A Systematic Review of Advancement in Gait Analysis Techniques
Article Sidebar
Main Article Content
Abstract
The examination and the survey of how a person moves, particularly the way of life of walking and running. It entails studying and quantifying a person's gait in terms of their stride length, cadence, foot position, and movement of various body joints. Wearable technology makes it possible to monitor the gait pattern continually while moving about freely. The direction line, gait line, foot line, foot angle, principle line, step length, step breadth, and displacement value obtained from the gyro and accelerated sensors coupled to the shank and thigh are all used to analyze the gait pattern. There has been a lot of research on this method of recognizing people by the way they walk.
The two most crucial facts are that OpenPose, a 2D multi-person posture estimation library, can detect 135 critical body locations without the requirement for fiducial markers, and that smartphone cameras can detect the gait pattern without the use of physical markers. In addition, lower extremity sagittal joint angles, spatiotemporal gait parameters, and timings of gait events were independently determined for motion capture. Gait analysis systems use portable, readily available cameras to measure gait characteristics. The pace of gait, length of steps, time of steps, cadence of steps, and the period of stance are the most crucial factors. Recently, the top standard for the examination of gait was used to evaluate the schemes based on two camera usage to evaluate the framework of different gait patterns.
The precision of the examination of SCA is being increased by data scientists through the development of AI-based computer algorithms. To increase individualization, Bertillon measured the body and faces of several convicts in 1883.
Article Details
Copyright (c) 2025 Jha A
This work is licensed under a Creative Commons Attribution 4.0 International License.
Sethi D, Bharti S, Prakash C. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work. Artif Intell Med. 2022;129:102314. Available from: https://doi.org/10.1016/j.artmed.2022.102314
Prakash C, Kumar R, Mittal N. Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif Intell Rev. 2018;49(1):1-40. Available from: https://link.springer.com/article/10.1007/s10462-016-9514-6
Institute of Electrical and Electronics Engineers, IEEE Computer Society, IEEE Biometrics Council. 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG): 4-8 May 2015, Ljublijana, Slovenia. Piscataway (NJ): IEEE; 2015. Available from: https://www.proceedings.com/26816.html
Del Din S, Galna B, Godfrey A, Bekkers EM, Pelosin E, Nieuwhof F, et al. Analysis of free-living gait in older adults with and without Parkinson’s disease and with and without a history of falls: identifying generic and disease specific characteristics. J Gerontol A Biol Sci Med Sci. 2019;74(4):500-506. Available from: https://doi.org/10.1093/gerona/glx254
Muro-de-la-Herran A, García-Zapirain B, Méndez-Zorrilla A. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors (Basel). 2014;14(2):3362-3394. Available from: https://doi.org/10.3390/s140203362
Badiye A, Kathane P, Krishan K. Forensic Gait Analysis. Forensic Gait Analysis. 2022;1-342. Available from: https://pubmed.ncbi.nlm.nih.gov/32491616/
Topham LK, Khan W, Al-Jumeily D, Hussain A. Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models. ACM Comput Surv. 2022;55(6):1-35. Available from: http://dx.doi.org/10.1145/3533384
Chen J. Gait correlation analysis based human identification. Sci World J. 2014;2014:168275. Available from: https://doi.org/10.1155/2014/168275
Galetto M, Gastaldi L, Lisco G, Mastrogiacomo L, Pastorelli S. Accuracy evaluation of a new stereophotogrammetry-based functional method for joint kinematic analysis in biomechanics. Proc Inst Mech Eng H. 2014;228(11):1183-1192. Available from: https://doi.org/10.1177/0954411914559736
Gastaldi L, Pastorelli S, Frassinelli S. A biomechanical approach to paralympic cross-country sit-ski racing. Clin J Sport Med. 2012;22(1):58-64. Available from: https://sponet.de/Record/4024537
