全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
Sensors  2013 

Design of a Wearable Sensing System for Human Motion Monitoring in Physical Rehabilitation

DOI: 10.3390/s130607735

Keywords: human motion monitoring, wireless body area networks, sensors, accelerometers, communication protocol, data synchronization, physical rehabilitation

Full-Text   Cite this paper   Add to My Lib

Abstract:

Human motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, is one of the current challenges. In this paper we present a wearable multi-sensor system for human motion monitoring, which has been developed for use in rehabilitation. It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device. The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking, as required, for example, in activity recognition, physical/athletic performance evaluation and rehabilitation.

References

[1]  Zhou, H.; Hu, H. Human motion tracking for rehabilitation—A survey. Biomed. Signal Process. Control 2008, 3, 1–18, doi:10.1016/j.bspc.2007.09.001.
[2]  Patel, S.; Park, H.; Bonato, P.; Chan, L.; Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuro Eng. Rehabil. 2012, 9, 1–17, doi:10.1186/1743-0003-9-1.
[3]  Bonato, P. Advances in wearable technology and applications in physical medicine and rehabilitation. J. NeuroEng. Rehiabil. 2005, 2, 1–4, doi:10.1186/1743-0003-2-1.
[4]  Moreno-Hagelsieb, L.; Tang, X.; Bulteel, O.; Overstraeten-Schlo?gel, N.V.; Andreé, N.; Dupuis, P.; Raskin, J.P.; Francis, L.; Flandre, D.; Fonteyne, P.; et al. Miniaturized and Low Cost Innovative Detection Systems for Medical and Environmental Applications. Proceedings of the 2nd Workshop on Circuits and Systems for Medical and Environmental Applications, Merida, Yucatan, Mexico, 13–15 December 2010; pp. 1–4.
[5]  Latré, B.; Braem, B.; Moerman, I.; Blondia, C.; Demeester, P. A survey on wireless body area networks. Wirel. Netw. 2011, 17, 1–18, doi:10.1007/s11276-010-0252-4.
[6]  Yang, J.; Wang, S.; Chen, N.; Chen, X.; Shi, P. Wearable Accelerometer Based Extendable Activity Recognition System. Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, 3–7 May 2010; pp. 3641–3647.
[7]  Guler, M.; Ertugrul, S. Measuring and Transmitting Vital Body Signs Using MEMS Sensors. Proceedings of the 1st Annual RFID Eurasia, Istanbul, Turkey, 5–6 September 2007; pp. 1–4.
[8]  Zhou, H.; Hu, H.; Tao, Y. Inertial measurements of upper limb motion. Med. Biol. Eng. Comput. 2006, 44, 479–487, doi:10.1007/s11517-006-0063-z. 16937199
[9]  Zhou, H.; Stone, T.; Hu, H.; Harris, N. Use of multiple wearable inertial sensors in upper limb motion tracking. Med. Eng. Phys. 2008, 30, 123–133, doi:10.1016/j.medengphy.2006.11.010. 17251049
[10]  Sung, M.; Marci, C.; Pentland, A. Wearable feedback systems for rehabilitation. J. NeuroEng. Rehabil. 2005, 2, 17, doi:10.1186/1743-0003-2-17.
[11]  Henesis WiModule. Available online: http://www.henesis.eu/prod-wimodule-eng.htm (accessed on 28 March 2013).
[12]  LAN/MAN Standards Committee–IEEE Computer Society. IEEE Standard for Local and Metropolitan Area Networks–Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs); 2011.
[13]  Tao, W.; Liu, T.; Zheng, R.; Feng, H. Gait analysis using wearable sensors. Sensors 2012, 12, 2255–2283, doi:10.3390/s120202255. 22438763
[14]  Yang, C.C.; Hsu, Y.L. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 2010, 10, 7772–7788, doi:10.3390/s100807772. 22163626
[15]  Bouten, C.; Koekkoek, K.; Verduin, M.; Kodde, R.; Janssen, J. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans. Biomed. Eng. 1997, 44, 136–147, doi:10.1109/10.554760. 9216127
[16]  Ravi, N.; Nikhil, D.; Mysore, P.; Littman, M.L. Activity Recognition from Accelerometer Data. Proceedings of the 17th Conf. on Innovative Applications of Artificial Intelligence, Pittsburgh, Pennsylvania, USA, 9–13 July 2005; Volume 3, pp. 1541–1546.
