Skip to main content
SpringerLink
Log in

Human gait recognition: A systematic review

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A biometric system is a technology that utilizes an individual’s unique physiological and behavioral characteristics to identify and authenticate them. It falls under the category of pattern recognition. Gait recognition, specifically the identification of individuals based on their walking patterns, has garnered significant attention from researchers due to its potential to accurately identify individuals from a distance. Gait recognition systems involve a complex integration of technical, operational and definitional choices and have been applied in a variety of contexts such as security, medical examinations, identity management, and access control. The utilization of gait recognition methods and tools has led to the development of various useful and widely accepted applications. This article provides an overview of the various techniques and approaches employed in gait recognition, including the framework, history, and parameters utilized. The article also delves into the different classifiers, both traditional and deep learning-based, used in the field. Additionally, it examines the different types of datasets utilized in experimental research and the methodology for evaluating articles on gait recognition. With its potential in security applications, gait recognition is expected to have a wide range of future applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Açıcı K, Erdaş ÇB, Aşuroğlu T, Toprak MK, Erdem H, Oğul H (2017) A Random Forest Method to Detect Parkinson’s Disease via Gait Analysis. In: Boracchi G, Iliadis L, Jayne C, Likas A (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham https://doi.org/10.1007/978-3-319-65172-9_51

  2. Ahmed K, Saini M (2022) FCML-gait: fog computing and machine learning inspired human identity and gender recognition using gait sequences. SIViP .https://doi.org/10.1007/s11760-022-02217-z

  3. AlAsadi AH (2014) Gait recognition using support vector machine and neural network. J Basrah Res 40(4):68–78

    Google Scholar 

  4. Alcaraz JC, Moghaddamnia S, Peissig J (2021) Efficiency of deep neural networks for joint angle modeling in digital gait assessment. EURASIP J Adv Signal Process, 10. https://doi.org/10.1186/s13634-020-00715-1

  5. Alotaibi M, Mahmood A (2017) Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding. SIViP 11(13):1–8. https://doi.org/10.1007/s11760-017-1067-x

    Article  Google Scholar 

  6. Alsaggaf WA, Mehmood I, Khairullah EF, Alhuraiji S, Sabir MFS, Alghamdi AS, El-Latif AAA (2021) A smart surveillance system for uncooperative gait recognition using cycle consistent generative adversarial networks (CCGANs). Computational Intelligence and Neuroscience 2021. https://doi.org/10.1155/2021/3110416

  7. Aqmar MR, Shinoda K, Furui S (2010) Robust gait recognition against speed variation. Proc. 20th international conference on pattern recognition. p. 2190-2193

  8. Arshad H, Khan MA, Sharif MI, Yasmin M, Tavares JMRS, Zhang YD, Satapathy S (2020) A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition. Expert Syst 39:1–21. https://doi.org/10.1111/exsy.12541

    Article  Google Scholar 

  9. Asif M, Tiwana MI, Khan US, Ahmad MW, Qureshi WS, Iqbal J (2022) Human gait recognition subject to different covariate factors in a multi-view environment. Results Eng 15:100556. https://doi.org/10.1016/j.rineng.2022.100556

    Article  Google Scholar 

  10. Babaee M, Li L, Rigoll G (2018) Gait recognition from incomplete gait cycle. Proc - Int Conf Image Proc ICIP:768–772

  11. Bae J, Tomizuka M (2011) Gait phase analysis based on a hidden Markov model. Mechatronics 21(6):961–970

    Article  Google Scholar 

  12. Baker R (2007) The history of gait analysis before the advent of modern computers. Gait Posture 26(3):331–342. https://doi.org/10.1016/j.gaitpost.2006.10.014

    Article  Google Scholar 

  13. Balamurugan S, Raj VJ, Peter SJ (2021) Deep features based Multiview gait recognition. Turkish J Comput Mathema Educ 12(10):472–478

    Google Scholar 

  14. Begg R, Kamruzzaman J (2006) Neural networks for detection and classification of walking pattern changes due to ageing. Australas Phys Eng Sci Med 29(2):188–195

    Article  Google Scholar 

  15. Begg RK, Palaniswami M, Owen B (2005) Support vector machines for automated gait classification. IEEE Trans Biomed Eng 52(5):828–838

    Article  Google Scholar 

  16. Benyacoub B, ElBernoussi S, Zoglat A, El Moudden I (2014) Classification with hidden markov model. Appl Math Sci 8(50):2483–2496

