Gait Analysis of Smart Phones with The Help of The Accelerometer Sensor

Year 2021, , 283 - 299, 30.04.2021

Emrah Aydemir , İbrahim Karslıoğlu

https://doi.org/10.35414/akufemubid.856995

Cited By: 1

Abstract

The ability to measure human movements in sports fields is among the important issues for performance measurement and development. This instance is also an important part of clinical evaluations. Electromagnetic systems are among the most widely used methods to evaluate human movements. In this study, walking data of 50 different people were used in a 100-meter-long corridor. The walking dataset was obtained from the accelerometer sensor with a software developed for the smartphone. Three-dimensional Local Binary Pattern (LBP) method was applied to the dataset and a total of 768 features were generated. Datasets were made with different classification algorithms and 97.2% successful classification was achieved with Subspace KNN. In the classification according to gender, 99.7% successful classification was obtained. With this method, it is thought that more economical methods will be developed instead of high-cost devices in detecting gait disorders.

Keywords

Gait Recognition, Accelerometer, Gait Pattern, Smart Phone

Akıllı Telefonların İvmeölçer Sensörü Yardımıyla Yürüyüş Deseni Analizi

Abstract

Spor alanlarında insan hareketlerini ölçme yeteneği performans ölçüm ve gelişimi için önemli konular arasındadır. Bu durum aynı zamanda klinik değerlendirmelerin de önemli bir parçasıdır. Özellikle elektromanyetik sistemler insan hareketlerini değerlendirmek için en yaygın kullanılan yöntemler arasında yer alır. Buradaki çalışmada 100 metre uzunluğunda bir koridorda 50 farklı kişinin yürüme verileri kullanılmıştır. Yürüme verileri akıllı telefon için geliştirilen bir yazılım ile ivmeölçer sensöründen elde edilmiştir. Verilere üç boyutlu Local Binary Pattern (LBP) yöntemi uygulanmış ve toplam 768 öznitelik çıkarılmıştır. Farklı sınıflandırma algoritmaları ile testler yapılmış ve Subspace KNN ile %97,2 başarılı sınıflandırma elde edilmiştir. Cinsiyete göre yapılan sınıflandırmada ise %99,7 başarılı sınıflandırma elde edilmiştir. Bu yöntem ile yürüme bozukluğu tespitinde yüksek maliyetli cihazlar yerine daha ekonomik yöntemler geliştirileceği düşünülmektedir.

