References

• 1. 1)
     • 17. Yan, Z., Subbaraju, V., Chakraborty, D., Misra, A., Aberer, K.: ‘Energy-efficient continuous activity recognition on mobile phones, an activity-adaptive approach’. Proc. of the 16th Int. Symp. on wearable computers, Newcastle, 2012, pp. 17–24.
  2. 2)
     • 5. Patel, M., Wang, J.: ‘Applications, challenges, and prospective in emerging body area networking technologies’, IEEE Wirel. Commun., 2010, 17, (1), pp. 80–88 (doi: 10.1109/MWC.2010.5416354).
  3. 3)
     • 3. Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: ‘Machine recognition of human activities: A survey’, IEEE Trans. Circuits Syst. Video Technol., 2008, 18, (11), pp. 1473–1488 (doi: 10.1109/TCSVT.2008.2005594).
  4. 4)
     • 5. Joseph, C.N., Kokulakumaran, S., Srijeyanthan, K., Thusyanthan, A., Gunasekara, C., Gamage, C.: ‘A framework for whole-body gesture recognition from video feeds’. Int. Conf. on Industrial and Information Systems (ICIIS), Dalian, China, July 2010, pp. 430–435.
  5. 5)
     • 14. Villalba, E., Ottaviano, M., Arredondo, M., Martinez, A., Guillen, S.: ‘Wearable monitoring system for heart failure assessment in a mobile environment’, Comput. Cardiol., Valencia, Spain, September 2006, pp. 237–240.
  6. 6)
     • 15. Ghasemzadeh, H., Amini, N., Saeedi, R., Sarrafzadeh, M.: ‘Power-aware computing in wearable sensor networks: an optimal feature selection’, IEEE Trans. Mobile Comput., 2014, 14, (4), pp. 800–812 (doi: 10.1109/TMC.2014.2331969).
  7. 7)
     • 19. French, B., Siewiorek, D., Smailagic, A., Deisher, M.: ‘Selective sampling strategies to conserve power in context aware devices’. 11th IEEE Int. Symp. on Wearable Computers, Boston, MA, USA, October 2007, pp. 77–80.
  8. 8)
     • 22. Mallinson, M., Drane, P., Hussain, S.: ‘Discrete radio power level consumption model in wireless sensor networks’, Second Int. Workshop on Information Fusion and Dissemination in Wireless Sensor Networks (SensorFusion), 2007.
  9. 9)
     • 8. Stiefmeier, T., Roggen, D., Ogris, G., Lukowicz, P., Tr¨oster, G.: ‘Wearable activity tracking in car manufacturing’, IEEE Pervasive Comput. Mag., 2008, 7, pp. 42–50 (doi: 10.1109/MPRV.2008.40).
  10. 10)
      • 4. Bodor, R., Jackson, B., Papanikolopoulos, N., Tracking, H.: ‘Visionbased human tracking and activity recognition’. Proc. of the 11th Mediterranean Conf. on Control and Automation. Kostrzewa Joseph, 2003, pp. 18–20.
  11. 11)
      • 12. Jafari, R., Li, W., Bajcsy, R., Glaser, S., Sastry, S.: ‘Physical activity monitoring for assisted living at home’. Proc. of Int. Workshop on Wearable and Implantable Body Sensor Networks (BSN). Germany, 2004.
  12. 12)
      • 11. Bourke, A., Obrien, J., Lyons, G.: ‘Evaluation of a threshold based tri-axial accelerometer fall detection algorithm’, Gait Posture, 2007, 26, (2), pp. 194–199 (doi: 10.1016/j.gaitpost.2006.09.012).
  13. 13)
      • 10. Anliker, U., Ward, J., Lukowicz, P., et al: ‘Amon: a wearable multiparameter medical monitoring and alert system’, IEEE Trans. Inf. Technol. Biomed., 2004, 8, (4), pp. 415–427 (doi: 10.1109/TITB.2004.837888).
  14. 14)
      • 9. Shotton, J., Fitzgibbon, A., Cook, M., et al: ‘Real-time human pose recognition in parts from single depth images’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Santa Fe, NM, USA, October 2011, pp. 277–285.
  15. 15)
      • 6. Chi, E.H., Song, J., Corbin, G.: ‘Killer app of wearable computing: wireless force sensing body protectors for martial arts’. Proc. of the 17th Annual ACM Conf. on User Interface Software and Technology, Santa Fe, NM, USA, October 2004, pp. 277–285.
  16. 16)
      • 20. ‘Sun SPOT website’, http://www.sunspotworld.com, accessed 2nd December 2014.
  17. 17)
      • 23. ‘Weka: Data Mining Software in Java’, http://www.cs.waikato.ac.nz/ml/weka, accessed 12nd December 2014.
  18. 18)
      • 21. Atallah, L., Lo, B., King, R., Yang, G.: ‘Sensor positioning for activity recognition using wearable accelerometer’, Trans. Biomed. Circuits Syst., 2011, 5, (4), pp. 320–329 (doi: 10.1109/TBCAS.2011.2160540).
  19. 19)
      • 13. Liszka, K., Mackin, M.A., Lichter, M.J., York, D.W., Pillai, D., Rosenbaum, D.S.: ‘Keeping a beat on the heart’, IEEE Pervasive Comput., 2004, 3, (4), pp. 42–49 (doi: 10.1109/MPRV.2004.10).
  20. 20)
      • 16. Chu, D., Lane, N.D., Lai, T.T., et al: ‘Balancing energy, latency and accuracy for mobile sensor data classification’. Proc. of the Ninth ACM Conf. on Embedded Networked Sensor Systems, ser. SenSys 11. New York, NY, USA, 2011, pp. 54–67.
  21. 21)
      • 7. Barry, M., Gutknecht, J., Kulka, I., Lukowicz, P., Stricker, T.: ‘From motion to emotion: a wearable system for the multimedial enhancement of a butoh dance performance’, J. Mobile Multimedia, 2005, 1, (2), pp. 112–132.
  22. 22)
      • 18. Zappi, P., Lombriser, C., Stiefmeier, T., et al: ‘Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection’, Lect. Notes Comput. Sci., 2008, 4913, pp. 17–33 (doi: 10.1007/978-3-540-77690-1_2).
  23. 23)
      • 2. Xu, X., Tang, J., Zhang, X., Liu, X., Zhang, H., Qiu, Y.: ‘Exploring techniques for vision based human activity recognition: Methods, systems, and evaluation’, Sensors, 2013, 13, (2), pp. 1635–1650 (doi: 10.3390/s130201635).