Abstract
The rapid development of economy has gained the attention of people towards physical health singing various physical exercise activities. These physical activities may lead to the joint damage which is studied in this part of the research. In order to study the causes of joint damage, biomechanical research is carried out for the human running process, and the internal law of running and the force characteristics of limb joints are analyzed through experimental research. This article is mainly based on the research based on the motion parameters measured by the treadmill at a speed of 8 km/h, establishing kinematics and dynamics models, and analyzing the motion characteristics of the human hip joint at different speeds. The data is filtered to obtain changes in running joint rotation angle and limb acceleration. The multirigid body model is combined and simmechanics is utilized for simulation. The experimental results show that the experimental curve at a speed of 8 km/h has similarities and differences with the simulation curve. The value of the simulation curve is slightly larger than that of the experimental curve, and the simulation curve has a smoother trajectory. This analysis proves the mathematical model of the human joint rotation angle and its correctness to simulate human movement.
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References
Bailey R, Cope E, Parnell D (2015) Realising the benefits of sports and physical activity: the human capital model. RETOS Nuevas Tendencias En Educación Física, Deporte y Recreación 28:147–154
Barshan B, Yurtman A (2020) Classifying daily and sports activities invariantly to the positioning of wearable motion sensor units. IEEE Internet Things J 7(6):4801–4815
Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P (2021) Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. In: Abd-El-Latif AA (ed) Computational Intelligence and Neuroscience, vol 2021. Hindawi Limited, London, pp 1–11. https://doi.org/10.1155/2021/8387680
Camomilla V, Bergamini E, Fantozzi S, Vannozzi G (2015) In-field use of wearable magneto-inertial sensors for sports performance evaluation. In: ISBS-conference proceedings archive
Chakraborty C, Sarkar C, Sinha D (2021) Design of a priority based local energy market using blockchain technology. In: Michael Faraday IET international summit 2020 (MFIIS 2020), vol 2020, IET, pp. 197–201.
Derungs A, Soller S, Weishäupl A, Bleuel J, Berschin G, Amft O (2018) Regression-based, mistake-driven movement skill estimation in Nordic Walking using wearable inertial sensors. In: 2018 IEEE international conference on pervasive computing and communications (PerCom). IEEE, pp 1–10
Dhiman G, Oliva D, Kaur A, Singh KK, Vimal S, Sharma A, Cengiz K (2021) BEPO: a novel binary emperor penguin optimizer for automatic feature selection. Knowl-Based Syst 211:106560
Espinosa HG, Lee J, James DA (2015) The inertial sensor: a base platform for wider adoption in sports science applications. J Fit Res 4(1):13–20
Gilchrist P, Wheaton B (2017) The social benefits of informal and lifestyle sports: a research agenda. Int J Sport Policy Polit 9:1–10
Gurchiek RD, Don HSRA, Watagoda LCP, McGinnis RS, van Werkhoven H, Needle AR et al (2019) Sprint assessment using machine learning and a wearable accelerometer. J Appl Biomech 35(2):164–169
He J, Gao F (2020) Mechanism, actuation, perception, and control of highly dynamic multilegged robots: a review. Chin J Mech Eng 33(1):1–30
Hu X, Wang Z, Tian R (2020) Calculation of the dynamic wind-induced deflection response of overhead lines: establishment and analysis of the multi-rigid-body model. IEEE Access 8:180883–180895
Johnston W, O’Reilly M, Argent R, Caulfield B (2019) Reliability, validity and utility of inertial sensor systems for postural control assessment in sport science and medicine applications: a systematic review. Sports Med 49(5):783–818
Kos A, Umek A (2018) Wearable sensor devices for prevention and rehabilitation in healthcare: swimming exercise with real-time therapist feedback. IEEE Internet Things J 6(2):1331–1341
Lapinski M, Brum Medeiros C, Moxley Scarborough D, Berkson E, Gill TJ, Kepple T, Paradiso JA (2019) A wide-range, wireless wearable inertial motion sensing system for capturing fast athletic biomechanics in overhead pitching. Sensors 19(17):3637
Lehmann T, Lorz A, Schleichardt A, Naundorf F, Witte K (2020) A multi-body model of a springboard in gymnastics. Sci Gymn J 12(3):265–275
Letsch F, Jirak D, Wermter S (2019) Localizing salient body motion in multi-person scenes using convolutional neural networks. Neurocomputing 330:449–464
