Impact of on-body IMU placement on inertial navigation

Impact of on-body IMU placement on inertial navigation

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Wireless Sensor Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Even though technology-aided personal navigation is an extensively studied research topic, approaches based on inertial sensors remain challenging. In this study, the authors present a comparison between different inertial systems, investigating the impacts of on-body placement of Inertial Measurement Units (IMUs) and, consequently, of different algorithms for the estimation of the travelled path on the navigation accuracy. In particular, the system performance is investigated considering two IMU placements: (i) on the feet and (ii) on the lower back. Sensor fusion is then considered in order to take advantage of the strengths of each placement. The results are validated through an extensive data collection in indoor and outdoor environments.


    1. 1)
      • 1. Segura, M., Mut, V., Sisterna, C.: ‘Ultra wideband indoor navigation system’, IET Radar Sonar Navig., 2012, 6, (5), pp. 402411.
    2. 2)
      • 2. Pagano, S., Peirani, S., Valle, M.: ‘Indoor ranging and localisation algorithm based on received signal strength indicator using statistic parameters for wireless sensor networks’, IET Wirel. Sens. Syst., 2015, 5, (5), pp. 243249.
    3. 3)
      • 3. Fan, Q., Sun, B., Sun, Y., et al: ‘Performance enhancement of MEMS-based INS/UWB integration for indoor navigation applications’, IEEE Sens. J., 2017, 17, (10), pp. 31163130.
    4. 4)
      • 4. Hu, W.Y., Lu, J.L., Jiang, S., et al: ‘WiBEST: a hybrid personal indoor positioning system’. Wireless Communications and Networking Conf. (WCNC 2013), Shanghai, China, 2013, pp. 21492154.
    5. 5)
      • 5. Misra, P., Enge, P.: ‘Global positioning system: signals, measurements and performance’ (Ganga-Jamuna Press, Lincoln, 2010, 2nd edn.).
    6. 6)
      • 6. Benedicto, J., Dinwiddy, S.E., Gatti, G., et al: ‘Galileo: satellite system design and technology developments’ (European Space Agency (ESA), Noordwijk, NL, 2000).
    7. 7)
      • 7. Camplani, M., Paiement, A., Mirmehdi, M., et al: ‘Multiple human tracking in RGB-depth data: a survey’, IET Comput. Vis., 2017, 11, (4), pp. 265285.
    8. 8)
      • 8. Strozzi, N., Parisi, F., Ferrari, G.: ‘On single sensor-based inertial navigation’. IEEE EMBS 13th Annual Int. Body Sensor Networks Conf. (BSN 2016), San Francisco, CA, 2016.
    9. 9)
      • 9. Strozzi, N., Parisi, F., Ferrari, G.: ‘A multifloor hybrid inertial/barometric navigation system’. IEEE Int. Conf. Indoor Positioning and Indoor Navigation (IPIN 2016), Alcala de Henares, ES, 2016.
    10. 10)
      • 10. Beauregard, S., Haas, H.: ‘Pedestrian dead reckoning:a basis for personal positioning’. Workshop on Positioning, Navigation and Communication (WPNC 2006), Hannover, Germany, 2006.
    11. 11)
      • 11. Fourati, H.: ‘Heterogeneous data fusion algorithm for pedestrian navigation via foot-mounted inertial measurement unit and complementary filter’, IEEE Trans. Instrum. Meas., 2015, 64, (1), pp. 221229.
    12. 12)
      • 12. Hsu, Y.L., Wang, J.S., Chang, C.W.: ‘A wearable inertial pedestrian navigation system with quaternion-based extended Kalman filter for pedestrian localization’, IEEE Sens. J., 2017, 17, (10), pp. 31933206.
    13. 13)
      • 13. Jimenez, A.R., Seco, F., Prieto, C., et al: ‘A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU’. Intelligent Signal Processing (WISP 2009), Budapest, Hungary, 2009, pp. 3742.
    14. 14)
      • 14. Alvarez, J.C., Alvarez, D., Lopez, A.M., et al: ‘Pedestrian navigation based on a waist-worn inertial sensor’, Sensors, December 2012, 12, pp. 1053610549.
    15. 15)
      • 15. Basso, M., Galanti, M., Innocenti, G., et al: ‘Pedestrian dead reckoning based on frequency self-synchronization and body kinematics’, IEEE Sens. J., 2017, 17, (2), pp. 534545.
    16. 16)
      • 16. Weinberg, H.: ‘Using the adxl202 in pedometer and personal navigation applications’. Analog Devices, 2002, Application Note AN-602.
    17. 17)
      • 17. Zijlstra, W., Hof, A.L.: ‘Assessment of spatio-temporal gait parameters from trunk accelerations during human walking’, Gait Posture, 2003, 18, pp. 110.
    18. 18)
      • 18. Gonzalez, R.C., Alvarez, D., Lopez, A.M., et al: ‘Modified pendulum model for mean step length estimation’. 2007 29th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, 2007, pp. 13711374.
    19. 19)
      • 19. Madgwick, S.O.H., Harrison, A.J.L., Vaidyanathan, R.: ‘Estimation of IMU and MARG orientation using a gradient descent algorithm’. IEEE Int. Conf. Rehabilitation Robotics Rehab Week (ICORR 2011), ETH Zurich Science City, Switzerland, 2011, pp. 17.

Related content

This is a required field
Please enter a valid email address