Agostini V, Knaflitz M. Statistical gait analysis. In: Distributed Diagnosis and Home Healthcare (D2H2). Vol. II. Stevenson Ranch (CA): American Scientific Publishers; 2012;99-121. Available from: https://iris.polito.it/handle/11583/2496001
Tadano S, Takeda R, Miyagawa H. Three dimensional gait analysis using wearable acceleration and gyro sensors based on quaternion calculations. Sensors (Basel). 2013;13(7):9321-9343. Available from: https://doi.org/10.3390/s130709321
Mayagoitia RE, Nene AV, Veltink PH. Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. J Biomech. 2002;35(4):537-542. Available from: http://dx.doi.org/10.1016/S0021-9290(01)00231-7
Favre J, Jolles BM, Aissaoui R, Aminian K. Ambulatory measurement of 3D knee joint angle. J Biomech. 2008;41(5):1029–35. Available from: https://doi.org/10.1016/j.jbiomech.2007.12.003
Dejnabadi H, Jolles BM, Casanova E, Fua P, Aminian K. Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors. IEEE Trans Biomed Eng. 2006;53(7):1385-1393. Available from: https://doi.org/10.1109/tbme.2006.873678
Zhu R, Zhou Z. A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. IEEE Trans Neural Syst Rehabil Eng. 2004;12(3):295-302. Available from: https://doi.org/10.1109/tnsre.2004.827825
Gunasekar T, Raghavendran P. The Mohand Transform Approach to Fractional Integro-Differential Equations. J Comput Anal Appl. 2024;33(1):358-371. Available from: https://www.researchgate.net/publication/378101073_The_Mohand_Transform_Approach_to_Fractional_Integro-Differential_Equations
Roetenberg D, Slycke PJ, Veltink PH. Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE Trans Biomed Eng. 2007;54(5):883-890. Available from: https://doi.org/10.1109/tbme.2006.889184
Giansanti D, Macellari V, Maccioni G, Cappozzo A. Is it feasible to reconstruct body segment 3-D position and orientation using accelerometric data? IEEE Trans Biomed Eng. 2003;50(4):476-483. Available from: https://doi.org/10.1109/tbme.2003.809490
Dai R, Stein RB, Andrews BJ, James KB, Wieler M. Application of tilt sensors in Functional electrical stimulation. IEEE Trans Rehabil Eng. 1996;4(1):63-72. Available from: https://doi.org/10.1109/86.506403
O’Donovan KJ, Kamnik R, O’Keeffe DT, Lyons GM. An inertial and magnetic sensor based technique for joint angle measurement. J Biomech. 2007;40(12):2604-2611. Available from: https://doi.org/10.1016/j.jbiomech.2006.12.010
Choi JH, Cho J, Park JH, Eun JM, Kim MS. An efficient gait phase detection device based on magnetic sensor array. In: Ibrahim F, Osman NA, Usman J, Kadri NA, editors. 4th Kuala Lumpur International Conference on Biomedical Engineering 2008. Berlin: Springer; 2008;778-781. Available from: https://link.springer.com/chapter/10.1007/978-3-540-69139-6_193
Rainoldi A, Melchiorri G, Caruso I. A method for positioning electrodes during surface EMG recordings in lower limb muscles. J Neurosci Methods. 2004;134(1):37-43. Available from: https://doi.org/10.1016/j.jneumeth.2003.10.014
Colombo G, Wirz M, Dietz V. Driven gait orthosis for improvement of locomotor training in paraplegic patients. Spinal Cord. 2001;39(5):252-255. Available from: https://doi.org/10.1038/sj.sc.3101154
Erni T, Colombo G. Locomotor training in paraplegic patients: A new approach to assess changes in leg muscle EMG patterns. Electroencephalogr Clin Neurophysiol. 1998;109(2):135-139. Available from: https://doi.org/10.1016/s0924-980x(98)00005-8
Kim KJ, Chang YM, Yoon SK, Hyun J. A novel piezoelectric PVDF film-based physiological sensing belt for a complementary respiration and heartbeat monitoring system. Integr Ferroelectr. 2009;107(1):53–68. Available from: http://dx.doi.org/10.1080/10584580903324493
Taya M, Kim WJ, Ono K. Piezoresistivity of a short fiber/elastomer matrix composite. Mech Mater. 1998;28(1–2):53-59. Available from: https://depts.washington.edu/cims/publications/pdf/ec1.pdf
Scilingo EP, Lorussi F, Mazzoldi A, De Rossi D. Strain sensing fabrics for wearable kinaesthetic systems. IEEE Sens J. 2002;3(4):460-467. Available from: http://dx.doi.org/10.1109/JSEN.2003.815771
Nicol AC. A new flexible electrogoniometer with wide-spread applications. In: Jonsson B, editor. International Series on Biomechanics X-B. Champaign (IL): Human Kinetics Publishers Inc.; 1987;1029-1033.