Li X, Liu G, Song C, Fu X, Ma S, Wan Q (2020) Rigid-body dynamic analysis of multi-stage planetary roller screw mechanism. Xibei Gongye Daxue Xuebao/j Northwest Polytech Univ 38(5):1001–1009
Lim SM, Oh HC, Kim J, Lee J, Park J (2018) LSTM-guided coaching assistant for table tennis practice. Sensors 18(12):4112
Liu Y, Sun Q, Sharma A, Sharma A, Dhiman G (2021) Line monitoring and identification based on roadmap towards edge computing. Wirel Pers Commun. https://doi.org/10.1007/s11277-021-08272-y
Magalhaes FAD, Vannozzi G, Gatta G, Fantozzi S (2015) Wearable inertial sensors in swimming motion analysis: a systematic review. J Sports Sci 33(7):732–745
Mertens JC, Boschmann A, Schmidt M, Plessl C (2018) Sprint diagnostic with GPS and inertial sensor fusion. Sports Eng 21(4):441–451
Muniz-Pardos B, Sutehall S, Gellaerts J, Falbriard M, Mariani B, Bosch A et al (2018) Integration of wearable sensors into the evaluation of running economy and foot mechanics in elite runners. Curr Sports Med Rep 17(12):480–488
Nakamura M, Sasaki M, Yamada W, Kita N, Onizawa T, Takatori Y et al (2019) Path loss model in crowded outdoor environments considering multiple human body shadowing of multipath at 4.7 Ghz and 26.4 Ghz. IEICE Trans Commun 102(8):1676–1688
Ratta P, Kaur A, Sharma S, Shabaz M, Dhiman G (2021) Application of blockchain and internet of things in healthcare and medical sector: applications, challenges, and future perspectives. J Food Qual 2021:1–20. https://doi.org/10.1155/2021/7608296
Ren X, Li C, Ma X, Chen F, Wang H, Sharma A et al (2021) Design of multi-information fusion based intelligent electrical fire detection system for green buildings. Sustainability 13(6):3405
Sant A, Garg L, Xuereb P, Chakraborty C (2021) A novel green IoT-based pay-as-you-go smart parking system. CMC Comput Mater Cont 67(3):3523–3544
Sharma A, Kumar N (2021) Third eye: an intelligent and secure route planning scheme for critical services provisions in internet of vehicles environment. IEEE Syst J. https://doi.org/10.1109/JSYST.2021.3052072
Shi F, Zhao M, Anzai T, Ito K, Inaba M (2019) Multi-rigid-body dynamics and online model predictive control for transformable multi-links aerial robot*. Adv Robot 33(19):1–14
Shiang TY, Hsieh TY, Lee YS, Wu CC, Yu MC, Mei CH, Tai IH (2016) Determine the foot strike pattern using inertial sensors. J Sens. https://doi.org/10.1155/2016/4759626
Taborri J, Palermo E, Rossi S (2019) Automatic detection of faults in race walking: a comparative analysis of machine-learning algorithms fed with inertial sensor data. Sensors 19(6):1461
Thakur T, Batra I, Luthra M, Vimal S, Dhiman G, Malik A, Shabaz M (2021) Gene expression-assisted cancer prediction techniques. J Healthc Eng 2021:1–9. https://doi.org/10.1155/2021/4242646
Wang Y, Chen M, Wang X, Chan RH, Li WJ (2018) IoT for next-generation racket sports training. IEEE Internet Things J 5(6):4558–4566
Wang J, Xia C, Sharma A, Gaba GS, Shabaz M (2021) Chest CT findings and differential diagnosis of Mycoplasma pneumoniae pneumonia and Mycoplasma pneumoniae combined with Streptococcal Pneumonia in children. J Healthc Eng 2021:1–10. https://doi.org/10.1155/2021/8085530
Yang D, Tang J, Huang Y, Xu C, Li J, Hu L et al (2017) TennisMaster: an IMU-based online serve performance evaluation system. In: Proceedings of the 8th augmented human international conference, pp 1–8
Yuvaraj N, Srihari K, Dhiman G, Somasundaram K, Sharma A, Rajeskannan S et al (2021) Nature-inspired-based approach for automated cyberbullying classification on multimedia social networking. Math Probl Eng. https://doi.org/10.1155/2021/6644652
Zatsiorsky VM, Fortney VL (1993) Sport biomechanics 2000. J Sports Sci 11(4):279–283
Zeng H, Wang Q, Liu J (2019) Multi-feature fusion based on multi-view feature and 3D shape feature for non-rigid 3D model retrieval. IEEE Access 7:41584–41595
Zrenner M, Gradl S, Jensen U, Ullrich M, Eskofier BM (2018) Comparison of different algorithms for calculating velocity and stride length in running using inertial measurement units. Sensors 18(12):4194
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The research is supported by postdoc fellowship granted by the Institute of Computer Technologies and Information Security, Southern Federal University, project No PD/20-03-KT.
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Huang, X., Sharma, A. & Shabaz, M. Biomechanical research for running motion based on dynamic analysis of human multi-rigid body model. Int J Syst Assur Eng Manag 13 (Suppl 1), 615–624 (2022). https://doi.org/10.1007/s13198-021-01563-4
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DOI: https://doi.org/10.1007/s13198-021-01563-4