[17]  Milenkovi?, A.; Otto, C.; Jovanov, E. Wireless sensor networks for personal health monitoring: Issues and an implementation. Comput. Commun. 2006, 29, 2521–2533, doi:10.1016/j.comcom.2006.02.011.
[18]  Ahmed, S.; Chen, T. Minimizing the effect of sampling jitters in wireless sensor networks. IEEE Signal Process. Lett. 2011, 18, 219–222, doi:10.1109/LSP.2011.2109711.
[19]  Sivrikaya, F.; Yener, B. Time synchronization in sensor networks: A survey. IEEE Netw. 2004, 18:4, 45–50.
[20]  Elson, J.; Girod, L.; Estrin, D. Fine-grained Network Time Synchronization Using Reference Broadcasts. Proceedings of the 5th Symposium on Operating Systems Design and Implementation, Boston, MA, USA, 9–11 December 2002; volume 36, pp. 147–163.
[21]  Paavola, M.; Kemppainen, J. Wireless Monitoring of a Steam Boiler-performance Measurements in Industrial Environment. Proceedings of the IEEE International Symposium on Industrial Electronics, Cambridge, United Kingdom, 30 June–2 July 2008; pp. 1166–1171.
[22]  Lee, J.S. Performance evaluation of IEEE 802.15.4 for low-rate wireless personal area networks. IEEE Trans. Consum. Electron. 2006, 52, 742–749, doi:10.1109/TCE.2006.1706465.
[23]  Mo, L.; Liu, S.; Gao, R.; John, D.; Staudenmayer, J.; Freedson, P. Wireless design of a multi-sensor system for physical activity monitoring. IEEE Trans. Biomed. Eng. 2012, 59, 3230–3237, doi:10.1109/TBME.2012.2208458. 23086196
[24]  Kunze, K.; Lukowicz, P. Using Acceleration Signatures from Everyday Activities for On-body Device Location. Proceedings of the 11th IEEE International Symposium on Wearable Computers, Boston, MA, USA, 11–13 October 2007; pp. 115–116.
[25]  XSens Xbus Kit: Measurement of human motion. Available online: http://www.xsens.com/en/general/xbus-kit (accessed on 8 May 2013).
[26]  González-Villanueva, L.; Chiesi, L.; Mussi, L. Wireless Human Motion Acquisition System for Rehabilitation Assessment. Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, Rome, Italy, 20–22 June 2012.
[27]  Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA data mining software: An update. SIGKDD Explor. Newslett. 2009, 11, 10–18, doi:10.1145/1656274.1656278.
[28]  Quinlan, J.R. C4.5: Programs for Machine Learning; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 1993.
[29]  Platt, J. Fast Training of Support Vector Machines using Sequential Minimal Optimization; Advances in Kernel Methods–Support Vector Learning, MIT Press: Cambridge, MA, USA, 1998.
[30]  Mitchell, T. Machine Learning; McGraw-Hill, Inc.: New York, NY, USA, 1997.
[31]  Nayak, N.N.; Shankar, K. Yoga: A therapeutic approach. Phys. Med. Rehabil. Clin. N. Am. 2004, 15, 783–798, doi:10.1016/j.pmr.2004.04.004. 15458752
[32]  Hart, C.E.F.; Tracy, B.L. Yoga as steadiness training: Effects on motor variability in young adults. J. Strength Cond. Res. 2008, 22, 1659–1669, doi:10.1519/JSC.0b013e31818200dd. 18714217
[33]  Platz, T.; Brown, R.G.; Marsden, C.D. Training improves the speed of aimed movements in Parkinson's disease. Brain 1998, 121, 505–514, doi:10.1093/brain/121.3.505. 9549526
[34]  Majsak, M.J.; Kaminski, T.; Gentile, A.M.; Flanagan, J.R. The reaching movements of patients with Parkinson's disease under self-determined maximal speed and visually cued conditions. Brain 1998, 121, 755–766, doi:10.1093/brain/121.4.755. 9577399
[35]  Alvarez-Alvarez, A.; Trivino, G.; Cordón, O. Human gait modeling using a genetic fuzzy finite state machine. IEEE Trans. Fuzzy Syst. 2012, 20, 205–223, doi:10.1109/TFUZZ.2011.2171973.
[36]  González-Villanueva, L.; Alvarez-Alvarez, A.; Ascari, L.; Trivino, G. Computational Model of Human Body Motion Performing a Complex Exercise by Means of a Fuzzy Finite State Machine. Proceedings of the International Conference on Medical Imaging Using Bio-Inspired and Soft Computing (MIBISOC), Brussels, Belgium, 15–17 May 2013.
[37]  Carroll, C.M.; Dixon, C.B. The effect of feedback on exercise performance in recreationally-active young adults. Keyst. J. Undergrad. Res. 2011, 1, 37–40.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133