Skip to main content
Springer Nature Link
Log in

Trajectory planning in college football training using deep learning and the internet of things

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The research is conducted to improve the efficiency of college football training. The long short-term memory (LSTM) + genetic algorithm (GA) is proposed based on deep learning and internet of things (IoT) intelligent wearable devices. Specifically, the LSTM makes up for the weak local searchability of GA. The research idea is to use LSTM–GA to train and simulate the network model. The experimental result reads: the LSTM–GA model converges at the 101st iteration. The model fitness is about 11% higher than GA and about 2% higher than the LSTM. Thus, the proposed LSTM–GA can plan football players' trajectory to score. Therefore, players can wear IoT-ready intelligent wearable devices to plan the optimal shooting trajectory. The research contribution is to optimize the LSTM–GA model of IoT-ready intelligent wearable devices. The proposal can improve the efficiency of college football training.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

References

  1. Hui L, Hdjab C, Bo Y et al (2019) An analysis of research hotspots and modeling techniques on carbon capture and storage. Sci Total Environ 687:687–701. https://doi.org/10.1016/j.scitotenv.2019.06.013

    Article  Google Scholar 

  2. Taghavifar H, Xu B, Taghavifar L et al (2019) Optimal path-planning of nonholonomic terrain robots for dynamic obstacle avoidance using single-time velocity estimator and reinforcement learning approach. IEEE Access 7:1–1. https://doi.org/10.1109/ACCESS.2019.2950166

    Article  Google Scholar 

  3. Santos L, Santos F, Mendes J et al (2020) Path planning aware of robot’s center of mass for steep slope vineyards. Robotica 38(4):684–698. https://doi.org/10.1017/S0263574719000961

    Article  Google Scholar 

  4. Hao L, Ma G, Dong J (2020) Path planning method of anti-collision for the operation road of port cargo handling robot. J Coastal Res 103(sp1):892. https://doi.org/10.2112/SI103-185.1

    Article  Google Scholar 

  5. Hernández-Mejía C, Vázquez-Leal H, Torres-Muoz D (2020) A novel collision-free path planning modeling and simulation methodology for robotical arms using resistive grids. Robotica 38(7):1176–1190. https://doi.org/10.1017/S0263574719001310

    Article  Google Scholar 

  6. Cheng KP, Mohan RE, Nhan N et al (2020) Multi-objective genetic algorithm-based autonomous path planning for hinged-tetro reconfigurable tiling robot. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3006579

    Article  Google Scholar 

  7. Kumar PB, Rawat H, Parhi DR (2019) Path planning of humanoids based on artificial potential field method in unknown environments. Expert Syst 36(2):1–12. https://doi.org/10.1111/exsy.12360

    Article  Google Scholar 

  8. Chen J, Wang P, Wang H (2021) Pedestrian-induced load identification from structural responses using genetic algorithm with numerical and experimental validation. J Bridg Eng 26(3):04021001. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001687

    Article  Google Scholar 

  9. Nowakowski P, Szwarc K, Boryczka U (2018) Vehicle route planning in e-waste mobile collection on demand supported by artificial intelligence algorithms. Transp Res Part D Transp Environ 63:1–22. https://doi.org/10.1016/j.trd.2018.04.007

    Article  Google Scholar 

  10. Manoharan S (2019) An improved safety algorithm for artificial intelligence enabled processors in self driving cars. J Artif Intell 1(02):95–104

    Google Scholar 

  11. Ajeil FH, Ibraheem IK, Azar AT et al (2020) Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments. Sensors 20(7):1880. https://doi.org/10.3390/s20071880

    Article  Google Scholar 

  12. Jacob IJ, Darney PE (2021) Artificial bee colony optimization algorithm for enhancing routing in wireless networks. J Artif Intell 3(01):62–71. https://doi.org/10.36548/jaicn.2021.1.006

    Article  Google Scholar 

  13. AlJanabi S, Alkaim A (2022) A novel optimization algorithm (Lion-AYAD) to find optimal DNA protein synthesis. Egypt Inform J. https://doi.org/10.1016/j.eij.2022.01.004