Keywords

Yürüyüş Tanıma, İvmeölçer, Yürüyüş Deseni, Akıllı Telefon

References

• Addlesee, M.D., Jones, A., Livesey, F., & Samaria, F., 1997. The ORL active floor [sensor system]. IEEE Personal Communications, 4(5), 35-41.
• Allix, K., Bissyandé, T. F., Jérome, Q., Klein, J., State, R., & Le Traon, Y., 2014. Large-scale machine learning-based malware detection: confronting the" 10-fold cross validation" scheme with reality. Paper presented at the Proceedings of the 4th ACM conference on Data and application security and privacy.
• Annadhorai, A., Guenterberg, E., Barnes, J., Haraga, K., & Jafari, R., 2008. Human identification by gait analysis. Paper presented at the Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments.
• Aqmar, M. R., Shinoda, K., & Furui, S., 2012. Robust gait-based person identification against walking speed variations. IEICE TRANSACTIONS on Information and Systems, 95(2), 668-676.
• Arora, P., Hanmandlu, M., & Srivastava, S., 2015. Gait based authentication using gait information image features. Pattern Recognition Letters, 68, 336-342.
• Balasubramanian, C. K., Bowden, M. G., Neptune, R. R., & Kautz, S. A., 2007. Relationship between step length asymmetry and walking performance in subjects with chronic hemiparesis. Archives of physical medicine and rehabilitation, 88(1), 43-49.
• Barclay, C. D., Cutting, J. E., & Kozlowski, L. T., 1978. Temporal and spatial factors in gait perception that influence gender recognition. Perception & psychophysics, 23(2), 145-152.
• Barra, P., Bisogni, C., Nappi, M., Freire-Obregón, D., & Castrillón-Santana, M., 2019. Gait analysis for gender classification in forensics. Paper presented at the International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications.
• BenAbdelkader, C., Cutler, R., & Davis, L., 2002. Motion-based recognition of people in eigengait space. Paper presented at the Proceedings of Fifth IEEE international conference on automatic face gesture recognition.
• Bengio, Y., & Grandvalet, Y., 2004. No unbiased estimator of the variance of k-fold cross-validation. Journal of machine learning research, 5(Sep), 1089-1105.
• Bingham, G. P., Schmidt, R. C., & Rosenblum, L. D., 1995. Dynamics and the orientation of kinematic forms in visual event recognition. Journal of Experimental Psychology: Human Perception and Performance, 21(6), 1473.
• Bouchrika, I., & Nixon, M. S., 2006. People detection and recognition using gait for automated visual surveillance. 2006 IET Conference on Crime and Security, London, UK, 2006, pp. 576-581.
• Breiman, L., 1999. Pasting small votes for classification in large databases and on-line. Machine learning, 36(1-2), 85-103.
• Breiman, L., 2001. Random forests. Machine learning, 45(1), 5-32.
• Chao, H., He, Y., Zhang, J., & Feng, J., 2019. Gaitset: Regarding gait as a set for cross-view gait recognition. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.
• Cherkassky, V., & Mulier, F. M., 2007. Learning from data: concepts, theory, and methods: John Wiley & Sons Press, New Jersey, 30-60.
• Cola, G., Avvenuti, M., & Vecchio, A., 2017. Real-time identification using gait pattern analysis on a standalone wearable accelerometer. The Computer Journal, 60(8), 1173-1186.
• Connor, P., & Ross, A., 2018. Biometric recognition by gait: A survey of modalities and features. Computer Vision and Image Understanding, 167, 1-27.
• Cover, T., & Hart, P., 1967. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13, 19-17.
• Cunado, D., Nixon, M. S., & Carter, J. N., 2003. Automatic extraction and description of human gait models for recognition purposes. Computer Vision and Image Understanding, 90(1), 1-41.
• Del Pozo, G. B., Sanchez-Avila, C., De-Santos-Sierra, A., & Guerra-Casanova, J., 2012. Speed-independent gait identification for mobile devices. International Journal of Pattern Recognition and Artificial Intelligence, 26(08), 1260013.
• Derawi, M. O., Bours, P., & Holien, K., 2010. Improved cycle detection for accelerometer based gait authentication. Paper presented at the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.
• Derawi, M. O., Nickel, C., Bours, P., & Busch, C. 2010. Unobtrusive user-authentication on mobile phones using biometric gait recognition. In 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (pp. 306-311). IEEE.
• Dewar, M., & Judge, G., 1980. Temporal asymmetry as a gait quality indicator. Medical and Biological Engineering and Computing, 18(5), 689-693.
• Efron, B., 2004. The estimation of prediction error: covariance penalties and cross-validation. Journal of the American Statistical Association, 99(467), 619-632.
• Efron, B., & Tibshirani, R. J., 1994. An introduction to the bootstrap: CRC Press, 40-80.
• El-Alfy, E.-S., & Binsaadoon, A. G., 2019. Automated gait-based gender identification using fuzzy local binary patterns with tuned parameters. Journal of Ambient Intelligence and Humanized Computing, 10(7), 2495-2504.
• Frank, J., Mannor, S., & Precup, D., 2010. Activity and gait recognition with time-delay embeddings. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.
• Gafurov, D., Helkala, K., & Søndrol, T., 2006. Biometric Gait Authentication Using Accelerometer Sensor JCP, 1(7), 51-59.
