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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Zhou, H., Hu, H.: Reducing drifts in the inertial measurements of wrist and elbow positions. IEEE Trans. Instrum. Meas. 59(3), 575–585 (2010)
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)
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)
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)
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)
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)
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)
Chen, S., Lach, J., Member, S., Lo, B., Member, S.: Sensors: a systematic review. IEEE J. Biomed. Health Inf. 20(6), 1521–1537 (2016)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors, pp. 1–9. XSENS TECHNOLOGIES (2013)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Qiu, S., Wang, Z., Zhao, H., Liu, L., Wang, J., Li, J. (2019). Gait Analysis for Physical Rehabilitation via Body-Worn Sensors and Multi-information Fusion. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-02819-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02818-3
Online ISBN: 978-3-030-02819-0
eBook Packages: EngineeringEngineering (R0)