Rowe PJ, Nicol AC, Kelly IG. Flexible goniometer computer system for the assessment of hip function. Clin Biomech. 1989;4(1):68-72. Available from: https://doi.org/10.1016/0268-0033(89)90041-7
Donno M, Palange E, Di Nicola F, Bucci G, Ciancetta F. A flexible optical fiber goniometer for dynamic angular measurements. In: Proceedings of the IEEE Instrument and Measurement Technology Conference, Warsaw, Poland; 2007;1-5. Available from: http://dx.doi.org/10.1109/IMTC.2007.379176
Laskoski GT, Martins LDL, Pichorim SF, Abatti PJ. Development of a telemetric goniometer. In: Dössel O, Schlegel WC, editors. World Congress on Medical Physics and Biomedical Engineering. Munich, Germany; 2009;25/IX. 227-30. Available from: http://dx.doi.org/10.1007/978-3-642-03889-1_61
Santic A, Bilas V, Lackovic I. A system for force measurements in feet and crutches during normal and pathological gait. Period Biol. 2002;104:305-310. Available from: https://www.researchgate.net/publication/236148842_A_system_for_force_measurements_in_feet_and_crutches_during_normal_and_pathological_gait
Hessert MJ, Vyas M, Leach J, Hu K, Lipsitz LA, Novak V. Foot pressure distribution during walking in young and old adults. BMC Geriatr. 2005;5:8. Available from: https://doi.org/10.1186/1471-2318-5-8
Kobayashi K, Gransberg L, Knutsson E, Nolen P. A new system for three-dimensional gait recording using electromagnetic tracking. Gait Posture. 1997;6(1):63-75. Available from: https://doi.org/10.1016/S0966-6362(96)01102-2
Bull AMJ, Berkshire FH, Amis AA. Accuracy of an electromagnetic measurement device and application to the measurement and description of knee joint motion. J Eng Med. 1998;212(6):347-355. Available from: https://doi.org/10.1243/0954411981534123
Muro-de-la-Herran A, García-Zapirain B, Méndez-Zorrilla A. Gait analysis methods: An overview of wearable and non-wearable systems. Sensors (Basel). 2014;14(2):3362-3394. Available from: https://doi.org/10.3390/s140203362
Kusakunniran W, Wu Q, Zhang J, Li H. Support Vector Regression for multi-view gait recognition based on local motion feature selection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2010 Jun 13–18; San Francisco, CA, USA 974-81. Available from: https://doi.org/10.1109/CVPR.2010.5540113
Arias-Enriquez O, Chacon-Murguia MI, Sandoval-Rodriguez R. Kinematic analysis of gait cycle using a fuzzy system for medical diagnosis. In: 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS); 2012 Aug 6–8; Berkeley, CA, USA 1-6.
Iwashita Y, Kurazume R, Ogawara K. Expanding gait identification methods from straight to curved trajectories. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV); 2013 Jan 15–17; Tampa, FL, USA. 193-9. Available from: https://kyushu-u.elsevierpure.com/en/publications/expanding-gait-identification-methods-from-straight-to-curved-tra
Gabel M, Gilad-Bachrach R, Renshaw E, Schuster A. Full body gait analysis with Kinect. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2012 Aug 28–Sep 1; San Diego, CA, USA. 1964-1967. Available from: https://ieeexplore.ieee.org/document/6346340
Clark RA, Pua YH, Bryant AL, Hunt MA. Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining. Gait Posture. 2013;38(4):1064-1066. Available from: https://doi.org/10.1016/j.gaitpost.2013.03.029
Robertson G, Kamen G, Caldwell G, Hamill J, Whittlesey S. Research Methods in Biomechanics. 2nd ed. Champaign (IL): Human Kinetics; 2013. Available from: http://www.humankinetics.com/products/All-products/research-methods-in-biomechanics-2nd-edition
Middleton L, Buss AA, Bazin A, Nixon MS. A floor sensor system for gait recognition. In: 2005 4th IEEE Workshop on Automatic Identification Advanced Technologies; 2005 Oct 17–18; Buffalo, NY, USA. 171-176. Available from: https://www.researchgate.net/publication/313347931_A_floor_sensor_system_for_gait_recognition
Stenum J, Rossi C, Roemmich RT. Two-dimensional video-based analysis of human gait using pose estimation. PLoS Comput Biol. 2021;17(4):e1008935. Available from: https://doi.org/10.1371/journal.pcbi.1008935
Viswakumar A, Rajagopalan V, Ray T, Gottipati P, Parimi C. Development of a robust, simple, and affordable human gait analysis system using bottom-up pose estimation with a smartphone camera. Front Physiol. 2022;12:784865. Available from: https://doi.org/10.3389/fphys.2021.784865
Ning G, Liu P, Fan X, Zhang C. A top-down approach to articulated human pose estimation and tracking. arXiv [Preprint]. 2019 Jan 23. Available from: https://doi.org/10.48550/arXiv.1901.07680
Li M, Zhou Z, Li J, Liu X. Bottom-up pose estimation of multiple person with bounding box constraint. arXiv [Preprint]. 2018 Jul 26. Available from: https://doi.org/10.48550/arXiv.1807.09972
Sipari D, Chaparro-Rico BDM, Cafolla D. SANE (Easy Gait Analysis System): Towards an AI-assisted automatic gait-analysis. Int J Environ Res Public Health. 2022;19(16):10032. Available from: https://doi.org/10.3390/ijerph191610032
Steinert A, Sattler I, Otte K, Röhling H, Mansow-Model S, Müller-Werdan U. Using new camera-based technologies for gait analysis in older adults in comparison to the established GAITrite system. Sensors (Basel). 2020;20(1):125. Available from: https://doi.org/10.3390/s20010125
Risinger DM, Saks MJ, Thompson WC, Rosenthal R. The Daubert/Kumho implications of observer effects in forensic science: Hidden problems of expectation and suggestion. Calif Law Rev. 2002;90(1):1-56. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=301408
Raab FH, Blood EB, Steiner TC, Jones HR. Magnetic position and orientation tracking system. IEEE Trans Aerosp Electron Syst. 1979;15(5):709-718. Available from: https://ohiostate.pressbooks.pub/app/uploads/sites/45/2020/01/raabetal.pdf