LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,121
تعداد مقالات10,276
تعداد مشاهده مقاله42,999,564
تعداد دریافت فایل اصل مقاله9,411,939
Molavian, Rozhin, Fatahi, Ali, Abbasi, Hamed, Khezri, Davood. (1402). Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review. سامانه مدیریت نشریات علمی, 13(5), 383-402. doi: 10.31661/jbpe.v0i0.2305-1621
Rozhin Molavian; Ali Fatahi; Hamed Abbasi; Davood Khezri. "Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review". سامانه مدیریت نشریات علمی, 13, 5, 1402, 383-402. doi: 10.31661/jbpe.v0i0.2305-1621
Molavian, Rozhin, Fatahi, Ali, Abbasi, Hamed, Khezri, Davood. (1402). 'Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review', سامانه مدیریت نشریات علمی, 13(5), pp. 383-402. doi: 10.31661/jbpe.v0i0.2305-1621
Molavian, Rozhin, Fatahi, Ali, Abbasi, Hamed, Khezri, Davood. Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review. سامانه مدیریت نشریات علمی, 1402; 13(5): 383-402. doi: 10.31661/jbpe.v0i0.2305-1621

Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

Journal of Biomedical Physics and Engineering
دوره 13، شماره 5، آذر و دی 2023، صفحه 383-402    اصل مقاله (852.83 K)
نوع مقاله: Systematic Review
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2305-1621
نویسندگان
Rozhin Molavian1؛ Ali Fatahi* 1؛ Hamed Abbasi2؛ Davood Khezri3
1Department of Sport Biomechanics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2Department of Sport Injuries and Corrective Exercises, Sport Sciences Research Institute, Tehran, Iran
3Department of Sport Biomechanics and Technology, Sport Science Research Institute, Tehran, Iran
چکیده
Background: Artificial neural network helps humans in a wide range of activities, such as sports.
Objective: This paper aims to investigate the effect of artificial intelligence on decision-making related to human gait and sports biomechanics, using computer-based software, and to investigate the impact of artificial intelligence on individuals’ biomechanics during gait and sports performance.
Material and Methods: This review was conducted in compliance with the PRISMA guidelines. Abstracts and citations were identified through a search based on Science Direct, Google Scholar, PubMed, Elsevier, Springer Link, Web of Science, and Scopus search engines from 1995 up to 2023 to obtain relevant literature about the impact of artificial intelligence on biomechanics. A total of 1000 articles were found related to biomechanical characteristics of gait and sport and 26 articles were directly pertinent to the subject.
Results: The extent of the application of artificial intelligence in sports biomechanics in various fields. In addition, various variables in the fields of kinematics, kinetics, and the field of time can be investigated based on artificial intelligence. Conventional computational techniques are limited by the inability to process data in its raw form. Artificial Intelligence (AI) and Machine Learning (ML) techniques can handle complex and high-dimensional data. 
Conclusion: The utilization of specialized systems and neural networks in gait analysis has shown great potential in sports performance analysis. Integrating AI into this field would be a significant advancement in sport biomechanics. Coaches and athletes can develop more precise training regimens with specialized performance prediction models.

تازه های تحقیق

Rozhin Molavian (Google Scholar)

Ali Fatahi (Google Scholar)

