Abstract
Walking is one of the major human activities, and walking pattern is unique for each individual. Thus, human gait can be applied in biometric personal authentication. The traditional method for gait recognition is based on one or multiple cameras. With the rapid development of Micro-Electro-Mechanical System (MEMS), small light inertial sensors have been used for human identification so far. In this study, a gait based personal authentication method is proposed using MEMS inertial sensors. They are fixed in the smart shoes, collecting motion signals and transmitting them to the server. Then, gait parameters such as step length, cadence, stance phase, swing phase and the pitch angular are calculated and used as features for personal identification. A probabilistic neural network is proposed as a classification mechanism to uniquely identify different users. Experiments are conducted to validate the proposed method. By using two cross-validation techniques, the overall mean classification rate for 22 persons is up to 85.3 and 85.7% respectively, which demonstrates the effectiveness of the method.
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References
Alvarez JC, Alvarez D, Lopez A, Gonzalez RC (2012) Pedestrian navigation based on a waist-worn inertial sensor. Sensors 12(8):10536–10549. https://doi.org/10.3390/s120810536
Chen L, Nugent CD, Wang H (2012) A knowledge-driven approach to activity recognition in smart homes. IEEE Trans Knowl Data Eng 24(6):961–974. https://doi.org/10.1109/TKDE.2011.51
Chen L, Nugent CD, Okeyo G (2014) An ontology-based hybrid approach to activity modeling for smart homes. IEEE Trans Hum Machine Syst 44(1):92–105. https://doi.org/10.1109/THMS.2013.2293714
Derawi MO, Nickel C, Bours P, Busch C (2010) Unobtrusive user-authentication on mobile phones using biometric gait recognition. In: Intelligent information hiding and multimedia signal processing (IIH-MSP), sixth international conference, pp 306–311. https://doi.org/10.1109/IIHMSP.2010.83
Frank J, Mannor S, Pineau J, Precup D (2013) Time series analysis using geometric template matching. IEEE Trans Pattern Anal Mach Intell 35:740–754. https://doi.org/10.1109/TPAMI.2012.121
Gao L, Bourke AK, Nelson J (2014) Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med Eng Phys 36(6):779–785. https://doi.org/10.1016/j.medengphy.2014.02.012
Gjoreski H, Gams M, Lustrek M (2012) Context-based fall detection using inertial and location sensors. In: Ambient intelligence, international joint conference, pp 1–16. https://doi.org/10.1007/978-3-642-34898-3_1
Godfrey A, Bourke AK, Olaighin GM, Van DVP, Nelson J (2011) Activity classification using a single chest mounted tri-axial accelerometer. Med Eng Phys 33(9):1127–1135. https://doi.org/10.1016/j.medengphy.2011.05.002
Guan Y, Li CT, Roil F (2015) On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 37(7):1521–1528. https://doi.org/10.1109/TPAMI.2014.2366766
Han J, Bhanu B (2016) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28:316–322. https://doi.org/10.1109/TPAMI.2006.38
Han S, Wang J (2011) A novel method to integrate IMU and magnetometers in attitude and heading reference systems. J Navig 64(4):727–738. https://doi.org/10.1017/S0373463311000233
Hoang T, Choi D, Vo V, Nguyen A, Nguyen T (2013) A lightweight gait authentication on mobile phone regardless of installation error. In: Security and privacy protection in information processing systems, IFIP international information security conference, pp 83–101. https://doi.org/10.1007/978-3-642-39218-4_7
Lu J, Wang G, Moulin P (2014) Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Trans Inf Forensics Secur 9(1):51–61. https://doi.org/10.1109/TIFS.2013.2291969
Mariani B, Rochat S, BULA CJ, Aminian A (2012) Heel and toe clearance estimation for gait analysis using wireless inertial sensors. IEEE Trans Biomed Eng 59(11):3162–3168. https://doi.org/10.1109/TBME.2012.2216263
Ngo TT, Makihara Y, Nagahara H, Mukaigawa Y, Yagi Y (2014) The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recogn 47(1):228–237. https://doi.org/10.1016/j.patcog.2013.06.028
Preece SJ, Goulermas JY, Kenney LPJ, Howard D (2009) A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng 56(3):871–879. https://doi.org/10.1109/TBME.2008.2006190
Rafi M, Hamid MDE, Khan MS, Wahidabanu RSD (2011) A parametric approach to gait signature extraction for human motion identification. Int J Image Process 5(2):185–198
Salarian A, Burkhard PR, Vingerhoets FJG, Jolle BM, Aminian K (2013) A novel approach to reducing number of sensing units for wearable gait analysis systems. IEEE Trans Biomed Eng 60(1):72–77. https://doi.org/10.1109/TBME.2012.2223465
Sama A, Ruiz FJ, Agell N, Perez-Lopez C, Catala A, Cabestany J (2013) Gait identification by means of box approximation geometry of reconstructed attractors in latent space. Neurocomputing 121:79–88. https://doi.org/10.1016/j.neucom.2012.12.042
Skillen KL, Chen L, Nugent CD, Donnelly MP, Solheim I (2012) A user profile ontology based approach for assisting people with dementia in mobile environments. In: Engineering in medicine and biology society, 34th annual international conference, pp 6390–6393. https://doi.org/10.1109/EMBC.2012.6347456
Sprager S, Juric MB (2015) An efficient HOS-based gait authentication of accelerometer data. IEEE Trans Inf Forensics Secur 10(7):1486–1498. https://doi.org/10.1109/TIFS.2015.2415753
Specht DF (1990) Probabilistic neural networks. Neural Networks 3(1):109–118
Sun H, Yuao T (2012) Curve aligning approach for gait authentication based on a wearable accelerometer. Physiol Meas 33:1111–1120. https://doi.org/10.1088/0967-3334/33/6/1111
Trivino G, Alvarez-Alvarez A, Bailador G (2010) Application of the computational theory of perceptions to human gait pattern recognition. Pattern Recogn 43:2572–2581. https://doi.org/10.1016/j.patcog.2010.01.017
Wang C, Zhang J, Wang L, Pu J, Yuan X (2012) Human identification using temporal information preserving gait template. IEEE Trans Pattern Anal Mach Intell 34(11):2164–2176. https://doi.org/10.1109/TPAMI.2011.260
Wu G, Xue S (2008) Portable preimpact fall detector with inertial sensors. IEEE Trans Neural Syst Rehabil Eng 16(2):178–183. https://doi.org/10.1109/TNSRE.2007.916282
Yun X, Bachmann ER (2006) Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Trans Robot 22(6):1216–1227. https://doi.org/10.1017/S0373463311000233
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant no. 61501076), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant no. 2015BAI06B02), Project of Dalian high-level talents innovation support (Grant no. 2016RQ077) and Health General Research Project of Zhejiang Province, China (Grant no. 2018R045).
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Tao, S., Zhang, X., Cai, H. et al. Gait based biometric personal authentication by using MEMS inertial sensors. J Ambient Intell Human Comput 9, 1705–1712 (2018). https://doi.org/10.1007/s12652-018-0880-6
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DOI: https://doi.org/10.1007/s12652-018-0880-6