    MathSciNet  Google Scholar 

  17. Bertoli M, Cereatti A, Trojaniello D, Avanzino L, Pelosin E, Del Din S (2018) Estimation of spatio temporal parameters of gait from magneto inertial measurement units : multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults. Biomed Eng Online 17(58):1–14. https://doi.org/10.1186/s12938-018-0488-2

    Article  Google Scholar 

  18. Bouchrika I, Goffredo M, Carter JN, Nixon MS (2009) Covariate analysis for view-point independent gait recognition. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes bioinformatics), 5558. p. 990–999. https://doi.org/10.1007/978-3-642-01793-3_100

  19. Britto AV, Ansari V (2018) Human gait analysis using artificial neural networks. Proc IEEE Int Conf Comput Intell Comput Res (ICCIC):1–6

  20. Castro FM, Marín-Jiménez MJ, Guil N, de la Pérez Blanca N (2020) Multimodal feature fusion for CNN-based gait recognition: an empirical comparison. Neural Comput & Applic 32(17):14173–14193

    Article  Google Scholar 

  21. Chen C, Liang J, Zhu X (2011) Gait recognition based on improved dynamic Bayesian networks. Pattern Recogn 44(4):988–995

    Article  Google Scholar 

  22. Chen Q, Wang Y, Liu Z, Liu Q, Huang D (2017) Feature map pooling for cross-view gait recognition based on S. p. 54-61

  23. Choi S, Youn I-H, LeMay R, Burns S, Youn J-H (2014) Biometric gait recognition based on wireless acceleration sensor using k-nearest neighbor classification. Proc. International Conference on Computing, Networking and Communications (ICNC). p. 1091–1095, https://doi.org/10.1109/ICCNC.2014.6785491

  24. Connor P, Ross A (2017) Biometric recognition by gait: A survey of modalities and features. Comput Vis Image Underst 167:1–27. https://doi.org/10.1016/j.cviu.2018.01.007

    Article  Google Scholar 

  25. Derlatka M, Bogdan M (2015) Ensemble kNN classifiers for human gait recognition based on ground reaction forces. Proc. 8th international conference on human system interaction (HSI). p. 88-93

  26. Elharrouss O, Almaadeed N, Al-Maadeed S, Bouridane A (2021) Gait recognition for person re-identification. J Supercomput 77(4):3653–3672. https://doi.org/10.1007/s11227-020-03409-5

    Article  Google Scholar 

  27. Feng Y, Li Y, Luo J (2016) Learning effective gait features using LSTM. Proc - Int Conf Pattern Recognition 0. p. 325–330

  28. Gadaleta M, Merelli L, Rossi M (2016) Human authentication from ankle motion data using convolutional neural networks. Proc IEEE Statistical Signal Processing Workshop (SSP). p. 1–5

  29. Gao Z, Wu J, Wu T, Huang R, Zhang A, Zhao J (2022a) Robust clothing-independent gait recognition using hybrid part-based gait features PeerJ Computer Science, 8. https://doi.org/10.7717/peerj-cs.996

  30. Gao J, Zhang S, Guan X, Meng X (2022b) Multiview gait recognition based on slack allocation generation adversarial network. Wirel Commun Mob Comput 2022:1–12. https://doi.org/10.1155/2022/1648138

    Article  Google Scholar 

  31. Ghosh R (2022) A faster R-CNN and recurrent neural network based approach of gait recognition with and without carried objects. Expert Syst Appl 205:117730. https://doi.org/10.1016/j.eswa.2022.117730

    Article  Google Scholar 

  32. Giorgi G, Martinelli F, Saracino A, Sheikhalishahi M (2017) Try walking in my shoes, if you can: accurate gait recognition through deep learning. Proc. international conference on computer safety, reliability, and security. p. 384-395

  33. Gou H, Yan L, Xiao J (2015) A gait recognition system based on SVM and accelerations. MATEC Web Conf 30:3–6

    Article  Google Scholar 

  34. Guan Y, Li CT, Hu Y (2012) Robust clothing-invariant gait recognition. Proceedings of the 2012 8th international conference on intelligent information hiding and multimedia signal processing, IIH-MSP 2012, July. p. 321–324 https://doi.org/10.1109/IIH-MSP.2012.84