    Article  Google Scholar 

  14. Hao K, Zhao J, Yu K et al (2020) Path planning of mobile robots based on a multi-population migration genetic algorithm. Sensors 20(20):5873. https://doi.org/10.3390/s20205873

    Article  Google Scholar 

  15. Yurek S, Eaton MJ, Lavaud R et al (2021) Modeling structural mechanics of oyster reef self-organization including environmental constraints and community interactions. Ecol Model 440:109389. https://doi.org/10.1016/j.ecolmodel.2020.109389

    Article  Google Scholar 

  16. Votion J, Cao Y (2019) Diversity-based cooperative multivehicle path planning for risk management in costmap environments. IEEE Trans Ind Electron 66(8):6117–6127. https://doi.org/10.1109/TIE.2018.2874587

    Article  Google Scholar 

  17. Xu C, Shen J, Du X et al (2018) An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access 6:48697–48707. https://doi.org/10.1109/ACCESS.2018.2867564

    Article  Google Scholar 

  18. Jonker D, Langevin S (2017) System and method for large scale information processing using data visualization for multi-scale communities. Google Patents, pp 2859–2864

  19. Nguyen Mau T, Inoguchi Y (2020) Locality-sensitive hashing for information retrieval system on multiple GPGPU devices. Appl Sci 10(7):2539. https://doi.org/10.3390/app10072539

    Article  Google Scholar 

  20. Saritha RR, Paul V, Kumar PG (2019) Content based image retrieval using deep learning process. Clust Comput 22(2):4187–4200. https://doi.org/10.1007/s10586-018-1731-0

    Article  Google Scholar 

  21. Sawyer MW, Buchmann C, Kalbaugh CA (2015) Examining the impact of helmet orientation visual cues on the passing effectiveness of quarterbacks in american football. Procedia Manuf 3:1181–1186. https://doi.org/10.1016/j.promfg.2015.07.196

    Article  Google Scholar 

  22. Tu X, Xie W, Chen Z et al (2021) Analysis of deep neural network models for inverse design of silicon photonic grating coupler. J Lightw Technol. https://doi.org/10.1109/JLT.2021.3057473

    Article  Google Scholar 

  23. Morgan M, Braasch J (2020) Holistic, long-term soundscape monitoring in Upstate New York using convolutional long short-term memory deep neural networks. J Acoust Soc Am 148(4):2740–2740. https://doi.org/10.1121/1.5147610

    Article  Google Scholar 

  24. Radha M, Fonseca P, Moreau A et al (2019) Sleep stage classification from heart-rate variability using long short-term memory neural networks. Sci Rep 9(1):14149. https://doi.org/10.1038/s41598-019-49703-y

    Article  Google Scholar 

  25. Jiang T, Zhan C, Yang Y (2019) Long short-term memory network with external memories for image caption generation. J Electron Imaging 28(2):1. https://doi.org/10.1117/1.JEI.28.2.023022

    Article  Google Scholar 

  26. Yu H, Xu Z, Zhou G et al (2020) Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem. Biogeosciences 17(3):781–792. https://doi.org/10.5194/bg-17-781-2020

    Article  Google Scholar 

  27. Tan Y, Zhao G (2019) Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries. IEEE Tran Ind Electron 67(10):8723–8731. https://doi.org/10.1109/TIE.2019.2946551

    Article  Google Scholar 

  28. Sepas-Moghaddam A, Etemad A, Pereira F et al (2021) Long short-term memory with gate and state level fusion for light field-based face recognition. IEEE Trans Inf Forensics Secur 16:1365–1379. https://doi.org/10.1109/TIFS.2020.3036242

    Article  Google Scholar 

  29. Du G, Wang Z, Gao B et al (2020) A convolution bidirectional long short-term memory neural network for driver emotion recognition. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.3007357

    Article  Google Scholar 

  30. Cao Y, Fan X, Guo Y et al (2020) Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods. J Polym Eng 40(4):360–371. https://doi.org/10.1515/polyeng-2019-0326

    Article  Google Scholar 

  31. Mendoza VN, Ledeneva Y, García-Hernández RA (2020) Unsupervised extractive multi-document text summarization using a genetic algorithm. J Intell Fuzzy Syst 39(2):1–12. https://doi.org/10.3233/JIFS-179900

