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2020-05-06
A Novel Approach for Human Intention Recognition Based on Hall Effect Sensors and Permanent Magnets
By
Van Tai Nguyen,

    Dr. Van Tai Nguyen

    School of Mechanical Engineering

    University of Adelaide

    Australia

    Homepage

Tien-Fu Lu,

    Dr. Tien-Fu Lu

    School of Mechanical Engineering

    University of Adelaide

    Australia

    Homepage

Paul Grimshaw,

    Dr. Paul Grimshaw

    School of Mechanical Engineering

    University of Adelaide

    Australia

    Homepage

William Robertson

    Dr. William Robertson

    School of Mechanical Engineering

    The University of Adelaide

    Australia

    Homepage

Progress In Electromagnetics Research M, Vol. 92, 55-65, 2020
doi:10.2528/PIERM19112801 | Google Scholar

Abstract
Human intention recognition is important for any interaction between the user and the exoskeleton. This study proposes a novel approach, based on a contactless sensory system, using linear Hall effect sensors to recognize human intentions. This contactless sensory system consists of four Hall effect sensors mounted on the exoskeleton, whilst a ring-shaped permanent magnet with diametrical magnetization consisting of two semi-rings is worn on the user's forearm. The model of the magnetic field created by the permanent magnet is also developed. Based on the developed magnetic field model and by interpreting the signals from the Hall effect sensory system received while the user's elbow and forearm move, the intention identification algorithm is derived. A lightweight elbow and forearm assistive exoskeleton is developed. The proposed approach for human intention recognition is used to assist in controlling the exoskeleton, following the wearer's intended motions. By implementing this contactless sensory system, wearers can use the exoskeleton easily and can move their forearm comfortably, while the human intention motion is recognized and used to control the exoskeleton. Moreover, achieved signals are unaffected by skin perspiration and muscle fatigue. As the sensory system is mounted on the exoskeleton, there is only indirect contact between the user's body and the sensors, leading to improved comfort. Finally, the system does not require expert knowledge to place the sensors on the body of the user. This approach can be extended to detect human intentions for the control of exoskeletons with more degrees of freedom.
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Citation
Van Tai Nguyen, Tien-Fu Lu, Paul Grimshaw, and William Robertson, "A Novel Approach for Human Intention Recognition Based on Hall Effect Sensors and Permanent Magnets," Progress In Electromagnetics Research M, Vol. 92, 55-65, 2020.
doi:10.2528/PIERM19112801
References

1. Rahman, M. H., C. O. Luna, M. Saad, and P. Archambault, "EMG based control of a robotic exoskeleton for shoulder and elbow motion assist," Jounal of Automation and Control Engineering, Vol. 3, No. 4, 270-276, 2015.        Google Scholar

2. Kiguchi, K. and Y. Hayashi, "An EMG-based control for an upper-limb power-assist exoskeleton robot," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 42, No. 4, 1064-1071, Aug. 2012.        Google Scholar

3. Andreasen, D. S., S. K. Alien, and D. A. Backus, "Exoskeleton with EMG based active assistance for rehabilitation," Proceedings of the 9th International Conference on Rehabilitation Robotics, 2005, ICORR 2005, 333-336, 2005.        Google Scholar

4. Lenzi, T., S. M. M. De Rossi, N. Vitiello, and M. C. Carrozza, "Intention-based EMG control for powered exoskeletons," IEEE Transactions on Biomedical Engineering, Vol. 59, No. 8, 2180-2190, Aug. 2012.        Google Scholar

5. Rahman, M. H., C. O. Luna, M. Saad, and P. Archambault, "Motion control of an exoskeleton robot using electromyogram signals," Proceedings of Advances in Robotics, Mechatronics and Circuits: 18th International Conference on Circuits (Part of CSCC'14), 2014 International Conference on Mechatronics and Robotics, Structural Analysis (MEROSTA 2014), ISBN: 978-1-61804-242-2, 2014.        Google Scholar

6. Kiguchi, K., T. Tanaka, and T. Fukuda, "Neuro-fuzzy control of a robotic exoskeleton with EMG signals," IEEE Transactions on Fuzzy Systems, Vol. 12, No. 4, 481-490, Aug. 2004.        Google Scholar

7. Tang, Z., K. Zhang, S. Sun, Z. Gao, L. Zhang, and Z. Yang, "An upper-limb power-assist exoskeleton using proportional myoelectric control," Sensors, Vol. 14, 6677-6694, 2014.        Google Scholar

8. Irimia, D. C., M. S. Poboroniuc, F. Serea, A. Baciu, and R. Olaru, "Controlling a FESEXOSKELETON rehabilitation system by means of brain-computer interface," Proceedings of International Conference and Exposition on Electrical and Power Engineering (EPE), 352-355, Iasi, 2016.        Google Scholar

9. Lalitharatne, T. D., K. Teramoto, Y. Hayashi, K. Tamura, and K. Kiguchi, "EEG-based evaluation for perception-assist in upper-limb power-assist exoskeletons," Proceedings of 2014 World Automation Congress (WAC), 307-312, Waikoloa, HI, 2014.        Google Scholar

10. Kilicarslan, A., S. Prasad, R. G. Grossman, and J. L. Contreras-Vidal, "High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton," Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5606-5609, Osaka, 2014.        Google Scholar