کلیدواژه‌ها
Artificial Intelligence؛ Biomechanics؛ Machine Learning؛ Performance؛ Sports
سایر فایل های مرتبط با مقاله
مراجع
  1. Lapham AC, Bartlett RM. The use of artificial intelligence in the analysis of sports performance: a review of applications in human gait analysis and future directions for sports biomechanics. J Sports Sci. 1995;13(3):229-37. doi: 10.1080/02640419508732232. PubMed PMID: 7563290.
  2. Yeadon M, Challis J. The future of performance‐related sports biomechanics research. J Sports Sci. 1994;12(1):3-32. doi: 10.1080/02640419408732156. PubMed PMID: 8158746.
  3. Nadikattu RR. Implementation of new ways of artificial intelligence in sports. J Xidian Univ. 2020;14(5):5983-97. doi: 10.2139/ssrn.3620017.
  4. Sennaar K. Artificial intelligence in sports–current and future applications. 2019. Available at: https://emerj.com/ai-sector-overviews/artificial-intelligence-in-sports/.
  5. Khera P, Kumar N. Role of machine learning in gait analysis: a review. J Med Eng Technol. 2020;44(8):441-67. doi: 10.1080/03091902.2020.1822940. PubMed PMID: 33078988.
  6. Kugler P, Schuldhaus D, Jensen U, Eskofier B. Mobile recording system for sport applications. Proceedings of the 8th international symposium on computer science in sport; Liverpool: IACSS; 2011.
  7. Eskofier B, Tuexen S, Kugler P, Jensen U, Wright I. Development of Pattern Recognition Methods for Golf Swing Motion Analysis. Proceedings of 8th International Symposium of the International Association of Computer Science in Sports; China: IACSS; 2011. p. 21-24.
  8. Novatchkov H, Baca A. Artificial intelligence in sports on the example of weight training. J Sports Sci Med. 2013;12(1):27. PubMed PMID: 24149722. PubMed PMCID: PMC3761781.
  9. Perl J. Artificial neural networks in motor control research. Clin Biomech. 2004;19(9):873-5. doi: 10.1016/j.clinbiomech.2004.04.010. PubMed PMID: 15475119.
  10. McCullagh J. Data mining in sport: A neural network approach. Int J Sports Sci Eng. 2010;4(3):131-8.
  11. Acikkar M, Akay MF, Ozgunen KT, Aydin K, Kurdak SS. Support vector machines for aerobic fitness prediction of athletes. Expert Syst Appl. 2009;36(2):3596-602. doi: 10.1016/j.eswa.2008.02.002.
  12. Eskofier B, Wagner M, Munson I, Oleson M. Embedded Classification of Speed and Inclination during Running. Int J Comput Sci Sport. 2010;9(1):4-16.
  13. Fischer A, Do M, Stein T, Asfour T, Dillmann R, Schwameder H. Recognition of Individual Kinematic Patterns during Walking and Running-A Comparison of Artificial Neural Networks and Support Vector Machines. Int J Comput Sci Sport. 2011;10(1):63-7.
  14. Nutt J, Marsden C, Thompson P. Human walking and higher level gait disorders, particularly in the elderly. Neurology. 1993;43:268-79. doi: 10.1212/wnl.43.2.268. PubMed PMID: 8437689.
  15. Prakash C, Gupta K, Mittal A, Kumar R, Laxmi V. Passive marker based optical system for gait kinematics for lower extremity. Procedia Comput Sci. 2015;45:176-85. doi: 10.1016/j.procs.2015.03.116.
  16. Johnson AY, Bobick AF. A multi-view method for gait recognition using static body parameters. International Conference on Audio-and Video-Based Biometric Person Authentication; Berlin, Heidelberg: Springer; 2001. p. 301-11.
  17. 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. doi: 10.1007/s10462-016-9514-6.
  18. Dolatabadi E, Taati B, Mihailidis A. An automated classification of pathological gait using unobtrusive sensing technology. IEEE Trans Neural Sys Rehab Eng. 2017;25(12):2336-46. doi: 10.1109/TNSRE.2017.2736939. PubMed PMID: 28792901.
  19. Lai DT, Levinger P, Begg RK, Gilleard WL, Palaniswami M. Automatic recognition of gait patterns exhibiting patellofemoral pain syndrome using a support vector machine approach. IEEE Trans Inf Technol Biomed. 2009;13(5):810-7. doi: 10.1109/TITB.2009.2022927. PubMed PMID: 19447723.
  20. Amirouche FM, Ider SK, Trimble J. Analytical method for the analysis and simulation of human locomotion. J Biomech Eng. 1990;112(4):379-86. doi: 10.1115/1.2891200. PubMed PMID: 2273863.
  21. Woodle A, Elliott S. The Vector System for dynamic gait analysis. Clin Podiatr Med Surg. 1993;10(3):485-500. PubMed PMID: 8364851.
  22. Li B, Xu X. Application of artificial intelligence in basketball sport. J Educ Health Sport. 2021;11(7):54-67. doi: 10.12775/JEHS.2021.11.07.005.
  23. Nunes Rodrigues AC, Santos Pereira A, Sousa Mendes RM, Araújo AG, Santos Couceiro M, Figueiredo AJ. Using artificial intelligence for pattern recognition in a sports context. 2020;20(11):3040. doi: 10.3390/s20113040. PubMed PMID: 32471189. PubMed PMCID: PMC7309132.
