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Mobile Information Systems
Volume 2015, Article ID 540512, 10 pages
http://dx.doi.org/10.1155/2015/540512
Research Article

Adaptive Power Saving Method for Mobile Walking Guidance Device Using Motion Context

Department of Computer Science and Information Engineering, Inha University, Incheon 22212, Republic of Korea

Received 17 August 2015; Revised 9 November 2015; Accepted 10 November 2015

Academic Editor: Javid Taheri

Copyright © 2015 Jin-Hee Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher, “Activity recognition and monitoring using multiple sensors on different body positions,” in Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks (BSN '06), pp. 114–116, IEEE, Cambridge, Mass, USA, April 2006. View at Publisher · View at Google Scholar
  2. L. Bao and S. S. Intille, “Activity recognition from user-annotated acceleration data,” in Pervasive Computing, A. Ferscha and F. Mattern, Eds., pp. 1–17, Springer, Berlin, Germany, 2004. View at Google Scholar
  3. Y. Wang, J. Lin, M. Annavaram et al., “A framework of energy efficient mobile sensing for automatic user state recognition,” in Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys '09), pp. 179–192, ACM, Kraków, Poland, June 2009. View at Publisher · View at Google Scholar
  4. J. H. Lee, Y. Lee, E. S. Lee, J. S. Park, and B. S. Shin, “Adaptive power control of obstacle avoidance system using via motion context for visually impaired person,” in Proceedings of the International Conference on Cloud Computing and Social Networking (ICCCSN '12), pp. 1–4, Bandung, Indonesia, April 2012.
  5. N. Kern, B. Schiele, and A. Schmidt, “Multi-sensor activity context detection for wearable computing,” in Ambient Intelligence, vol. 2875 of Lecture Notes in Computer Science, pp. 220–232, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  6. A. Krause, D. P. Siewiorek, A. Smailagic, and J. Farringdon, “Unsupervised, dynamic identification of physiological and activity context in wearable computing,” in Proceedings of the 7th IEEE International Symposium on Wearable Computers (ISWC '03), pp. 88–97, White Plains, NY, USA, October 2003. View at Scopus
  7. N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman, “Activity recognition from accelerometer data,” in Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence (IAAI '05), vol. 3, pp. 1541–1546, Pittsburgh, Pa, USA, July 2005.
  8. T. Choudhury, G. Borriello, S. Consolvo et al., “The mobile sensing platform: an embedded activity recognition system,” IEEE Pervasive Computing, vol. 7, no. 2, pp. 32–41, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 156–167, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Mizell, “Using gravity to estimate accelerometer orientation,” in Proceedings of the 7th IEEE International Symposium on Wearable Computers (ISWC '03), pp. 252–253, October 2003. View at Scopus
  11. T. Huynh and B. Schiele, “Analyzing features for activity recognition,” in Proceedings of the Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies (sOc-EUSAI '05), pp. 159–163, Grenoble, France, October 2005. View at Publisher · View at Google Scholar
  12. X. Long, B. Yin, and R. M. Aarts, “Single-accelerometer-based daily physical activity classification,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), pp. 6107–6110, IEEE, Minneapolis, Minn, USA, September 2009. View at Publisher · View at Google Scholar
  13. Z. L. Husz, A. M. Wallace, and P. R. Green, “Human activity recognition with action primitives,” in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '07), pp. 330–335, London, UK, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Nakata, “Recognizing human activities in video by multi-resolutional optical flows,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '06), pp. 1793–1798, Beijing, China, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Zhu and W. Sheng, “Multi-sensor fusion for human daily activity recognition in robot-assisted living,” in Proceedings of the 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI '09), pp. 303–304, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Y. Malik, Power Consumption Analysis of a Modern Smartphone, Lecture Notes in Computer Science, 2012.
  17. G. P. Perrucci, F. H. P. Fitzek, and J. Widmer, “Survey on energy consumption entities on the smartphone platform,” in Proceedings of the IEEE 73rd Vehicular Technology Conference (VTC Spring '11), pp. 1–6, Yokohama, Japan, May 2011. View at Publisher · View at Google Scholar
  18. A. Shye, B. Scholbrock, and G. Memik, “Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures,” in Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-42 '09), pp. 168–178, IEEE, New York, NY, USA, December 2009.
  19. M. Kennedy, A. Ksentini, Y. Hadjadj-Aoul, and G.-M. Muntean, “Adaptive energy optimization in multimedia-centric wireless devices: a survey,” IEEE Communications Surveys and Tutorials, vol. 15, no. 2, pp. 768–786, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Yang and M. Song, “Threshold-based frame-rate adjustment for energy-saving in portable media players,” in Proceedings of the IEEE International Conference on Consumer Electronics (ICCE '15), pp. 50–51, Las Vegas, Nev, USA, January 2015. View at Publisher · View at Google Scholar
  21. M. Kennedy, H. Venkataraman, and G.-M. Muntean, “Dynamic stream control for energy efficient video streaming,” in Proceedings of the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB '11), pp. 1–6, Nuremberg, Germany, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Zhang, Y. Hu, S. Chan, and L.-T. Chia, “Motion context: a new representation for human action recognition,” in Computer Vision—ECCV 2008, vol. 5305 of Lecture Notes in Computer Science, pp. 817–829, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  23. M. Azizyan, I. Constandache, and R. Roy Choudhury, “SurroundSense: mobile phone localization via ambience fingerprinting,” in Proceedings of the 15th Annual ACM International Conference on Mobile Computing and Networking (MobiCom '09), pp. 261–272, Beijing, China, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. S. P. Chatzis, D. I. Kosmopoulos, and T. A. Varvarigou, “Robust sequential data modeling using an outlier tolerant hidden markov model,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1657–1669, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software,” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10–18, 2009. View at Publisher · View at Google Scholar
  26. R. Cross, “Standing, walking, running, and jumping on a force plate,” American Journal of Physics, vol. 67, no. 4, pp. 304–309, 1999. View at Publisher · View at Google Scholar · View at Scopus
  27. D. Winter, “Human balance and posture control during standing and walking,” Gait and Posture, vol. 3, no. 4, pp. 193–214, 1995. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Taherkhani, “Recognizing sorting algorithms with the C4.5 decision tree classifier,” in Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC '10), pp. 72–75, Braga, Portugal, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. C. Ding and X. He, “K-means clustering via principal component analysis,” in Proceedings of the 21st International Conference on Machine Learning (ICML '04), pp. 225–232, ACM, Alberta, Canada, July 2004. View at Publisher · View at Google Scholar
  30. H.-L. Lou, “Implementing the Viterbi algorithm,” IEEE Signal Processing Magazine, vol. 12, no. 5, pp. 42–52, 1995. View at Publisher · View at Google Scholar · View at Scopus
  31. J.-E. Kim, J. Han, and C.-G. Lee, “Optimal 3-coverage with minimum separation requirements for ubiquitous computing environments,” Mobile Networks and Applications, vol. 14, no. 5, pp. 556–570, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. J.-H. Lee, E. Choi, S. Lim, and B.-S. Shin, “Wearable computer system reflecting spatial context,” in Proceedings of the 1st IEEE International Workshop on Semantic Computing and Applications (IWSCA '08), pp. 153–159, Incheon, Republic of Korea, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Lee and M. Song, “Selective device activation for power reduction in accelerometer-based wearable guidance systems for the blind,” in Proceedings of the IEEE International Conference on Consumer Electronics (ICCE '12), pp. 106–107, Las Vegas, Nev, USA, January 2012. View at Publisher · View at Google Scholar · View at Scopus