11. Lee, K., D. Liu, L. Perroud, R. Chavarriaga, and J. del R. Millán, "A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers," Robotics and Autonomous Systems, Vol. 90, 15-23, 2017.        Google Scholar

12. Li, Z., W. He, C. Yang, S. Qiu, L. Zhang, and C. Y. Su, "Teleoperation control of an exoskeleton robot using brain machine interface and visual compressive sensing," Proceedings of 2016 12th World Congress on Intelligent Control and Automation (WCICA), 1550-1555, Guilin, 2016.        Google Scholar

13. Vidaurre, C., C. Klauer, T. Schauer, A. R. Murguialday, and K.-R. Müller, "EEG-based BCI for the linear control of an upper-limb neuroprosthesis," Medical Engineering & Physics, Vol. 38, 1195-1204, 2016.        Google Scholar

14. Schultz, A. E. and T. A. Kuiken, "Neural interfaces for control of upper limb prostheses: The state of the art and future possibilities," PM&R, Vol. 3, No. 1, 55-67, 2011.        Google Scholar

15. Noda, T., N. Sugimoto, J. Furukawa, M. A. Sato, S. H. Hyon, and J. Morimoto, "Brain-controlled exoskeleton robot for BMI rehabilitation," Proceedings of the 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), 21-27, Osaka, 2012.        Google Scholar

16. Ammar, L. I., B. Y. Kaddouh, M. K. Mohanna, and I. H. Elhajj, "SAS: SMA aiding sleeve," Proceedings of the 2010 IEEE International Conference on Robotics and Biomimetics, 1596-1599, Tianjin, 2010.        Google Scholar

17. Ul Islam, M. R. and S. Bai, "Intention detection for dexterous human arm motion with FSR sensor bands," Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, 139-140, Vienna, Austria, 2017.        Google Scholar

18. Xiao, Z. G., A. M. Elnady, and C. Menon, "Control an exoskeleton for forearm rotation using FMG," Proceedings of the 5th IEEERAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 591-596, Sao Paulo, 2014.        Google Scholar

19. Cho, E., R. Chen, L.-K. Merhi, Z. Xiao, B. Pousett, and C. Menon, "Force myography to control robotic upper extremity prostheses: A feasibility study," Frontiers in Bioengineering and Biotechnology, Vol. 4, 18, 2016.        Google Scholar

20. Huang, J., W. Huo, W. Xu, S. Mohammed, and Y. Amirat, "Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition," IEEE Transactions on Automation Science and Engineering, Vol. 12, No. 4, 1257-1270, 2015.        Google Scholar

21. Nguyen, V. T., T.-F. Lu, and P. Grimshaw, "Human intention recognition based on contactless sensors to control an elbow and forearm assistive exoskeleton," Proceedings of the 7th International Conference of Asian Society for Precision Engineering and Nanotechnology (ASPEN 2017), ARM-P-06, Seoul, Nov. 2017.        Google Scholar

22. Fontana, M., F. Salsedo, and M. Bergamasco, "Novel magnetic sensing approach with improved linearity," Sensors, 2013.        Google Scholar

23. Wang, S., J. Jin, T. Li, and G. Liu, "High-accuracy magnetic rotary encoder," System Simulation and Scientific Computing, 74-82, Springer Berlin Heidelberg, Berlin, 2012.        Google Scholar

24. Lee, Y. Y., R. H. Wu, and S. T. Xu, "Applications of linear Hall-effect sensors on angular measurement," Proceedings of the 2011 IEEE International Conference on Control Applications (CCA), 479-482, Denver, CO, 2011.        Google Scholar

25. Smirnov, Y., T. Kozina, E. Yurasova, and A. Sokolov, "Analog-to-digital converters of the components of a displacement with the use of microelectronic sine-cosine magnetic encoders," Measurement Techniques, Vol. 57, No. 1, 41-46, 2014.        Google Scholar

26. Hu, J., J. Zou, F. Xu, Y. Li, and Y. Fu, "An improved PMSM rotor position sensor based on linear hall sensors," IEEE Transactions on Magnetics, Vol. 48, No. 11, 3591-3594, 2012.        Google Scholar

27. Ramsden, E., Hall-effect Sensors. Theory and Applications, 2nd Ed., 272 pages, Newness, Elsevier, 2006.

28. Nguyen, V. T. and T.-F. Lu, "Modelling of magnetic field distributions of elliptical cylinder permanent magnets with diametrical magnetization," Journal of Magnetism and Magnetic Materials, Vol. 491, 2019.        Google Scholar

29. Nguyen, V. T. and T.-F. Lu, "Analytical expression of the magnetic field created by a permanent magnet with diametrical magnetization," Progress In Electromagnetics Research C, Vol. 87, 163-174, 2018.        Google Scholar

30. Nguyen, V. T., "Modelling of magnetic fields of permanent magnets with diametrical magnetization,", MPhil Thesis, 2019.        Google Scholar

31. Jezný, J. and M. Čurilla, "Position measurement with Hall effect sensors," American Journal of Mechanical Engineering, Vol. 1, No. 7, 231-235, 2013.        Google Scholar

32. Romilly, D. P., C. Anglin, R. G. Gosine, C. Hershler, and S. U. Raschke, "A functional task analysis and motion simulation for the development of a powered upper-limb orthosis," IEEE Transactions on Rehabilitation Engineering, Vol. 2, No. 3, 119-129, 1994.        Google Scholar

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