• Gafurov, D., & Snekkenes, E., 2009. Gait recognition using wearable motion recording sensors. EURASIP Journal on Advances in Signal Processing, 2009, 1-16.
• García-Pedrajas, N., & Ortiz-Boyer, D. 2009. Boosting k-nearest neighbor classifier by means of input space projection. Expert Systems with Applications, 36(7), 10570-10582.
• Goffredo, M., Carter, J. N., & Nixon, M. S., 2008. Front-view gait recognition. Paper presented at the 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.
• Gouwanda, D., & Senanayake, S. A., 2011. Identifying gait asymmetry using gyroscopes—A cross-correlation and Normalized Symmetry Index approach. Journal of biomechanics, 44(5), 972-978.
• Gul, A., Perperoglou, A., Khan, Z., Mahmoud, O., Miftahuddin, M., Adler, W., & Lausen, B. 2018. Ensemble of a subset of k NN classifiers. Advances in data analysis and classification, 12(4), 827-840.
• Ho, T. K., 1998. Nearest neighbors in random subspaces. Paper presented at the Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR).
• Huang, B., Chen, M., Huang, P., & Xu, Y., 2007. Gait modeling for human identification. Paper presented at the Proceedings 2007 IEEE International Conference on Robotics and Automation.
• Iwashita, Y., Kurazume, R., & Stoica, A., 2014. Gait identification using invisible shadows: robustness to appearance changes. Paper presented at the 2014 Fifth International Conference on Emerging Security Technologies.
• Iwashita, Y., Stoica, A., & Kurazume, R., 2010. Person Identification using Shadow Analysis. Paper presented at the BMVC.
• Iwashita, Y., Uchino, K., Kurazume, R., & Stoica, A., 2012. Gait identification from invisible shadows. Paper presented at the Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and Biometric Technology for Human Identification IX.
• Ji, N., Zhou, H., Guo, K., Samuel, O. W., Huang, Z., Xu, L., & Li, G., 2019. Appropriate mother wavelets for continuous gait event detection based on time-frequency analysis for hemiplegic and healthy individuals. Sensors, 19(16), 3462.
• Johnston, A. H., & Weiss, G. M., 2015. Smartwatch-based biometric gait recognition. Paper presented at the 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).
• Karasu, S., & Başkan, S., 2016. Classification of power quality disturbances by using ensemble technique. Paper presented at the 2016 24th Signal Processing and Communication Application Conference (SIU).
• Katiyar, R., Pathak, V. K., & Arya, K., 2014. Human gait recognition system based on shadow free silhouettes using truncated singular value decomposition transformation model. International Journal of Artificial Intelligence and Soft Computing, 4(4), 283-301.
• Khera, P., & Kumar, N., 2020. Role of machine learning in gait analysis: a review. Journal of Medical Engineering & Technology, 44(8), 441-467.
• Kwapisz, J. R., Weiss, G. M., & Moore, S. A., 2010. Cell phone-based biometric identification. Paper presented at the 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).
• Lamar-León, J., García-Reyes, E. B., & Gonzalez-Diaz, R., 2012. Human Gait Identification Using Persistent Homology, Berlin, Heidelberg, 50-100.
• Lee, S., Liu, Y., & Collins, R., 2007. Shape variation-based frieze pattern for robust gait recognition. Paper presented at the 2007 IEEE Conference on Computer Vision and Pattern Recognition.
• Li, X., Makihara, Y., Xu, C., Yagi, Y., & Ren, M., 2018. Gait-based human age estimation using age group-dependent manifold learning and regression. Multimedia tools and applications, 77(21), 28333-28354.
• Li, X., Maybank, S. J., Yan, S., Tao, D., & Xu, D., 2008. Gait components and their application to gender recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 145-155.
• Li, Y., Hu, H., Zhou, G., & Deng, S., 2018. Sensor-based continuous authentication using cost-effective kernel ridge regression. IEEE Access, 6, 32554-32565.
• Liu, Y., Collins, R., & Tsin, Y., 2002. Gait sequence analysis using frieze patterns. Paper presented at the European Conference on Computer Vision.
• Makihara, Y., Okumura, M., Iwama, H., & Yagi, Y., 2011. Gait-based age estimation using a whole-generation gait database. Paper presented at the 2011 International Joint Conference on Biometrics (IJCB).
• Mansouri, N., Issa, M. A., & Jemaa, Y. B., 2017. Gait features fusion for efficient automatic age classification. IET Computer Vision, 12(1), 69-75.
• Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., Makela, S.-M., & Ailisto, H., 2005. Identifying users of portable devices from gait pattern with accelerometers. Paper presented at the Proceedings.(ICASSP'05. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
• Muaaz, M., & Mayrhofer, R., 2017. Smartphone-based gait recognition: From authentication to imitation. IEEE Transactions on Mobile Computing, 16(11), 3209-3221.
• Murray, M. P., Drought, A. B., & Kory, R. C., 1964. Walking patterns of normal men. JBJS, 46(2), 335-360.
• Nabila, M., Mohammed, A. I., & Yousra, B. J., 2017. Gait-based human age classification using a silhouette model. IET Biometrics, 7(2), 116-124.
