Advertisement

Gait Analysis for Physical Rehabilitation via Body-Worn Sensors and Multi-information Fusion

  • Sen QiuEmail author
  • Zhelong Wang
  • Hongyu Zhao
  • Long Liu
  • Jiaxin Wang
  • Jie Li
Conference paper
Part of the Internet of Things book series (ITTCC)

Abstract

How to effectively use wearable sensors for medical rehabilitation is an interdisciplinary research hotspot of control subjects and biomedical engineering. This paper intends to integrate accelerometer, gyroscope and magnetometer to build a low-cost, intelligent and lightweight wearable human gait analysis platform. On account of complexity and polytopes of walking motion characteristics, the key is to solve the existing robustness and adaptability problems of current gait analysis algorithm. This project is starting from the sensor physical properties and human physiology structure, aiming to establish lower limb kinematics model constraint, and solving the applicability problem of the traditional zero velocity update algorithm. Digital filter and error correction of gait parameters could be done with multi-level data fusion algorithm. Preliminary clinical gait experiments results indicated the proposed method has great potential as an auxiliary for medical rehabilitation. The ultimate target is to realize auxiliary diagnosis and exercise rehabilitation plan formulation for patients with abnormal gait.

Keywords

Human gait analysis Biological information detection Multi-information fusion Exercise rehabilitation Micro-electro-mechanical sensor 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61473058, and in part by the Fundamental Research Funds for the Central Universities under Grant DUT18RC(4)034. This project was supported by China Postdoctoral Science Foundation under Grant 2017M621132 and 2017M621131. The authors would like to express their thanks to these funding bodies.

