSORS;

EID: 24944533758     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11428572_14     Document Type: Conference Paper

References (22)

1. J. Mantyjarvi, J. Himberg, and T. Seppanen. Recognizing human motion with multiple acceleration sensors. In 2001 IEEE International Conference on Systems, Man and Cybernetics, volume 3494, pages 747-752, 2001.
2. Urs Anliker, Jan Beutel, Matthias Dyer, Rolf Enzler, Paul Lukowicz, Lothar Thiele, and Gerhard Tröster. A systematic approach to the design of distributed wearable systems. IEEE Transactions on Computers, 53(3):1017-1033, Aug. 2004.
3. Mathias Stäger, Paul Lukowicz, Niroshan Perera, Thomas von Büren, Gerhard Tröster, and Thad Starner. SoundButton: Design of a Low Power Wearable Audio Classification System. In ISWC 2003: Proceedings of the 7th IEEE International Symposium on Wearable Computers, pages 12-17, October 2003.
4. W. Wolf. Computers as Components: Principles of Embedded Computing System Design. Morgan Kaufman Publishers, 2002.
5. T. Blickle, J. Teich, and L. Thiele. System-level synthesis using evolutionary algorithms. Design Automation for Embedded Systems, 3(1):23-58, January 1998.
6. R. K. Gupta. Co-Synthesis of Hardware and Software for Digital Embedded Systems. Kluwer Academic Publishers, August 1995.
7. I. Karkowski and H. Corporaal. Design space exploration algorithm for heterogeneous multi-processor embedded system design. In Proc. 35th Design Automation Conf. (DAC), pages 82-87, 1998.
8. J. Liu, P. H. Chou, N. Bagherzadeh, and F. Kurdahi. A constraint-based application model and scheduling techniques for power-aware systems. In Proc. 9th Int. Symp. on Hardware/Software Codesign (CODES), pages 153-158, 2001.
9. G. De Micheli. Synthesis and Optimization of Digital Circuits. McGraw-Hill, 1994.
10. M. Eisenring, L. Thiele, and E. Zitzler. Handling conflicting criteria in embedded system design. IEEE Des. Test. Comput., 17(2):51-59, April-June 2000.
11. T. Koshizen. Improved sensor selection technique by integrating sensor fusion in robot position estimation. Journal of Intelligent and Robotic Systems, 29:79-92, 2000.
12. H. Junker, P. Lukowicz, and G. Troester. Using information theory to design context-sensing wearable systems. accepted for publication as an IEEE Monograph on Sensor Network Operations.
13. Joachim Denzler and Christopher M. Brown. Information theoretic sensor data selection for active object recognition and state estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2):145-157, February 2002.
14. Jihoon Yang and Vasant Honavar. Feature subset selection using a genetic algorithm. IEEE Intelligent Systems, 13:44-49, 1998.
15. Z. Sun, G. Bebis, X. Yuan, and S. Louis. Genetic feature subset selection for gender classification: A comparison study. In IEEE Workshop on Applications of Computer Vision, pages 165-170, Dezember 2002.
16. T. Blickle, J. Teich, and L. Thiele. System-level synthesis using evolutionary algorithms. Design Automation for Embedded Systems, 3(1):23-58, January 1998.
17. L. Thiele, S. Chakraborty, M. Gries, and S. Künzli. Design space exploration of network processor architectures. In Network Processor Design 2002: Design Principles and Practices. Morgan Kaufmann Publishers, 2002.
18. E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In Proc. EUROGEN 2001 - Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems, 2001.
19. R. Battiti. Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5(4):537-550, July 1994.
20. N. Kwak and C. Chong-Ho. Input feature selection for classification problems. IEEE Transactions on Neural Networks, Jan. 2002.
21. R.M. Fano. Class notes for transmission of information, course 6.574. Technical report, MIT, Cambridge, Mass., 1952.
22. H. Junker, P. Lukowicz, and G. Troester. Locomotion analysis using a simple feature derived from force sensitive resistors. In Proceedings Second IASTED International Conference on Biomedical Engineering, 2004, 2004.