  24. Ardern CL, Büttner F, Andrade R, Weir A, Ashe MC, Holden S, et al. Implementing the 27 PRISMA 2020 Statement items for systematic reviews in the sport and exercise medicine, musculoskeletal rehabilitation and sports science fields: the PERSiST (implementing Prisma in Exercise, Rehabilitation, Sport medicine and SporTs science) guidance. Br J Sports Med. 2022;56(4):175-95. doi: 10.1136/bjsports-2021-103987. PubMed PMID: 34625401. PubMed PMCID: PMC8862073.
  25. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377-84. doi: 10.1136/jech.52.6.377. PubMed PMID: 9764259. PubMed PMCID: PMC1756728.
  26. Şah M, Direkoğlu C. Review and evaluation of player detection methods in field sports: Comparing conventional and deep learning based methods. Multimed Tools Appl. 2023;82(9):13141-65. doi: 10.1007/s11042-021-11071-z.
  27. Ren H. Sports video athlete detection based on deep learning. Neural Comput Appl. 2023;35(6):4201-10. doi: 10.1007/s00521-022-07077-9.
  28. Schrapf N, Hassan A, Wiesmeyr S, Tilp M. An Artificial Neural Network Predicts Setter’s Setting Behavior in Volleyball Similar or Better than Experts. IFAC-PapersOnLine. 2022;55(20):612-7. doi: 10.1016/j.ifacol.2022.09.163.
  29. Lee HS, Lee J. Applying artificial intelligence in physical education and future perspectives. Sustainability. 2021;13(1):351. doi: 10.3390/su13010351.
  30. Liu J, Wang L, Zhou H. The application of human–computer interaction technology fused with artificial intelligence in sports moving target detection education for college athlete. Front Psychol. 2021;12:677590. doi: 10.3389/fpsyg.2021.677590. PubMed PMID: 34366996. PubMed PMCID: PMC8339562.
  31. Tuo X, Li T. Construction of a neural network model for performance prediction in shot put athletes. J Phys: Conf Ser. 2020;1684(1):012006. doi: 10.1088/1742-6596/1684/1/012006.
  32. Burić M, Pobar M, Ivašić-Kos M. Object detection in sports videos. 1st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO); Opatija, Croatia: IEEE; 2018. p. 1034-9.
  33. Acuna D. Towards real-time detection and tracking of basketball players using deep neural networks. Proceedings of the 31st Conference on Neural Information Processing Systems; Long Beach, CA, USA: NIPS; 2017.
  34. Tümer AE, Koçer S. Prediction of team league’s rankings in volleyball by artificial neural network method. Int J Perform Anal Sport. 2017;17(3):202-11. doi: 10.1080/24748668.2017.1331570.
  35. Lu K, Chen J, Little JJ, He H. Light cascaded convolutional neural networks for accurate player detection [Internet]. arXiv [Preprint]. 2017 [cited 2017 Sep 29]. Available from: https://arxiv.org/abs/1709.10230.
  36. Phinyomark A, Hettinga BA, Osis ST, Ferber R. Gender and age-related differences in bilateral lower extremity mechanics during treadmill running. PloS One. 2014;9(8):e105246. doi: 10.1371/journal.pone.0105246. PubMed PMID: 25137240. PubMed PMCID: PMC4138160.
  37. Pobar M, Ivasic-Kos M. Active player detection in handball scenes based on activity measures. Sensors. 2020;20(5):1475. doi: 10.3390/s20051475. PubMed PMID: 32182649. PubMed PMCID: PMC7085540.
  38. Zhao CY, Zhang XG, Guo Q. The application of machine-learning on lower limb motion analysis in human exoskeleton system. Social Robotics: 4th International Conference ICSR; Chengdu, China: ICSR; 2012. p. 600-11.
  39. Rozumalski A, Schwartz MH. Crouch gait patterns defined using k-means cluster analysis are related to underlying clinical pathology. Gait Posture. 2009;30(2):155-60. doi: 10.1016/j.gaitpost.2009.05.010. PubMed PMID: 19535249.
  40. Kim JJ, Lee JJ. Gait adaptation method of biped robot for various terrains using central pattern generator (CPG) and learning mechanism. International Conference on Control, Automation and Systems; Seoul, Korea (South): IEEE; 2007.
  41. Silva AJ, Costa AM, Oliveira PM, Reis VM, Saavedra J, Perl J, et al. The use of neural network technology to model swimming performance. J Sports Sci Med. 2007;6(1):117. PubMed PMID: 24149233. PubMed PMCID: PMC3778687.
  42. Xu G, Zhang Y, Begg R. Mining gait pattern for clinical locomotion diagnosis based on clustering techniques. International Conference on Advanced Data Mining and Applications; China: ADMA; 2006.
  43. Pretorius A, Parry DA. Human decision making and artificial intelligence: a comparison in the domain of sports prediction. Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists; 2016. p. 1-10.
  44. Juang JG. Fuzzy neural network approaches for robotic gait synthesis. IEEE Trans Syst Man Cybern B Cybern. 2000;30(4):594-601. doi: 10.1109/3477.865178. PubMed PMID: 18252391.
  45. Dorschky E, Nitschke M, Martindale CF, Van Den Bogert AJ, Koelewijn AD, Eskofier BM. CNN-based estimation of sagittal plane walking and running biomechanics from measured and simulated inertial sensor data. Front Bioeng Biotechnol. 2020;8:604. doi: 10.3389/fbioe.2020.00604. PubMed PMID: 32671032. PubMed PMCID: PMC7333079.