    Article  Google Scholar 

  32. Deng B (2020) Word order detection in English classroom teaching based on improved genetic algorithm of block coding. J Intell Fuzzy Syst 40(6):1–12. https://doi.org/10.3233/JIFS-189521

    Article  Google Scholar 

  33. Zhi BE, Shi R, Gan L et al (2021) Multi-satellites imaging scheduling using individual reconfiguration based integer coding genetic algorithm. Acta Astronaut 178:645–657. https://doi.org/10.1016/j.actaastro.2020.08.041

    Article  Google Scholar 

  34. Khan AH, Li S, Luo X (2020) Obstacle avoidance and tracking control of redundant robotic manipulator: an RNN-based metaheuristic approach. IEEE Trans Ind Inf 16(7):4670–4680. https://doi.org/10.1109/TII.2019.2941916

    Article  Google Scholar 

  35. Li Y, Zheng J (2020) Research on real-time obstacle avoidance planning for an unmanned surface vessel based on the grid cell mechanism. J Navig 73(6):1358–1371. https://doi.org/10.1017/S0373463320000338

    Article  Google Scholar 

  36. Acuna Y, Sun Y (2020) An efficiency-improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem. Geophys Prospect 68(4):1189–1210. https://doi.org/10.1111/1365-2478.12920

    Article  Google Scholar 

  37. Jung S, Pyeon JH, Lee HS et al (2020) Construction cost estimation using a case-based reasoning hybrid genetic algorithm based on local search method. Sustainability 12(19):7920. https://doi.org/10.3390/su12197920

    Article  Google Scholar 

  38. Yasojima EK, Celio L, Teixeira ON et al (2019) CAM-ADX: a new genetic algorithm with increased intensification and diversification for design optimization problems with real variables. Robotica 37(9):1–46. https://doi.org/10.1017/S026357471900016X

    Article  Google Scholar 

  39. Wang Y, Zhang N, Chen X et al (2021) A short-term residential load forecasting model based on LSTM recurrent neural network considering weather features. Energies 14(10):2737. https://doi.org/10.3390/en14102737

    Article  Google Scholar 

  40. Saleh K, Hossny M, Nahavandi S (2017) Intent prediction of vulnerable road users from motion trajectories using stacked LSTM network. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC). IEEE, 2017, pp 327–332. https://doi.org/10.1109/ITSC.2017.8317941

  41. Panchanathan S, Chakraborty S, McDaniel T et al (2017) Enriching the fan experience in a smart stadium using internet of things technologies. Int J Semant Comput 11(02):137–170. https://doi.org/10.1142/S1793351X17400062

    Article  Google Scholar 

  42. Al Janabi S, Yaqoob A, Mohammad M (2019) Pragmatic method based on intelligent big data analytics to prediction air pollution. In: International conference on big data and networks technologies. Springer, Cham, pp 84–109. https://doi.org/10.1007/978-3-030-23672-4_8

  43. Al-Janabi S, Alkaim A, Al-Janabi E et al (2021) Intelligent forecaster of concentrations (PM2.5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput Appl 33(21):14199–14229. https://doi.org/10.1007/s00521-021-06067-7

    Article  Google Scholar 

  44. AlJawadi R, Studniarski M, Younus A (2018) New genetic algorithm based on dissimilarities and similarities. Comput Sci. https://doi.org/10.7494/csci.2018.19.1.2522

    Article  Google Scholar 

  45. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734. https://doi.org/10.1007/s00500-018-3102-4

    Article  Google Scholar 

  46. Faramarzi A, Heidarinejad M, Stephens B et al (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190. https://doi.org/10.1016/j.knosys.2019.105190

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Physical Education College, Shangrao Normal University, Shangrao, 334001, China

    Yingrong Guan, Yaoyu Qiu & Cheng Tian

Authors
  1. Yingrong Guan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yaoyu Qiu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Cheng Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yingrong Guan.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guan, Y., Qiu, Y. & Tian, C. Trajectory planning in college football training using deep learning and the internet of things. J Supercomput 78, 18616–18635 (2022). https://doi.org/10.1007/s11227-022-04619-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04619-9

Keywords