Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Implementation of wireless MEMS sensor network for detection of gait events

Loading full text...

Full text loading...

/deliver/fulltext/iet-wss/9/1/IET-WSS.2018.5049.html;jsessionid=12girhqyihlem.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-wss.2018.5049&mimeType=html&fmt=ahah

Inspec keywords: bioMEMS; microsensors; data acquisition; calibration; virtual instrumentation; gait analysis; patient rehabilitation; medical computing; body area networks; accelerometers; body sensor networks; patient monitoring

Other keywords: biophysical parameters; MEMS-based sensor systems; sensor modules; Biometrics Lab System; inertial sensors; LabVIEW software; data acquisition; gravity sensor system; gyro sensor; accelerometer; real-time human health monitoring; calibration; optical system; wireless MEMS sensor network; continuous monitoring; gait event detection; human body; magnetic field angular rate; optical motion systems; timely monitoring; body sensor nodes; XBee-based WSN; wireless body area network; wireless transmission; human gait; real-time ambulatory recording; rehabilitation programmes; gait analysis; home-based rehabilitation; standard system; microelectromechanical system-based wireless sensor network; gait parameters

Subjects: Biomedical communication; Wireless sensor networks; Data handling techniques; Biomedical measurement and imaging; Microsensors and nanosensors; Physics of body movements; Measurement standards and calibration; Patient diagnostic methods and instrumentation; MEMS and NEMS device technology; Biology and medical computing; Micromechanical and nanomechanical devices and systems; Computerised instrumentation; Measurement standards and calibration; Patient care and treatment; Sensing and detecting devices; Computerised instrumentation; Patient care and treatment

References

    1. 1)
      • 10. Ailisto, H., Lindholm, M., Mäntyjärvi, J., et al: ‘Identifying users of portable devices from gait pattern with accelerometers[C]’. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Orlando, USA, 2005, vol. 2, pp. 973976.
    2. 2)
      • 15. Gebre-Egziabher, D., Elkaim, G.H., Powel, J.D., et al: ‘Calibration of strapdown magnetometers in magnetic field domain’, J. Aerosp. Eng., 2006, 19, pp. 87102.
    3. 3)
      • 18. Bergamini, E., Ligorio, G., Summa, A., et al: ‘Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks’, Sensors, 2014, 14, (10), pp. 1862518649.
    4. 4)
      • 6. Rucco, R., Agosti, V., Jacini, F., et al: ‘Spatio-temporal and kinematic gait analysis in patients with frontotemporal dementia and Alzheimer's disease through 3D motion capture’, Gait Posture, 2017, 52, pp. 312317.
    5. 5)
      • 9. Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., et al: ‘Identifying people from gait pattern with accelerometers[C]’. Proc. of SPIE Biometric Technology for Human Identification II, Orlando, USA, March 2005, vol. 5779, pp. 714.
    6. 6)
      • 11. Gouwanda, D., Senanayake, S.M.N.A.: ‘Emerging trends of body-mounted sensors in sports and human gait analysis’. 4th Kuala Lumpur Int. Conf. on Biomedical Engineering, Kuala Lumpar, Malaysia, 2008.
    7. 7)
      • 5. Schutte, L., Narayanan, U., Stout, J.L., et al: ‘An index for quantifying deviations from normal gait’, Gait Posture, 2000, 11, (1), pp. 2531.
    8. 8)
      • 2. Tong, K., Granat, M.H.: ‘A practical gait analysis system using gyroscopes’, Med. Eng. Phys., 1999, 21, (2), pp. 8794.
    9. 9)
      • 17. Palermo, E., Rossi, S., Marini, F., et al: ‘Experimental evaluation of accuracy and repeatability of a novel body-to-sensor calibration procedure for inertial sensor-based gait analyses’, Measurement., 2014, 52, pp. 145155.
    10. 10)
      • 19. Callaway, E., Gorday, P., Hester, L., et al: ‘Home networking with IEEE 802. 15. 4: a developing standard for low-rate wireless personal area networks’, IEEE Commun. Mag., 2002, 40, (8), pp. 7077.
    11. 11)
      • 4. Davis, III, R.B., Ounpuu, S., Tyburski, D., et al: ‘A gait analysis data collection and reduction technique’, Hum. Mov. Sci., 1991, 10, (5), pp. 575587.
    12. 12)
      • 12. Roetenberg, D., Luinge, H.J., Baten, C.T.M., et al: ‘Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation’, IEEE Trans. Neural Syst. Rehabil, 2005, 13, pp. 395405.
    13. 13)
      • 13. Roetenberg, D., Slycke, P.J., Veltink, P.H.: ‘Ambulatory position and orientation tracking fusing magnetic and inertial sensing’, IEEE Trans. Biomed. Eng., 2007, 54, pp. 883890.
    14. 14)
      • 8. Zhang, B., Tian, W., Jin, Z.: ‘Robust appearance-guided particle filter for object tracking with occlusion analysis’, AEU – Int. J. Electron. Commun., 2008, 62, (1), pp. 2432, ISSN 1434-8411.
    15. 15)
      • 7. Kapur, A., Kapur, A., Virji-Babul, N., et al: ‘Gesture-based affective computing on motion capture data’. First Int. Conf. on Affective Computing and Intelligent Interaction (ACII 2005), Beijing, China, 2005, (LNCS, 3784).
    16. 16)
      • 3. Aminian, K., Trevisan, C., Najafi, B., et al: ‘Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement’, Gait Posture, 2004, 20, (1), pp. 102107.
    17. 17)
      • 14. Picerno, P., Cereatti, A., Cappozzo, A.: ‘Joint kinematics estimate using wearable inertial and magnetic sensing modules’, Gait Posture, 2008, 28, pp. 588595.
    18. 18)
      • 16. Palermo, E., Rossi, S., Patanè, F., et al: ‘Experimental evaluation of indoor magnetic distortion effects on gait analysis performed with wearable inertial sensors’, Physiol. Meas., 2014, 35, pp. 399415.
    19. 19)
      • 21. Bogue, R.: ‘Recent developments in MEMS sensors: a review of applications, markets and technologies’, Sens. Rev., 2013, 33, (4), pp. 300304.
    20. 20)
      • 22. Łuczak, S.: ‘Accelerometer-based measurements of axial tilt’, J.Autom. Mobile Robot. Intell. Syst., 2012, 6, (1), pp. 3941.
    21. 21)
      • 20. Gafurov, D., Helkala, K., Søndrol, T.: ‘Biometric gait authentication using accelerometer sensor’, J. Chem. Phys., 2006, 1, (7), pp. 5159.
    22. 22)
      • 1. Medri, E., Tepavac, D., Needham, B., et al: ‘Comprehensive gait analysis in spinal cord injured patients with functional electrical stimulation’. Proc. of the 16th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. Engineering Advances: New Opportunities for Biomedical Engineers, Baltimore, USA, 1994.
    23. 23)
      • 23. Tuck, K.: ‘Tilt sensing using linear accelerometers’, Freescale Semiconductor Application Note AN3107, 2007.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-wss.2018.5049
Loading

Related content

content/journals/10.1049/iet-wss.2018.5049
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address