  35. Guo Q, Jiang D (2015) Method for walking gait identification in a lower extremity exoskeleton based on C4.5 decision tree algorithm. Int J Adv Robot Syst 12(4):1–11

    Article  Google Scholar 

  36. Gupta SK, Chattopadhyay P (2021) Gait recognition in the presence of co-variate conditions. Neurocomputing 454:76–87. https://doi.org/10.1016/j.neucom.2021.04.113

    Article  Google Scholar 

  37. Gupta A, Prasad PWC, Alsadoon A, Bajaj K (2015) Hybrid method for gait recognition using SVM and Baysian network. Proc. IEEE 8th international workshop on computational intelligence and applications (IWCIA). p. 89-94

  38. Hawas AR, El-Khobby HA, Abd-Elnaby M, Abd El-Samie FE (2019) Gait identification by convolutional neural networks and optical flow. Multimed Tools Appl 78(18):25873–25888

    Article  Google Scholar 

  39. Hnatiuc M, Geman O, Avram AG, Gupta D, Shankar K (2021) Human signature identification using iot technology and gait recognition. Electron 10(7). https://doi.org/10.3390/electronics10070852

  40. Horst F, Lapuschkin S, Samek W, Müller KR, Schöllhorn WI (2019) Explaining the unique nature of individual gait patterns with deep learning. Sci Rep 9(1):1–13. https://doi.org/10.1038/s41598-019-38748-8

    Article  Google Scholar 

  41. Hu L, Pan X, Tang Z, Luo X (2022) A fast fuzzy clustering algorithm for complex networks via a generalized momentum method. IEEE Trans Fuzzy Syst 30(9):3473–3485. https://doi.org/10.1109/tfuzz.2021.3117442

    Article  Google Scholar 

  42. Huan Z, Chen X, Lv S, Geng H (2019) Gait recognition of acceleration sensor for smart phone based on multiple classifier fusion. Math Probl Eng 2019:1–17. https://doi.org/10.1155/2019/6471532

    Article  Google Scholar 

  43. Huang PS, Harris CJ, Nixon MS (1999) Human gait recognition in canonical space using temporal templates. Vision, Imag Signal Proc, IEE Proc -Vision, Image Signal Proc 146(2):93–100

    Article  Google Scholar 

  44. Huang H, Zhou P, Li Y, Sun F (2021) A lightweight attention-based CNN model for efficient gait recognition with wearable IMU sensors. Sensors 21(8). https://doi.org/10.3390/s21082866

  45. Isaac ERHP, Elias S, Rajagopalan S, Easwarakumar KS (2019) Template-based gait authentication through Bayesian thresholding. IEEE/CAA J Automatica Sinica 6(1):209–219

    Article  Google Scholar 

  46. Islam MR, Pavel MSR, Tunaz SA (2019) Neurodegenerative disease classification using gait signal features and random Forest classifier. Proc. 4th international conference on electrical information and communication technology (EICT). p. 1-4

  47. Jarchi D, Pope J, Lee TKM, Tamjidi L, Mirzaei A, Sanei S (2018) A review on Accelerometry based gait analysis and emerging clinical applications. IEEE Rev Biomed Eng 11:177–194

    Article  Google Scholar 

  48. Jayasinghe U, Hwang F, Harwin WS (2022) Comparing loose clothing-mounted sensors with body-mounted sensors in the analysis of walking. Sensors (Basel, Switzerland) 22(17):6605. https://doi.org/10.3390/s22176605

    Article  Google Scholar 

  49. Jia N, Li CT, Sanchez V, Liew AWC (2017) Fast and robust framework for view-invariant gait recognition. Proc. - 2017 5th Int. work. Biometrics forensics (IWBF 2017). p. 1-6 https://doi.org/10.1109/IWBF.2017.7935092.

  50. Jiang X, Zhang Y, Yang Q, Deng B, Wang H (2020) Millimeter-wave array radar-based human gait recognition using multi-channel three-dimensional convolutional neural network. Sensors 20(19):5466. https://doi.org/10.3390/s20195466

    Article  Google Scholar 

  51. Junaid I, Ari S (2022) Gait recognition under different covariate conditions using deep learning technique. 2022 IEEE International conference on signal processing and communications (SPCOM). https://doi.org/10.1109/spcom55316.2022.9840818

  52. Kale A, Rajagopalan AN, Cuntoor N, Kruger V (2002) Gait-based recognition of humans using continuous HMMs. Proceedings of fifth IEEE international conference on automatic face gesture recognition. p. 336-341

