Gait Analysis for Physical Rehabilitation via Body-Worn Sensors and Multi-information Fusion
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.
KeywordsHuman gait analysis Biological information detection Multi-information fusion Exercise rehabilitation Micro-electro-mechanical sensor
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.
- 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
- 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
- 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
- 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
- 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.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