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A fuzzy approach towards inductive transfer and human–machine interface control design

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Abstract

Traditional machines do not adapt to their operators, instead they implicitly demand human adaptation. Human adaptive mechatronics (HAM) is the research topic that covers the design of devices and controllers for assisting the human. HAM devices are capable to measure and estimate the operator’s skill/dexterity, while a real-time assist-controller enhances machine adaptation, improving the overall human–machine performance. Nowadays, the demand for such devices has particular potential in many activities, which involve manual operations, such as in assistive technology. The main contribution of this work is the proposal of a fuzzy clustering methodology to the development of a real-time inductive transfer embedded controller, used for improving the operator’s proficiency, under a human-in-the-loop environment relying on visual feedback information. Other contribution is the proposal of a condition for inductive transfer between human operators, based on correlation analysis. The operator behaviour is modelled and enhanced from a human–machine interface fuzzy classifier and assisting scheme, which uses real-time data and additional information collected from an expert user. Experimental tests were performed by different participants under a driving simulator, for evaluation of the proposed methodology. The fuzzy clustering approach confirmed to significantly improve the transfer learning and the driving skills of the human operators.

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References

  • Abe M (2009) Vehicle handling dynamics. Elsevier, Amsterdam

    Google Scholar 

  • Andreu J, Baruah RD, Angelov P (2011) Real time recognition of human activities from wearable sensors by evolving classifiers. In: 2011 IEEE international conference on fuzzy systems (FUZZ), pp 2786–2793

  • Antunes R, Coito FV, Duarte-Ramos H (2013a) A multi-variable modeling approach for improving operator proficiency. Int J Math Models Methods Appl Sci 7(3):238–246

    Google Scholar 

  • Antunes R, Coito FV, Duarte-Ramos H (2013b) Skill evaluation in point-to-point human–machine operation. Appl Mech Mater 394:463–469

    Article  Google Scholar 

  • Antunes R, Brito Palma L, Coito F, Duarte-Ramos H (2015) Inductive transfer assist-control for human-interface steering device. In: 2015 IEEE international conference on evolving and adaptive intelligent systems (EAIS), pp 1–8

  • Antunes R, Brito Palma L, Coito F, Duarteramos H, Gil P (2016) Intelligent human–computer interface for improving pointing device usability and performance. In: 2016 12th IEEE international conference on control & automation (ICCA), pp 714–719

  • Åström KJ, Wittenmark B (1997) Computer-controlled systems: theory and design. Information and system sciences series. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Bazrafkan S, Kar A, Costache C (2015) Eye gaze for consumer electronics: controlling and commanding intelligent systems. IEEE Consum Electron Mag 4(4):65–71

    Article  Google Scholar 

  • Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Article  Google Scholar 

  • Brito Palma L, Vieira Coito F, Sousa Gil P (2012) Low order models for human controller—mouse interface. In: 2012 IEEE 16th international conference on intelligent engineering systems (INES), pp 515–520

  • Butz MV, Lönneker TD (2009) Optimized sensory-motor couplings plus strategy extensions for the TORCS car racing challenge. In: 2009 IEEE symposium on computational intelligence and games, pp 317–324

  • Cardamone L, Loiacono D, Lanzi PL (2009) Learning drivers for TORCS through imitation using supervised methods. In: 2009 IEEE symposium on computational intelligence and games, pp 148–155

  • Carlson T, Millán J (2013) Brain-controlled wheelchairs: a robotic architecture. IEEE Robot Autom Mag 20(1):65–73

    Article  Google Scholar 

  • Celik O, Ertugrul S (2010) Predictive human operator model to be utilized as a controller using linear, neuro-fuzzy and fuzzy-ARX modeling techniques. Eng Appl Artif Intell 23(4):595–603

    Article  Google Scholar 

  • Cichosz P, Pawełczak Ł (2014) Imitation learning of car driving skills with decision trees and random forests. Int J Appl Math Comput 24(3):579–597

    MATH  Google Scholar 

  • Dunn JC (1974) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Cybern Syst 3(3):32–57

    MathSciNet  MATH  Google Scholar 

  • Evans M, Noble J, Hochenbaum J (2013) Arduino in action. Manning, New York

    Google Scholar 

  • Escobar PF, Falcone T (eds) (2014) Atlas of single-port, laparoscopic, and robotic surgery. Springer, Berlin

    Google Scholar 

  • Gaines B (1969) Linear and nonlinear models of the human controller. Int J Man Mach Stud 1(4):333–360

