MEMS based wireless sensor network (WSN) for real-time human health monitoring is promising in home-based rehabilitation. It requires effective integration of a number of MEMS sensors and their placement on the human body to create a wireless body area network for continuous and timely monitoring of various biophysical parameters. This study attempts to develop an XBee-based WSN for real-time monitoring of human gait. Traditionally, the optical motion systems were used for gait analysis but they suffer from certain limitations such as the development of the complex algorithm and constrains in the work space. In comparison to the optical system, the attractive benefits of MEMS-based sensor systems are small size and low cost. Magnetic field angular rate and gravity sensor system can perform a complete analysis of the human gait. The sensor modules were developed using the inertial sensors mainly accelerometer and gyro sensor. LabVIEW software is used for data acquisition from the body sensor nodes and gait analysis. Biometrics Lab System is used as the standard system for calibration of data obtained from sensors. The joint angle range of motion was calculated using both the systems. The advantage of the proposed system is that it facilitates wireless transmission of gait parameters (joint angle measurement) for easy monitoring of human gait in various rehabilitation programmes.

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. 973–976.
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. 87–102.
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. 18625–18649.
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. 312–317.
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. 7–14.
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. 25–31.
8. 8)
  - 2. Tong, K., Granat, M.H.: ‘A practical gait analysis system using gyroscopes’, Med. Eng. Phys., 1999, 21, (2), pp. 87–94.
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. 145–155.
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. 70–77.
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. 575–587.
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. 395–405.
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. 883–890.
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. 24–32, 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. 102–107.
17. 17)
  - 14. Picerno, P., Cereatti, A., Cappozzo, A.: ‘Joint kinematics estimate using wearable inertial and magnetic sensing modules’, Gait Posture, 2008, 28, pp. 588–595.
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. 399–415.
19. 19)
  - 21. Bogue, R.: ‘Recent developments in MEMS sensors: a review of applications, markets and technologies’, Sens. Rev., 2013, 33, (4), pp. 300–304.
20. 20)
  - 22. Łuczak, S.: ‘Accelerometer-based measurements of axial tilt’, J.Autom. Mobile Robot. Intell. Syst., 2012, 6, (1), pp. 39–41.
21. 21)
  - 20. Gafurov, D., Helkala, K., Søndrol, T.: ‘Biometric gait authentication using accelerometer sensor’, J. Chem. Phys., 2006, 1, (7), pp. 51–59.
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.

Implementation of wireless MEMS sensor network for detection of gait events

References

Related content