References

  1. 1.
    Verghese, J., Holtzer, R., Lipton, R.B., Wang, C.: Quantitative gait markers and incident fall risk in older adults. J. Gerontol. Ser. A: Biol. Sci. Med. Sci. 64(8), 896–901 (2009)CrossRefGoogle Scholar
  2. 2.
    Senden, R., Savelberg, H., Grimm, B., Heyligers, I., Meijer, K.: Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling. Gait Posture 36(2), 296–300 (2012)CrossRefGoogle Scholar
  3. 3.
    Mortaza, N., Abu Osman, N., Mehdikhani, N.: Are the spatio-temporal parameters of gait capable of distinguishing a faller from a non-faller elderly. Eur. J. Phys. Rehabil. Med. 50(6), 677–691 (2014)Google Scholar
  4. 4.
    Zhou, H., Hu, H.: Reducing drifts in the inertial measurements of wrist and elbow positions. IEEE Trans. Instrum. Meas. 59(3), 575–585 (2010)CrossRefGoogle Scholar
  5. 5.
    Prakash, C., Gupta, K., Mittal, A., Kumar, R., Laxmi, V.: Passive marker based optical system for gait kinematics for lower extremity. Proc. Comput. Sci. 45(3), 176–185 (2015)CrossRefGoogle Scholar
  6. 6.
    Park, S.Y., Lee, S.Y., Kang, H.C., Kim, S.M.: EMG analysis of lower limb muscle activation pattern during pedaling: experiments and computer simulations. Int. J. Precis. Eng. Manuf. 13(4), 601–608 (2012)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Zhao, C., Qiu, S.: A system of human vital signs monitoring and activity recognition based on body sensor network. Sens. Rev. 34(1), 42–50 (2014)CrossRefGoogle Scholar
  8. 8.
    Qiu, S., Wang, Z., Zhao, H., Liu, L., Li, J., Jiang, Y., Fortino, G.: Body sensor network based robust gait analysis: toward clinical and at home use. IEEE Sens. J. 1–9 (2018)Google Scholar
  9. 9.
    Yu, L., Zheng, J., Wang, Y., Song, Z., Zhan, E.: Adaptive method for real-time gait phase detection based on ground contact forces. Gait Posture 41(1), 269–275 (2015)CrossRefGoogle Scholar
  10. 10.
    Qiu, S., Wang, Z., Zhao, H., Hu, H.: Using distributed wearable sensors to measure and evaluate human lower limb motions. IEEE Trans. Instrum. Meas. 65(4), 939–950 (2016)CrossRefGoogle Scholar
  11. 11.
    Chen, S., Lach, J., Member, S., Lo, B., Member, S.: Sensors: a systematic review. IEEE J. Biomed. Health Inf. 20(6), 1521–1537 (2016)CrossRefGoogle Scholar
  12. 12.
    Qiu, S., Wang, Z., Zhao, H., Liu, L., Jiang, Y.: Using body-worn sensors for preliminary rehabilitation assessment in stroke victims with gait impairment. IEEE Access 6, 31249–31258 (2018)CrossRefGoogle Scholar
  13. 13.
    Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., Jafari, R.: Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Trans. Hum. Mach. Syst. 43(1), 115–133 (2013)CrossRefGoogle Scholar
  14. 14.
    Qiu, S., Yang, Y., Hou, J., Ji, R., Hu, H., Wang, Z.: Ambulatory estimation of 3D walking trajectory and knee joint angle using MARG sensors. In: Fourth International Conference on Innovative Computing Technology (INTECH), pp. 191–196 (2014)Google Scholar
  15. 15.
    Wu, D., Wang, Z., Chen, Y., Zhao, H.: Mixed-kernel based weighted extreme learning machine for inertial sensor based human activity recognition with imbalanced dataset. Neurocomputing 190, 35–49 (2016)CrossRefGoogle Scholar
  16. 16.
    Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G.: Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf. Fusion 35, 68–80 (2016)CrossRefGoogle Scholar
  17. 17.
    Wang, Z., Qiu, S., Cao, Z., Jiang, M.: Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network. Sens. Rev. 33(1), 48–56 (2013)CrossRefGoogle Scholar
  18. 18.
    Qiu, S., Wang, Z., Zhao, H.: Heterogeneous data fusion for three-dimensional gait analysis using wearable MARG sensors. Int. J. Comput. Sci. Eng. 14(3), 222–233 (2017)Google Scholar
  19. 19.
    Qiu, S., Wang, Z., Zhao, H., Qin, K., Li, Z., Hu, H.: Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion. Inf. Fusion 39, 108–119 (2018)CrossRefGoogle Scholar
  20. 20.
    Farris, R.J., Quintero, H.A., Murray, S.A., Member, S., Ha, K.H., Hartigan, C., Goldfarb, M.: A preliminary assessment of legged mobility provided by a lower limb exoskeleton for persons with paraplegia. IEEE Trans. Neural Syst. Rehabil. Eng. 22(3), 482–490 (2014)CrossRefGoogle Scholar
  21. 21.
    Bamberg, S.J.M., Benbasat, A.Y., Scarborough, D.M., Krebs, D.E., Paradiso, J.A.: Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed. 12(4), 413–23 (2008)CrossRefGoogle Scholar
  22. 22.
    Favre, J., Aissaoui, R., Jolles, B.M., de Guise, J.A., Aminian, K.: Functional calibration procedure for 3D knee joint angle description using inertial sensors. J. Biomech. 42(14), 2330–2335 (2009)CrossRefGoogle Scholar
  23. 23.
    Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors, pp. 1–9. XSENS TECHNOLOGIES (2013)Google Scholar
  24. 24.
    Wang, Z., Li, J., Wang, J., Zhao, H., Qiu, S., Yang, N., Shi, X.: Inertial sensor-based analysis of equestrian sports between beginner and professional riders under. IEEE Trans. Instrum. Meas. 14(8), 1–13 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sen Qiu
    • 1
    Email author
  • Zhelong Wang
    • 1
  • Hongyu Zhao
    • 1
  • Long Liu
    • 1
  • Jiaxin Wang
    • 1
  • Jie Li
    • 1
  1. 1.School of Control Science and EngineeringDalian University of TechnologyDalianChina

Personalised recommendations