    Article  MATH  Google Scholar 

  • Gusikhin O, Rychtyckyj N, Filev D (2007) Intelligent systems in the automotive industry: applications and trends. Knowl Inf Syst 12(2):147–168

    Article  Google Scholar 

  • Harashima F, Suzuki S (2008) Future of mechatronics and human. SICE J Control Meas Syst Integr 1(1):18–25

    Article  Google Scholar 

  • Jiang L, Zhang J, Allen G (2010) Transferred correlation learning: an incremental scheme for neural network ensembles. In: The 2010 international joint conference on neural networks (IJCNN), pp 1–8

  • Jirgl M, Havlikova M, Bradac Z (2015) The dynamic pilot behavioral models. Procedia Eng 100:1192–1197

    Article  Google Scholar 

  • Li T, Chang SJ, Chen YX (2003) Implementation of human-like driving skills by autonomous fuzzy behavior control on an fpga-based car-like mobile robot. IEEE Trans Ind Electron 50(5):867–880

    Article  Google Scholar 

  • Li W, Sadigh D, Sastry S, Seshia SA (2014) Synthesis for human-in-the-loop control systems. Tools and algorithms for the construction and analysis of systems, vol 8413. Lect Notes Comput Sci. Springer, Berlin, pp 470–484

  • Molloy D (2014) Exploring BeagleBone: tools and techniques for building with embedded Linux. Wiley, New York

    Book  Google Scholar 

  • Morales D, La Hera P, Westerberg S, Freidovich L, Shiriaev A (2015) Path-constrained motion analysis: an algorithm to understand human performance on hydraulic manipulators. IEEE Trans Hum Mach Syst 45(2):187–199

    Article  Google Scholar 

  • Muñoz J, Gutierrez G, Sanchis A (2010) A human-like TORCS controller for the Simulated Car Racing Championship. In: 2010 IEEE conference on computational intelligence and games, pp 473–480

  • Oishi M, Mitchell I, Van der Loos H (2010) Design and use of assistive technology: social, technical, ethical, and economic challenges. Springer, Berlin

    Book  Google Scholar 

  • Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  • Razali N, Shamsudin N, Azid N, Hadi A, Ismail A (2012) A comparison of normality tests using spss, sas and minitab: an application to health related quality of life data. In: 2012 International conference on statistics in science, business, and engineering (ICSSBE), pp 1–6

  • Suzuki S (2010) Human adaptive mechatronics. IEEE Ind Electron Mag 4(2):28–35

    Article  Google Scholar 

  • Suzuki S, Harashima F (2012) Estimation algorithm of machine operational intention by Bayes filtering with self-organizing map. Adv Hum Comput Interact 2012:1–20

    Article  Google Scholar 

  • Suzuki S, Igarashi H, Kobayashi H, Yasuda T, Harashima F (2013) Human adaptive mechatronics and human-system modelling. Int J Adv Robot Syst 10:1–14

    Article  Google Scholar 

  • Tervo K, Koivo H (2010) Towards human skill adaptive manual control. Int J Adv Mechatron Syst 2(1/2):46–58

    Article  Google Scholar 

  • Tervo K, Bocca M, Eriksson L, Manninen A (2010) Wireless manual control for human adaptive mechatronics. Int J Adv Mechatron Syst 2(4):254–270

    Article  Google Scholar 

  • Wu Y, Li W, Minoh M, Mukunoki M (2013) Can feature-based inductive transfer learning help person re-identification? In: 2013 IEEE international conference on image processing, pp 2812–2816

  • Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Yang HC, Sababha B, Acar C, Rawashdeh O (2010) Rapid prototyping of quadrotor controllers using MATLAB RTW and dsPICs. In: AIAA Infotech@Aerospace, Atlanta, USA, pp 1–6

  • Yang L, Hanneke S, Carbonell J (2013) A theory of transfer learning with applications to active learning. Mach Learn 90(2):161–189

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work has been supported by Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, by CTS-Uninova research unit, by Escola Superior de Tecnologia de Setúbal do Instituto Politécnico de Setúbal and by national funds through FCT-Fundação para a Ciência e a Tecnologia within the research unit CTS-Centro de Tecnologia e Sistemas (project UID/EEA/00066/2013). The authors would like to thank all the institutions and all the participants in the driving experiments.

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Correspondence to Rui Azevedo Antunes.

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Azevedo Antunes, R., Brito Palma, L., Vieira Coito, F. et al. A fuzzy approach towards inductive transfer and human–machine interface control design. Evolving Systems 9, 43–56 (2018). https://doi.org/10.1007/s12530-016-9172-6

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  • DOI: https://doi.org/10.1007/s12530-016-9172-6

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