  53. Kececi A, Yildirak A, Ozyazici K, Ayluctarhan G, Agbulut O, Zincir I (2020) Implementation of machine learning algorithms for gait recognition. Eng Sci Technol an Int J 23(4):931–937. https://doi.org/10.1016/j.jestch.2020.01.005

    Article  Google Scholar 

  54. Kiprijanovska I, Gjoreski H, Gams M (2020) Detection of gait abnormalities for fall risk assessment using wrist worn inertial sensors and deep learning. Sensors (Switzerland) 20(18):1–21. https://doi.org/10.3390/s20185373

    Article  Google Scholar 

  55. Kong W, Saad MH, Hannan MA, Hussain A (2014) Human gait state classification using artificial neural network. Proc. IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP). p.1–5

  56. Konz L, Hill A, Banaei-Kashani F (2022) St-DeepGait: A spatiotemporal deep learning model for human gait recognition. Sensors 22(20):8075. https://doi.org/10.3390/s22208075

    Article  Google Scholar 

  57. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  58. Kukreja V, Kumar D, Kaur A(2021) Deep learning in human gait recognition: An overview international conference on advance computing and innovative Technologies in Engineering (ICACITE), 2021. pp. 9-13, https://doi.org/10.1109/ICACITE51222.2021.9404611

  59. Kumar MSN, Babu RV (2012) Human gait recognition using depth camera : A covariance based approach. Proc. Eigth Indian Conference on Computer Vision, Graphics and Image Processing, 1–6

  60. Kumar P, Mukherjee S, Saini R, Kaushik P, Roy PP, Dogra DP (2019) Multimodal gait recognition with inertial sensor data and video using evolutionary algorithm. IEEE Trans Fuzzy Syst 27(5):956–965. https://doi.org/10.1109/TFUZZ.2018.2870590

    Article  Google Scholar 

  61. Lai Z, Xu Y, Jin Z, Zhang D (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662

    Article  Google Scholar 

  62. Lee SS, Choi ST, Choi S-II (2019) Classification of gait type based on deep learning using various sensors with smart insole. Sensors (Switzerland) 19(8):1–15

    Article  Google Scholar 

  63. Li X, Makihara Y, Xu C, Yagi Y, Ren M (2019) Joint intensity transformer network for gait recognition robust against clothing and carrying status. IEEE Transac Inform Foren Sec 14(12):3102–3115

    Article  Google Scholar 

  64. Liao R, Yu S, An W, Huang Y (2020) A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn 98(2):1–11. https://doi.org/10.1016/j.patcog.2019.107069

    Article  Google Scholar 

  65. Liao R, Li Z, Bhattacharyya SS, York G (2022) Posemapgait: A model-based gait recognition method with pose estimation maps and graph convolutional networks. Neurocomputing 501:514–528. https://doi.org/10.1016/j.neucom.2022.06.048

    Article  Google Scholar 

  66. Lin J, Chan LLH, Yan H (2015) A decision tree based pedometer and its implementation on the android platform. Proc. fifth international conference on computer science and information technology. p. 73–83

  67. Linda GM, Themozhi G, Bandi SR (2020) Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network. Int J Wavelets Multiresolution Inf Process 18(1):1–29

    Article  MathSciNet  MATH  Google Scholar 

  68. Liu Z, Sarkar S (2006) Improved Gait recognition by Gait dynamics normalization. IEEE Trans Pattern Anal Mach Intell 28(6):863–876

  69. Luo J, Tjahjadi T (2020) Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding. Sensors 20(6):1–24

    Article  Google Scholar 

  70. Luo X, Liu Z, Li S, Shang M, Wang Z (2021) A fast non-negative latent factor model based on generalized momentum method. IEEE Trans Syst, Man, Cybern: Syst 51(1):610–620. https://doi.org/10.1109/tsmc.2018.2875452

    Article  Google Scholar 

  71. Lv Z, Xing X, Wang K, Guan D (2015) Class energy image analysis for video sensor-based gait recognition: A review. Sensors (Switzerland) 15(1):932–964

    Article  Google Scholar 

  72. Madduri A (2021) Human gait recognition using discrete wavelet and discrete cosine and transformation based features. Int J Comput Trends Technol 69(6):22–27