  46. Lehuger A, Duffner S, Garcia C. A robust method for automatic player detection in sport videos. Paris, France: Orange Labs; 2007.
  47. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [Internet]. arXiv [Preprint]. 2014 [cited 2014 Sep 4]. Available from: https://arxiv.org/abs/1409.1556.
  48. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84-90. doi: 10.1145/3065386.
  49. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell. 2017;39(6):1137-49. doi: 10.1109/TPAMI.2016.2577031. PubMed PMID: 27295650.
  50. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection [Internet]. arXiv [Preprint]. 2015 [cited 2015 Jun 8]. Available from: https://arxiv.org/abs/1506.02640.
  51. Huang YC, Liao IN, Chen CH, İk TU, Peng WC. Tracknet: A deep learning network for tracking high-speed and tiny objects in sports applications. 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS); Taipei, Taiwan: IEEE; 2019.
  52. Song H, Montenegro-Marin CE, krishnamoorthy S. Secure prediction and assessment of sports injuries using deep learning based convolutional neural network. J Ambient Intell Humaniz Comput. 2021;12:3399-410. doi: 10.1007/s12652-020-02560-4.
  53. Valueva MV, Nagornov N, Lyakhov PA, Valuev GV, Chervyakov NI. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Math Comput Simul. 2020;177:232-43. doi: 10.1016/j.matcom.2020.04.031.
  54. Mundt M, Koeppe A, David S, Witter T, Bamer F, Potthast W, et al. Estimation of gait mechanics based on simulated and measured IMU data using an artificial neural network. Front Bioeng Biotechnol. 2020;8:41. doi: 10.3389/fbioe.2020.00041. PubMed PMID: 32117923. PubMed PMCID: PMC7013109.
  55. Chau T. A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods. Gait Posture. 2001;13(1):49-66. doi: 10.1016/s0966-6362(00)00094-1. PubMed PMID: 11166554.
  56. Choi S, Youn IH, LeMay R, Burns S, Youn JH. Biometric gait recognition based on wireless acceleration sensor using k-nearest neighbor classification. International Conference on Computing, Networking and Communications (ICNC); Honolulu, HI, USA: IEEE; 2014.
  57. Prentice S, Patla A, Stacey D. Artificial neural network model for the generation of muscle activation patterns for human locomotion. J Electromyogr kinesiol. 2001;11(1):19-30. doi: 10.1016/s1050-6411(00)00038-9. PubMed PMID: 11166605.
  58. Popovic DB, Popovic MB. Design of a control for a neural prosthesis for walking: Use of artificial neural networks. 8th Seminar on Neural Network Applications in Electrical Engineering; Belgrade, Serbia: IEEE; 2006.
  59. Schöllhorn W, Jäger J, Janssen D. Artificial neural network models of sports motions. In: Routledge handbook of biomechanics and human movement science. New York: Routledge; 2008. p. 50-64.
  60. Begg R, Kamruzzaman J. A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech. 2005;38(3):401-8. doi: 10.1016/j.jbiomech.2004.05.002. PubMed PMID: 15652537.
  61. Nukala BT, Shibuya N, Rodriguez A, Tsay J, Lopez J, Nguyen T, et al. An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ANN) and support vector machine (SVM) algorithms. Open J Appl Biosens. 2015;3(4):29. doi: 10.4236/ojab.2014.34004.
  62. Janssen D, Schöllhorn WI, Lubienetzki J, Fölling K, Kokenge H, Davids K. Recognition of emotions in gait patterns by means of artificial neural nets. J Nonverbal Behav. 2008;32:79-92. doi: 10.1007/s10919-007-0045-3.
  63. Johnson AY, Bobick AF. A multi-view method for gait recognition using static body parameters. International Conference on Audio- and Video-Based Biometric Person Authentication; Berlin, Heidelberg: Springer; 2001. p. 301-11.
  64. Baker R. Gait analysis methods in rehabilitation. J Neuroeng Rehab. 2006;3:1-10. doi: 10.1186/1743-0003-3-4. PubMed PMID: 16512912. PubMed PMCID: PMC1421413.
  65. Zhang D, Wang Y, Zhang Z, Hu M. Estimation of view angles for gait using a robust regression method. Multimed Tools Appl. 2013;65:419-39. doi: 10.1007/s11042-012-1045-9.
  66. Sigal L, Balan AO, Black MJ. Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int J Comput Vis. 2010;87(1-2):4-27. doi: 10.1007/s11263-009-0273-6.
  67. Heinen MR, Osório FS. Gait control generation for physically based simulated robots using genetic algorithms. Ibero-American Conference on Artificial Intelligence; Berlin, Heidelberg: Springer; 2006. p. 562-71.
  68. Wawrzyński P. Autonomous reinforcement learning with experience replay for humanoid gait optimization. Procedia Comput Sci. 2012;13:205-11. doi: 10.1016/j.procs.2012.09.130.
آمار
تعداد مشاهده مقاله: 21,349
تعداد دریافت فایل اصل مقاله: 984


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب