Analysis of a Mobile System to Register the Kinematic Parameters in Ankle, Knee, and Hip based in Inertial Sensors

Víctor H. Flores-Morales, Byron G. Contreras-Bermeo, Freddy L. Bueno-Palomeque, Luis J. Serpa-Andrade

Abstract

Understanding the lower-extremity kinematic during daily and sport activities provides important information in order to detect abnormalities in human gait or analyse the execution of different sport techniques. Following this approach, this paper presents a kinematic data collection system of human gait in the lower extremities using six inertial sensors MPU 6050 and a microcontroller ATMEGA328P-PU. Six tests were performed and the angular variation was recorded during the execution. The curves obtained during the tests showed a maximum error of ± 4, ± 1, and -4 degrees at the Yaw, Pitch, and Roll angles respectively. This study proposes a mobile and inexpensive system for detecting the angular variation in reduced speed movements, ideal for goniometric measurement or analyse the techniques in certain sports.

References

  1. Alonge, F., Cucco, E., D'Ippolito, F., and Pulizzotto, A. (2014). The use of accelerometers and gyroscopes to estimate hip and knee angles on gait analysis. Sensors, 14(5):8430-8446.
  2. Balsalobre-Fernández, C., Tejero-González, C. M., del Campo-Vecino, J., and Bavaresco, N. (2014). The concurrent validity and reliability of a low-cost, highspeed camera-based method for measuring the flight time of vertical jumps. The Journal of Strength & Conditioning Research, 28(2):528-533.
  3. Callaway, A. J. (2015). Measuring kinematic variables in front crawl swimming using accelerometers: A validation study. Sensors, 15(5):11363-11386.
  4. Chambers, R., Gabbett, T. J., Cole, M. H., and Beard, A. (2015). The use of wearable microsensors to quantify sport-specific movements. Sports Medicine, pages 1- 17.
  5. Chan, M., Estève, D., Fourniols, J.-Y., Escriba, C., and Campo, E. (2012). Smart wearable systems: Current status and future challenges. Artificial intelligence in medicine, 56(3):137-156.
  6. Dadashi, F., Crettenand, F., Millet, G., Seifert, L., Komar, J., and Aminian, K. (2011). Front crawl propulsive phase detection using inertial sensors. Port. J. Sport Sci, 11:855-858.
  7. Foerster, F., Smeja, M., and Fahrenberg, J. (1999). Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. Computers in Human Behavior, 15(5):571-583.
  8. Hobert, M. A., Maetzler, W., Aminian, K., and Chiari, L. (2014). Technical and clinical view on ambulatory assessment in parkinson's disease. Acta Neurologica Scandinavica, 130(3):139-147.
  9. Lattes, M., Gatti, R., Roa, Y., Fruett, F., and Cunha, S. (2013). Assessment of micro sensor (sm-mae) in monitoring of cycling. In XXIV Congress of the International Society of Biomechanics.
  10. Lee, J. B., Burkett, B. J., Thiel, D. V., and James, D. A. (2011). Inertial sensor, 3d and 2d assessment of stroke phases in freestyle swimming. Procedia Engineering, 13:148-153.
  11. Liu, K., Liu, T., Shibata, K., Inoue, Y., and Zheng, R. (2009). Novel approach to ambulatory assessment of human segmental orientation on a wearable sensor system. Journal of biomechanics, 42(16):2747-2752.
  12. Mangin, M., Valade, A., Costes, A., Bouillod, A., Acco, P., and Soto-Romero, G. (2015). An instrumented glove for swimming performance monitoring. In Proceedings of the 3rd International Congress on Sport Sciences Research and Technology Support, pages 53- 58.
  13. Rueterbories, J., Spaich, E. G., Larsen, B., and Andersen, O. K. (2010). Methods for gait event detection and analysis in ambulatory systems. Medical engineering & physics, 32(6):545-552.
  14. Shi, G., Chan, C. S., Li, W. J., Leung, K.-S., Zou, Y., and Jin, Y. (2009). Mobile human airbag system for fall protection using mems sensors and embedded svm classifier. Sensors Journal, IEEE, 9(5):495-503.
  15. Turcot, K., Aissaoui, R., Boivin, K., Pelletier, M., Hagemeister, N., De Guise, J., et al. (2008). New accelerometric method to discriminate between asymptomatic subjects and patients with medial knee osteoarthritis during 3-d gait. Biomedical Engineering, IEEE Transactions on, 55(4):1415-1422.
  16. Wong, W. Y., Wong, M. S., and Lo, K. H. (2007). Clinical applications of sensors for human posture and movement analysis: a review. Prosthetics and orthotics international, 31(1):62-75.
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Paper Citation


in Harvard Style

Flores-Morales V., Contreras-Bermeo B., Bueno-Palomeque F. and Serpa-Andrade L. (2016). Analysis of a Mobile System to Register the Kinematic Parameters in Ankle, Knee, and Hip based in Inertial Sensors . In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-758-205-9, pages 29-33. DOI: 10.5220/0005934800290033


in Bibtex Style

@conference{icsports16,
author={Víctor H. Flores-Morales and Byron G. Contreras-Bermeo and Freddy L. Bueno-Palomeque and Luis J. Serpa-Andrade},
title={Analysis of a Mobile System to Register the Kinematic Parameters in Ankle, Knee, and Hip based in Inertial Sensors},
booktitle={Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,},
year={2016},
pages={29-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005934800290033},
isbn={978-989-758-205-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,
TI - Analysis of a Mobile System to Register the Kinematic Parameters in Ankle, Knee, and Hip based in Inertial Sensors
SN - 978-989-758-205-9
AU - Flores-Morales V.
AU - Contreras-Bermeo B.
AU - Bueno-Palomeque F.
AU - Serpa-Andrade L.
PY - 2016
SP - 29
EP - 33
DO - 10.5220/0005934800290033