    Article  Google Scholar 

  73. Makhdoomi NA, Gunawan TS, Habaebi MH (2013) Human gait recognition and classification using similarity index for various conditions. IOP Conf Ser Mater Sci Eng 53(1):1–5

    Google Scholar 

  74. Milovanović I, Popović DB (2012) Principal component analysis of gait kinematics data in acute and chronic stroke patients. Comput Math Methods Med 2012:1–9. https://doi.org/10.1155/2012/649743

    Article  MATH  Google Scholar 

  75. Mowbray SD, Nixon MS (2003) Automatic gait recognition via fourier descriptors of deformable objects. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes bioinformatics), 2688. p. 566–573

  76. Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A (2014) Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14(2):3362–3394. https://doi.org/10.3390/s140203362

    Article  Google Scholar 

  77. Nithyakani P, Ferni UM (2020) The systematic review on gait analysis: trends and developments. Eur J Mol Clin Med 07(06):1636–1654

    Google Scholar 

  78. Park G, Lee KM, Koo S (2021) Uniqueness of gait kinematics in a cohort study. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-94815-z

  79. Peinado-Contreras A, Munoz-Organero M (2020) Gait-based identification using deep recurrent neural networks and acceleration patterns. Sensors 20(23):6900 https://doi.org/10.3390/s20236900

  80. Provencher MT, Abdu WA (2000) Giovanni Alfonso Borelli: "Father of spinal biomechanics", Spine (Phila Pa 1976) 25(1):131–136. https://doi.org/10.1097/00007632-200001010-00022

  81. Qi Y, Soh CB, Gunawan E, Low KS, Thomas R (2014) Estimation of spatial-temporal gait parameters using a low-cost ultrasonic motion analysis system. Sensors (Switzerland) 14(8):15434–15457

    Article  Google Scholar 

  82. Qi YJ, Kong YP, Zhang Q (2022) A cross-view gait recognition method using two-way similarity learning. Math Probl Eng 2022:1–14. https://doi.org/10.1155/2022/2674425

    Article  Google Scholar 

  83. Qiao S, Pang S, Zhai X, Wang M, Yu S, Ding T, Cheng X (2021) Human body multiple parts parsing for person Reidentification based on Xception. Int J Comput Intel Syst 14(1):482–490

    Article  Google Scholar 

  84. Rani V, Kumar M, Singh B (2022) Handcrafted features for human gait recognition: CASIA-A dataset. Communications in Computer and Information Science. p. 77–88. https://doi.org/10.1007/978-3-031-21385-4_7

  85. Renani MS, Myers CA, Zandie R, Mahoor MH, Davidson BS, Clary CW (2020) Deep learning in gait parameter prediction for OA and TKA patients wearing IMU sensors. Sensors (Basel, Switzerland) 20(19):1–21

    Google Scholar 

  86. Ricciardi C, Valente AS, Edmund K, Cantoni V, Green R, Fiorillo A, Picone I, Santini S, Cesarelli M (2020) Linear discriminant analysis and principal component analysis to predict coronary artery disease. Health Inform J 26(3):2181–2192. https://doi.org/10.1177/1460458219899210

    Article  Google Scholar 

  87. Saleh AM, Hamoud T (2021) Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation. J Big Data 8(1):1–20. https://doi.org/10.1186/s40537-020-00387-6

    Article  Google Scholar 

  88. San-Segundo R, Echeverry-Correa JD, Salamea-Palacios C, Lebai LS, Pardo JM (2017) I-vector analysis for gait-based person identification using smartphone inertial signals. Pervasive Mob Comput, 38. p. 140–153

  89. Seifert AK, Zoubir AM, Amin MG (2017) Radar-based human gait recognition in cane-assisted walks. 2017 IEEE Radar Conf, RadarConf 2017:1428–1433. https://doi.org/10.1109/RADAR.2017.7944431

    Article  Google Scholar 

  90. Semwal VB, Singha J, Sharma PK, Chauhan A, Behera B (2017) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl 76(22):24457–24475

    Article  Google Scholar 

  91. Sepas-Moghaddam A, Etemad A (2021) Deep gait recognition: a survey. Computer Vision and Pattern Recognition, pp 1–19. http://arxiv.org/abs/2102.09546

  92. Sheth AA, Sharath M, Reddy ASC, K S. (2023) Gait recognition using convolutional neural network. In J Online Biomed Engin (iJOE) 19(01):107–118. https://doi.org/10.3991/ijoe.v19i01.3382

