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뇌파 및 사물인터넷을 활용한 다방향 이동성 메카넘 휠 제어 설계

Design of Multi-Directional Mobility Mechanism Wheel Control Utilizing Brainwaves and IoT

  • 황재민 (서원대학교 컴퓨터공학과) ;
  • 김봉현 (서원대학교 컴퓨터공학과)
  • Jae-Min Hwang (Department of Computer Engineering, Seowon University) ;
  • Bong-Hyun Kim (Department of Computer Engineering, Seowon University)
  • 투고 : 2023.07.24
  • 심사 : 2023.09.15
  • 발행 : 2023.10.31

초록

현재 휠체어는 전동 휠체어와 수동 휠체어가 있지만, 전동 휠체어조차 자기 근육을 사용할 수 있는 사람이 사용할 수 있다. 자기 근육을 거의 사용하지 못하는 사람의 경우 누군가가 끌어주는 휠체어만을 사용해야 하지만, 누구나 자유를 꿈꾸고 원한다. 이에 따라, 자기 근육을 사용할 수 없는 사람도 다른 사람의 도움 없이 움직일 수 있는 휠체어가 필요하다. 뇌파 기반 다방향 이동성 메카넘 휠은 장치 사용자의 후두엽(O1, O2, P7, P8)에서 자극기와 동일한 주파수 성분을 SVM 모델을 이용하여 분석 후 장치를 제어한다. 장치에 카메라와 초음파 센서를 장착하여 물체와 거리를 감지해 정지하고, 추락의 위험이 있는 높이의 턱에서 정지할 수 있다. 또한, 카메라를 이용해 이동 장치 사용자의 전방 상황을 보호자에게 보여줄 수 있으며, GPS를 이용하여 사용자의 실시간 위치를 더 정확하게 확인할 수 있어 장치 사용자의 안전을 보장하고 보호자의 모니터링을 용이하게 하는 기술을 설계하였다. 이를 통해 장애인은 더 자유롭게 이동할 수 있고, 보호자는 장애인을 더 쉽게 모니터링을 할 수 있다. 근육을 사용하지 못하는 사람과 보호자에게 도움을 주어 삶의 질을 높이고자 한다.

Wheelchairs currently come in two types: electric and manual. However, even electric wheelchairs require some degree of muscle control for operation. Individuals with limited muscle control often rely on someone else to push their wheelchair, depriving them of the independence that everyone desires. Consequently, there is a need for wheelchairs that individuals without muscle control can operate independently. The brainwave-based omnidirectional mobility Mecanum wheel analyzes frequency components similar to those of a stimulator in the occipital lobe (O1, O2, P7, P8) of the user, utilizing an SVM model for control. Equipped with cameras and ultrasonic sensors, the device can detect objects and distances, allowing it to halt and prevent falls at elevated thresholds. Moreover, the camera can provide the caregiver with a front view of the user's surroundings, while GPS enables more accurate real-time monitoring of the user's location, ensuring the user's safety and facilitating caregiver monitoring. This technology aims to enhance the quality of life for individuals with limited muscle control and their caregivers, enabling greater freedom of movement for the disabled and easier monitoring for caregivers.

키워드

참고문헌

  1. L.K.Lee and S.Y.Oh, "Development of Smart Wheelchair System and Navigation Technology For Stable Driving Performance In Indoor-Outdoor Environments," Journal of the Institute of Electronics and Information Engineers, Vol.52, No.7, pp.153-161, 2015. https://doi.org/10.5573/ieie.2015.52.7.153
  2. H.J.Lee, D.I.Shin and D.K.Shin, "The Classification Algorithm of Users' Emotion Brain-Wave," The Journal of Korean Institute of Communications and Information Sciences, Vol.39C, No.02, pp.1-3, 2014. https://doi.org/10.7840/kics.2014.39C.1.1
  3. L.Kanungo, N.Garg, A.Bhobe, S.Rajguru and V.Baths, "Wheelchair Automation by a Hybrid BCI System Using SSVEP and Eye Blinks," 2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), pp.1-4, 2021.
  4. M.K.Ahn, J.H.Hong, S.U.Kang, H.H.Cho and S.C.Jun, "Classification of motor imagery using EEG source information," Journal of KISS, Vol.37, No.2(c), 2010.
  5. C.J.Lee, "Development of the Game for Increasing Intensive Power using EEG Signal," Journal of Korea Game Society, Vol.9, No.2, pp.23-24, 2009.
  6. J.Y.Lee, Y.R.Lee and H.N.Kim, "Frequency Recognition in SSVEP-based BCI systems with a Combination of CCA and PSDA," Journal of The Institute of Electronics and Information Engineers, Vol.52, No.10, 2015. https://doi.org/10.5573/IEIE.2015.52.10.139
  7. K.B.Lee, C.H.Lee, J.H.Bae and J.I.Lee, "EEG Signal Classification Algorithm based on DWT and SVM for Driving Robot Control," Journal of The Institute of Electronics and Information Engineers, Vol.52, No.8, 2015. https://doi.org/10.5573/IEIE.2015.52.8.117
  8. B.S.Chu and S.Y.Whee, "Mobile Performance Evaluation of Mecanum Wheeled Omni-directional Mobile Robot," Journal of the Korean Society of Manufacturing Technology Engineers, Vol.23, No.4, pp.374-379, 2014. https://doi.org/10.7735/ksmte.2014.23.4.374
  9. K.Y.Ryu, S.J.Kim, H.S.Jung and H.S.Kweon, "Application of band-pass filtering techniques for improvement on 3D tomogram," Proceeding of the Korea Information Processing Society Conference, pp.382-383, 2011.
  10. J.N.Kim, "Design of Electric Automatic Manual Wheelchair Driving System," Journal of the Korea Academia-Industrial cooperation Society, Vol.14, no.11, pp.5392-5395, 2013. https://doi.org/10.5762/KAIS.2013.14.11.5392
  11. J.Lee, "Autonomous Navigation System of Power Wheelchair using Distance Measurement Sensors," The Journal of Korea Institute of Information, Electronicsm and Communication, Vol.6, No.3, pp.174-182, 2013.
  12. S.W.Rhee, H.J.Cho and C.J.Chae, "EEG Signal Classification based on SVM Algorithm," Journal of the Korea Convergence Society, Vol.11, No.2, pp.17-22, 2020. https://doi.org/10.15207/JKCS.2020.11.2.017
  13. H.H.Kim, "Implementation of Brain-machine Interface System using Cloud IoT," Journal of Internet of Things and Convergence, Vol.9, No.1, pp.25-31, 2023. https://doi.org/10.20465/KIOTS.2023.9.1.025
  14. C.W.Choi and H.Y.Chung, "An Account Management System on IoT Device," Journal of Internet of Things and Convergence, Vol.7, No.1, pp.71-77, 2021.
  15. J.Y.Lee, Y.R.Lee and H.N.Kim, "Frequency Recognition in SSVEP-based BCI systems with a Combination of CCA and PSDA," Journal of the Institute of Electronics and Information Engineers, Vol.52, No.10, pp.139-147, 2015. https://doi.org/10.5573/ieie.2015.52.10.139
  16. J.E.Son, H.M.Lim and J.H.Ku, "Study of MNS and SSVEP activity according to Frequency and Duty rate of Flickering Action video," Journal of Biomedical Engineering Research, Vol.39, No.1, pp.16-21, 2018. https://doi.org/10.9718/JBER.2018.39.1.16