Abstract
This paper affords a method for the recognition of gait biometrics using J48 decision tree algorithm for an integrated accelerometer of mobile phone. The proposed model allows recognizing the subjects based on their gait. When a subject walks, a unique angle is created at his knee joint. Classifying the subject with the help of his knee angle enables unique recognition. The model is designed, developed, and tested in WEKA considering the parameters x-axis, y-axis, z-axis, and knee angle for the recognition of gait. Acceleration data is received from integrated sensor of cell phone placed in a front pocket of right leg’s trouser of the subject. Data is then analyzed and with the involvement of total 41 volunteers over 18–30 years old in the experiment, we achieved accuracy of 89.45%.
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References
J. Rana, N. Arora, D. Hiran, An intensive assessment of factors affecting gait identification and recognition. Int. J. Innovations Adv. Comput. Sci. (IJIACS). 6(6), 238–244 (2017)
L. Bao, S. Intille, Activity recognition from user-annotated acceleration data. Pervasive Comput. 1–17 (2004)
P. Gupta, T. Dallas, Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans. Biomed. Eng. 61(6), 1780–1786 (2014)
S. Das, L. Green, B. Perez, M. Murphy, A. Perring, Detecting user activities using the accelerometer on android smartphones. Team Res. Ubiquitous Secure Technol. (2010)
JR. Quinlan, C4. 5: Programs for Machine Learning, vol. 1 (Morgan Kaufmann, 1993)
D. Gafurov, E. Snekkenes, P. Bours, Gait authentication and identification using wearable accelerometer sensor. Autom. Ident. Adv. Technol. 220–225 (2007)
G. Pan, Y. Zhang, Z. Wu, Accelerometer-based gait recognition via voting by signature points. Electron. Lett. 45(22), 1116–1118 (2009)
L. Rong, Z. Jianzhong, L. Ming, H. Xiangfeng, A Wearable acceleration sensor system for gait recognition, in Industrial Electronics and Applications (2007), pp. 2654–2659
H.K. Chan, H. Zheng, H. Wang, R. Gawley, M. Yang, R. Sterritt, Feasibility study on iphone accelerometer for gait detection, in Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2011), pp. 184–187
S. Sprager, D. Zazula, Impact of different walking surfaces on gait identification based on higher-order statistics of accelerometer data. In Signal and Image Processing Applications (2011), pp. 360–365
J.R. Kwapisz, G.M. Weiss, S.A. Moore, Cell phone-based biometric identification, in Biometrics: Theory Applications and Systems (2010), pp.1–7
F. Juefei-Xu, C. Bhagavatula, A. Jaech, U. Prasad, M. Savvides, Gait-ID on the move: pace independent human identification using cell phone accelerometer dynamics, in Biometrics: Theory, Applications and Systems (2012), pp. 8–15
N. Kunnathu, Biometric user authentication on smartphone accelerometer sensor data. proceedings of student-faculty research day, CS (2015)
T. Bennett, R. Jafari, N. Gans, An extended kalman filter to estimate human gait parameters and walking distance, in American Control Conference (ACC) (2013), pp. 752–757
Saito, H., Watanabe, T., Arifin, A.: Ankle and knee joint angle measurements during gait with wearable sensor system for rehabilitation, in World Congress on Medical Physics and Biomedical Engineering (Springer Berlin Heidelberg, 2009), pp. 506–509
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Images and the datasets used in this work are our own and not from any others work.
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Rana, J., Arora, N., Hiran, D. (2019). Gait Recognition Using J48-Based Identification with Knee Joint Movements. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_17
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DOI: https://doi.org/10.1007/978-981-13-3600-3_17
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