• Nickel, C., Busch, C., Rangarajan, S., & Möbius, M. 2011. Using hidden markov models for accelerometer-based biometric gait recognition. In 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (pp. 58-63). IEEE.Phillips, P. J., Sarkar, S., Robledo, I., Grother, P., & Bowyer, K., 2002. The gait identification challenge problem: Data sets and baseline algorithm. Paper presented at the Object recognition supported by user interaction for service robots.
• Rokach, L., 2010. Ensemble-based classifiers. Artificial intelligence review, 33(1-2), 1-39.
• Schapire, R. E., Freund, Y., Bartlett, P., & Lee, W. S., 1998. Boosting the margin: A new explanation for the effectiveness of voting methods. The annals of statistics, 26(5), 1651-1686.
• Semwal, V. B., Singha, J., Sharma, P. K., Chauhan, A., & Behera, B., 2017. An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimedia tools and applications, 76(22), 24457-24475.
• Shen, C., Li, Y., Chen, Y., Guan, X., & Maxion, R. A., 2017. Performance analysis of multi-motion sensor behavior for active smartphone authentication. IEEE Transactions on Information Forensics and Security, 13(1), 48-62.
• Shinzaki, M., Iwashita, Y., Kurazume, R., & Ogawara, K., 2015. Gait-based person identification method using shadow biometrics for robustness to changes in the walking direction. Paper presented at the 2015 IEEE Winter Conference on Applications of Computer Vision.
• Shorter, K. A., Polk, J. D., Rosengren, K. S., & Hsiao-Wecksler, E. T., 2008. A new approach to detecting asymmetries in gait. Clinical Biomechanics, 23(4), 459-467.
• Shuwandy, M. L., Zaidan, B., Zaidan, A., & Albahri, A., 2019. Sensor-based mHealth authentication for real-time remote healthcare monitoring system: A multilayer systematic review. Journal of medical systems, 43(2), 33.
• Si, W., Yang, G., Chen, X., & Jia, J., 2019. Gait identification using fractal analysis and support vector machine. Soft Computing, 23(19), 9287-9297.
• Sprager, S., & Zazula, D., 2009a. A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. WSEAS Transactions on Signal Processing, 5(11), 369-378.
• Sprager, S., & Zazula, D., 2009b. Gait identification using cumulants of accelerometer data. Paper presented at the Proceedings of the 2nd WSEAS International Conference on Sensors, and Signals and Visualization, Imaging and Simulation and Materials Science.
• Stoica, A., 2010. Method for using information in human shadows and their dynamics. In: Google Patents.
• Stone, M., 1977. An asymptotic equivalence of choice of model by cross‐validation and Akaike's criterion. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 44-47.
• Sun, B., Wang, Y., & Banda, J., 2014. Gait characteristic analysis and identification based on the iPhone’s accelerometer and gyrometer. Sensors, 14(9), 17037-17054.
• Sun, F., Mao, C., Fan, X., & Li, Y., 2018. Accelerometer-based speed-adaptive gait authentication method for wearable IoT devices. IEEE Internet of Things Journal, 6(1), 820-830.
• Suriani, N. S., Mustafa, M. H., & Abd Rahman, F. Y., 2020. Gait Analysis for Rehabilitation Assessment System Simulator. Applications of Modelling and Simulation, 4, 290-296.
• Tao, W., Liu, T., Zheng, R., & Feng, H., 2012. Gait analysis using wearable sensors. Sensors, 12(2), 2255-2283.
• Tong, S., Fu, Y., Ling, H., & Zhang, E., 2017. Gait identification by joint spatial-temporal feature. Paper presented at the Chinese Conference on Biometric Recognition.
• Verlekar, T. T., Soares, L. D., & Correia, P. L., 2017. Shadow type identification for gait recognition using shadows. Paper presented at the 23rd Portuguese Conference on Pattern Recognition, RECPAD.
• Verlekar, T. T., Soares, L. D., & Correia, P. L., 2018. Gait recognition in the wild using shadow silhouettes. Image and Vision Computing, 76, 1-13.
• Wang, L., Tan, T., Ning, H., & Hu, W., 2003. Silhouette analysis-based gait recognition for human identification. IEEE transactions on pattern analysis and machine intelligence, 25(12), 1505-1518.
• Yalabık, N., Yavuzer, G., Atalay, V., & Şen Köktaş, N., 2008. YAHTS: Yürüyüş Analizi ile Hastalık Tanıma Sistemi. Ankara, 106E035)
• Zeng, W., Chen, J., Yuan, C., Liu, F., Wang, Q., & Wang, Y., 2018. Accelerometer-based gait recognition via deterministic learning. Paper presented at the 2018 Chinese Control And Decision Conference (CCDC).
• Zhang, Y., Pan, G., Jia, K., Lu, M., Wang, Y., & Wu, Z. 2014. Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE transactions on cybernetics, 45(9), 1864-1875.
• Zhang, H., Yang, S., Guo, L., Zhao, Y., Shao, F., & Chen, F., 2015. Comparisons of isomiR patterns and classification performance using the rank-based MANOVA and 10-fold cross-validation. Gene, 569(1), 21-26.
• Zhao, G., Liu, G., Li, H., & Pietikainen, M., 2006. 3D gait recognition using multiple cameras. Paper presented at the 7th International Conference on Automatic Face and Gesture Recognition (FGR06).
• Zhong, Y., Deng, Y., & Meltzner, G., 2015. Pace independent mobile gait biometrics. Paper presented at the 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).
• Zou, Q., Ni, L., Wang, Q., Li, Q., & Wang, S., 2017. Robust gait recognition by integrating inertial and RGBD sensors. IEEE transactions on cybernetics, 48(4), 1136-1150.
• Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q., 2020. Deep Learning-Based Gait Recognition Using Smartphones in the Wild. IEEE Transactions on Information Forensics and Security, 15, 3197-3212.