    Article  Google Scholar 

  93. Shirke S, Pawar SS, Shah K (2014) Literature review: model-free human gait recognition. Proc. - 2014 4th Int. Conf. Commun. Syst. Netw. Technol. CSNT 2014. p. 891–895

  94. Si W, Zhang J, Li YD, Tan W, Shao YF, Yang GL (2020) Remote identity verification using gait analysis and face recognition. Wirel Commun Mob Comput 2020:1–10. https://doi.org/10.1155/2020/8815461

    Article  Google Scholar 

  95. Singh JP, Jain S, Arora S, Singh UP (2018) Vision-based gait recognition: A survey. IEEE Access 6:70497–70527. https://doi.org/10.1109/ACCESS.2018.2879896

    Article  Google Scholar 

  96. Singh JP, Jain S, Arora S, Singh UP (2021) A survey of behavioral biometric gait recognition: current success and future perspectives. Arc Comput Meth Eng 28(1):107–148

    Article  Google Scholar 

  97. Sokolova A, Konushin A (2017) Pose-based deep gait recognition. IET Biomet 8(2):1–10

    Google Scholar 

  98. Strukova OV, Myasnikov EV (2019) The choice of methods for the construction of PCA-based features and the selection of SVM parameters for person identification by gait. J Phys Conf Ser 1368(3):032001. https://doi.org/10.1088/1742-6596/1368/3/032001

    Article  Google Scholar 

  99. Sundaresan A, RoyChowdhury A, Chellappa R (2003) A hidden Markov model-based framework for recognition of humans from gait sequences. Proc. international conference on image processing (cat. No.03CH37429), II-93. https://doi.org/10.1109/ICIP.2003.1246624

  100. Tafazzoli F, Bebis G, Louis S, Hussain M (2014) Improving human gait recognition using feature selection. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes bioinformatics), 8888:830–840

  101. Talha M, Soomro HA, Naeem N, Ali E, Kyrarini M (2022) Human identification using a smartphone motion sensor and gait analysis. The15th international conference on PErvasive technologies related to assistive environments. https://doi.org/10.1145/3529190.3529199

  102. Tao D, Li X, Wu X, Maybank SJ (2007) General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10):1700–1715

    Article  Google Scholar 

  103. Thapar D, Nigam A, Aggarwal D, Agarwal P (2018) VGR-net: A view invariant gait recognition network. Proc. IEEE 4th international conference on identity, security, and behavior analysis (ISBA), 1-8

  104. Tong SB, Yz F, Ling HF (2019) Cross-view gait recognition based on a restrictive triplet network. Pattern Recogn Lett 125(2):212–219

    Article  Google Scholar 

  105. Tran L, Hoang T, Nguyen T, Kim H, Choi D (2021) Multi-Model Long Short-Term Memory Network for Gait Recognition Using Window-Based Data Segment. IEEE Access. PP. 1–1. https://doi.org/10.1109/ACCESS.2021.3056880

  106. Ťupa O, Procházka A, Vyšata O, Schätz M, Mares J, Valis M, Mařík V (2015) Motion tracking and gait feature estimation for recognizing Parkinson’s disease using MS Kinect. Biomed Eng Online 14:1–20

    Article  Google Scholar 

  107. Vasudevan P, Faerie - Mattins R, Srivarshan S, Narayanan A, Wadhwani G, Parvathi R, Maheswari R (2023) Gait image classification using Deep Learning Models for medical diagnosis. Comput, Mat Continua 74(3):6039–6063. https://doi.org/10.32604/cmc.2023.032331

    Article  Google Scholar 

  108. Wagner J, Szymański M, Błażkiewicz M, Kaczmarczyk K (2023) Methods for spatiotemporal analysis of human gait based on data from depth sensors. Sensors 23(3):1218. https://doi.org/10.3390/s23031218

    Article  Google Scholar 

  109. Wan C, Wang L, Phoha VV (2018) A survey on gait recognition. ACM Comput Surv 51(5):1–35

    Article  Google Scholar 

  110. Wang J, She M, Nahavandi S, Kouzani A (2010) A review of vision-based gait recognition methods for human identification. Proc. - 2010 digit. Image Comput. Tech. Appl. (DICTA 2010). p. 320–327. https://doi.org/10.1109/DICTA.2010.62

  111. Wang F, Yan L, Xiao J (2019a) Human gait recognition system based on support vector machine algorithm and using wearable sensors. Sensors Mater 31(4):1335–1349

