Abstract
In this paper, a new method for human gait analysis based on the Kinect Sensor is introduced. Such method based on Kinect sensor can be divided into three steps: data acquisition, pre-processing and gait parameter calculation. First, a GUI (Graphical User Interface) was designed to control the Kinect sensor and get the required raw gait data. In the pre-processing, abnormal frames are removed first. Afterwards,the influence of Kinect’s installation error is eliminated by coordinate system transformation. What’s more,the noise is eliminated by using moving average filtering and median filtering. Finally, gait parameters are obtained by the designed algorithm which composed of gait cycle detection, gait parameter calculation, and gait phase extraction. The validity of the gait analysis method based on Kinect v2 was verified by experiments.
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References
Schlachetzki, J.C.M., et al.: Wearable sensors objectively measure gait parameters in parkinson’s disease. PloS One 12(10), e0183989 (2017)
LeMoyne, R., Mastroianni, T.: Wearable and wireless gait analysis platforms: smartphones and portable media devices. In: Wireless MEMS Networks and Applications, pp. 129–152. Elsevier, 2017
Long, Y., Zhijiang, D., Cong, L., Wang, W., Zhang, Z., Dong, W.: Active disturbance rejection control based human gait tracking for lower extremity rehabilitation exoskeleton. ISA Trans. 67, 389–397 (2017)
Bouten, C.V.C., Koekkoek, K.T.M., Verduin, M., Kodde, R., Janssen, J.D.: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans. Biomed. Eng. 44(3), 136–147 (1997)
Li, Y., Guo, J., Zhang, Q.: Methods and technologies of human gait recognition. J. Jilin Univ.(Eng. Technol. Ed.) 50(1), 1–18 (2020)
Tanawongsuwan, R., Bobick, A.: Gait recognition from time-normalized joint-angle trajectories in the walking plane. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, volume 2, pages II-II. IEEE, 2001
Wang, L., Tan, T., Ning, H., Weiming, H.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)
Little, J., Boyd, J.: Recognizing people by their gait: the shape of motion. Videre: J. Comput. Vis. Res. 1(2), 1–32 (1998)
Kinect for xbox one, (2020). https://en.wikipedia.org/wiki/Kinect
Rahman, M.: Beginning Microsoft Kinect for Windows SDK 2.0: Motion and Depth Sensing for Natural User Interfaces. Apress, Montreal (2017)
Terven, J.R., Córdova-Esparza, D.M.: Kin2. a kinect 2 toolbox for matlab. Sci. Comput. Program. 130, 97–106 (2016)
Huang Q.: Design and implementation of gait analysis system based on rgb-d information. Master’s thesis, Huazhong University of Science and Technology (2019)
Leijie, L.: Real-time optimization based on kinect v2 skeleton data. Electron. World 6, 145–146 (2018)
Sahak, R., Zakaria, N.K., Tahir, N.M., Yassin, A.I.M. and Jailani, R.: Review on current methods of gait analysis and recognition using kinect. In: 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 229–234. IEEE, 2019
Sinha, A., Chakravarty, K., Bhowmick, B., et al.: Person identification using skeleton information from kinect. In: Proceedings of International Conference on Advances in Computer-Human Interactions, pp. 101–108 (2013)
Qing, J.G., Song, Y.W., Ye, Q., Li, Y.Q., Tang, X.: The biomechanics principle of walking and analysis on gaits. J. Nanjing Inst. Phys. Educ. (Nat. Sci.). 04, 1–7+39 (2006)
Vaughan C.L., Brian, L.D., Jeremy, C.O.: Dynamics of Human Gait, Human Kinetics Publishers (1992)
Acknowledgements
The authors would like to gratefully acknowledge the reviewers comments. This work is supported by National Natural Science Foundation of China (Grant Nos. 52075180 and U1713207), Science and Technology Program of Guangzhou (Grant Nos. 201904020020), and the Fundamental Research Funds for the Central Universities.
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Wang, N., Lin, G., Zhang, X. (2020). Human Gait Analysis Method Based on Kinect Sensor. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_41
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DOI: https://doi.org/10.1007/978-3-030-66645-3_41
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