    Article  Google Scholar 

  112. Wang Y, Song C, Huang Y, Wang Z, Wang L (2019b) Learning view invariant gait features with two-stream GAN. Neurocomputing 339:245–254

    Article  Google Scholar 

  113. Wang L, Li Y, Xiong F, Zhang W (2021a) Gait recognition using optical motion capture: A decision fusion based method. Sensors 21(10):1–17. https://doi.org/10.3390/s21103496

    Article  Google Scholar 

  114. Wang Y, Xia Y, Zhang Y (2021b) Beyond view transformation: feature distribution consistent GANs for cross-view gait recognition. Vis Comput 2021:1915–1928. https://doi.org/10.1007/s00371-021-02254-8

    Article  Google Scholar 

  115. Wang Y, Song Q, Ma T, Yao N, Liu R, Wang B (2022a) Research on human gait phase recognition algorithm based on multi-source information fusion. Electronics 12(1):193. https://doi.org/10.3390/electronics12010193

    Article  Google Scholar 

  116. Wang Y, Song Q, Ma T, Yao N, Liu R, Wang B (2022b) Research on human gait phase recognition algorithm based on multi-source information fusion. Electronics 12(1):193. https://doi.org/10.3390/electronics12010193

    Article  Google Scholar 

  117. Wu F, Li X, Zhao W, Ning B (2022) Gait recognition based on the plantar pressure data of ice and snow athletes. Comput Intel Neurosci 2022:1–15. https://doi.org/10.1155/2022/2982894

    Article  Google Scholar 

  118. Xia L, Wang H, Guo W (2019) Gait recognition based on Wasserstein generating adversarial image inpainting network, Journal of Central South University 26:2759–2770

  119. Xu Z, Lu W, Zhang Q, Yeung Y, Chen X (2019) Gait recognition based on capsule network. J Vis Commun Image Represent 59:159–167

    Article  Google Scholar 

  120. Xu C, Makihara Y, Li X, Yagi Y (2023) Occlusion-aware human mesh model-based gait recognition. IEEE transactions on information forensics and security. p. 1–1. https://doi.org/10.1109/tifs.2023.3236181

  121. Yazdi HS, Fariman HJ, Roohi J (2012) Gait recognition based on invariant leg classification using a neuro-fuzzy algorithm as the fusion method. ISRN Artif. Intell. p. 1–9. https://doi.org/10.5402/2012/289721

  122. Yeoh T, Aguirre HE, Tanaka K (2016) Clothing-invariant gait recognition using convolutional neural network. Proc. international symposium on intelligent signal processing and communication systems (ISPACS). p. 1-5

  123. Zeng X, Zhang X, Yang S, Shi Z, Chi C (2021) Gait-based implicit authentication using edge computing and deep learning for mobile devices. Sensors 21(13):4592. https://doi.org/10.3390/s21134592

    Article  Google Scholar 

  124. Zhang J (2012) Gait & Posture Basic gait analysis based on continuous wave radar. Gait Posture 36(4):667–671. https://doi.org/10.1016/j.gaitpost.2012.04.020

    Article  Google Scholar 

  125. Zhang Y, Ma Y (2019) Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia. Comput Biol Med 106(2):33–39

    Article  Google Scholar 

  126. Zhao G, Liu G, Li H, Pietikäinen M (2006) 3D gait recognition using multiple cameras. Proc. 7th international conference on automatic face and gesture recognition (FGR06). p. 529-534

  127. Zheng L, Bie Z, Sun Y, Wang J, Su C, Wang S, Tian Q (2016) Mars: A video benchmark for large-scale person re-identification. Computer Vision – ECCV 2016:868–884. https://doi.org/10.1007/978-3-319-46466-4_52

    Article  Google Scholar 

  128. Zou Q, Wang Y, Wang Q, Zhao Y, Li Q (2020) Deep learning-based gait recognition using smartphones in the wild. IEEE Trans Inf Forensics Secur 15:3197–3212

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Veenu Rani & Munish Kumar

Authors
  1. Veenu Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munish Kumar.

Ethics declarations

Conflict of interest

No

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rani, V., Kumar, M. Human gait recognition: A systematic review. Multimed Tools Appl 82, 37003–37037 (2023). https://doi.org/10.1007/s11042-023-15079-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15079-5

Keywords

Search

Navigation