A novel design and implementation of wheelchair navigation system using Leap Motion sensor

References

• Issa T, Isaias P. Sustainable design. London (UK): Springer; 2015.
   Google Scholar
• Abascal J. Human-computer interaction in assistive technology: from “Patchwork” to “Universal Design.” In 2002 IEEE international conference on systems, man and cybernetics, Tunisia. New York (NY): IEEE. vol. 3; 2002. p. 6.
   Google Scholar
• Škraba A, Koložvari A, Kofjač D, et al. Wheelchair maneuvering using leap motion controller and cloud based speech control: prototype realization. In 2015 4th Mediterranean Conference on Embedded Computing (MECO), Budva. New York (NY): IEEE; 2015. p. 391–394.
   Google Scholar
• Boyali A, Hashimoto N, Matsumato O. Hand posture control of a robotic wheelchair using a leap motion sensor and block sparse representation based classification. In SMART 2014, the Third International Conference on Smart Systems, Devices and Technologies, Paris. 2014. p. 20–25.
   Google Scholar
• Eid MA, Giakoumidis N, Saddik AE. A novel eye-gaze-controlled wheelchair system for navigating unknown environments: case study with a person with ALS. IEEE Access. 2016;4:558–573.
   Web of Science ®Google Scholar
• Veerati R, Suresh E, Chakilam A, et al. Eye monitoring based motion controlled wheelchair for quadriplegics. In Microelectronics, electromagnetics and telecommunications. Singapore: Springer; 2018. p. 41–49.
   Google Scholar
• Goswami S, Mujawar A, Dubey N, et al. Eye movement based wheelchair control system for physically challenged persons. Int J Res. 2016;3:125–128.
   Google Scholar
• Elliott MA, Malvar H, Maassel LL, et al. Eye-controlled, power wheelchair performs well for ALS patients. Muscle Nerve. 2019;60(5):513–519.
   PubMed Web of Science ®Google Scholar
• Zupan A. Sophisticated wheechairs. Rehabilitacija 2007, vol. 6 supl. I, Inštitut Republike Slovenije za rehabilitacijo, Linhartova 51. 1000 Ljubljana, pp. 15–18, 2007.
   Google Scholar
• Kadmin A, Jidin A, Bakar A, et al. Wireless voice-based wheelchair controller system. J Telecommun Electron Comput Eng. 2016;8:117–122.
   Google Scholar
• Joshi K, Ranjan R, Sravya E, et al. Design of voice-controlled smart wheelchair for physically challenged persons. In Emerging technologies in data mining and information security. Singapore: Springer; 2019. p. 79–91.
   Google Scholar
• Qassim HM, Eesee AK, Osman OT, et al. Controlling a motorized electric wheelchair based on face tilting. Bio-Algorithms Med-Syst. 2019;15(4).
   Web of Science ®Google Scholar
• Vázquez JE, Martin-Ortiz M, Olmos-Pineda I, et al. Wheelchair control based on facial gesture recognition. Int J Inform Technol Syst Approach. 2019;12:104–122.
   Web of Science ®Google Scholar
• Velasco-Álvarez F, Fernández-Rodríguez A, Ron-Angevin R. Switch mode to control a wheelchair through EEG signals. In Converging clinical and engineering research on neurorehabilitation II. Cham: Springer; 2017. p. 801–805.
   Google Scholar
• Utama J, Saputra M. Design of electric wheelchair controller based on brainwaves spectrum EEG sensor. IOP Conf Ser Mater Sci Eng. 2018;407:012080.
   Google Scholar
• Babri O, Malik S, Ibrahim T, et al. Voice controlled motorized wheelchair with real time obstacle avoidance. In Proceedings of the Third International Conference on Communications and Information Technology (ICCIT). New York (NY): IEEE; 2012.
   Google Scholar
• Felzer T, Freisleben B. HaWCoS: the hands-free wheelchair control system. In Proceedings of the fifth international ACM conference on Assistive technologies. New York (NY): ACM; 2002. p. 127–134.
   Google Scholar
• Barea R, Boquete L, Mazo M, et al. Wheelchair guidance strategies using EOG. J Intell Robotic Syst. 2002;34(3):279–299.
   Web of Science ®Google Scholar
• Craig DA, Nguyen H. Adaptive EEG thought pattern classifier for advanced wheelchair control. In 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. New York (NY): IEEE; 2007. p. 2544–2547.
   Google Scholar
• Tsui CSL, Jia P, Gan JQ, et al. EMG-based hands-free wheelchair control with EOG attention shift detection. In IEEE International Conference on Robotics and Biomimetics. ROBIO 2007. New York (NY): IEEE. 2007. p. 1266–1271.
   Google Scholar
• Gopura R, Bandara D, Gunasekara J, et al. Recent trends in EMG-based control methods for assistive robots. In Electrodiagnosis in new frontiers of clinical research. Rijeka: InTech; 2013.
   Google Scholar
• Oonishi Y, Oh S, Hori Y. New control method for power-assisted wheelchair based on upper extremity movement using surface myoelectric signal. In 10th IEEE International Workshop on Advanced Motion Control. AMC’08. New York (NY): IEEE; 2008. p. 498–503.
   Google Scholar
• Barea R, Boquete L, Mazo M, et al. EOG Technique to guide a wheelchair. In Proceedings of the 16th IMACS World Congress, Lausanne. 2000.
   Google Scholar
• Iturrate I, Antelis J, Minguez J. Synchronous EEG brain-actuated wheelchair with automated navigation. In IEEE International Conference on Robotics and Automation. New York (NY): IEEE; 2009. p. 2318–2325.
   Google Scholar
• Al-Qaysi Z, Zaidan B, Zaidan A, et al. A review of disability EEG based wheelchair control system: coherent taxonomy, open challenges and recommendations. Comput Methods Programs Biomed. 2018;164:221–237.
   PubMed Web of Science ®Google Scholar
• Clark GD, Lindqvist J. Engineering gesture-based authentication systems. IEEE Pervasive Comput. 2015;14(1):18–25.
   Web of Science ®Google Scholar
• Sagayam KM, Hemanth DJ. Hand posture and gesture recognition techniques for virtual reality applications: a survey. Virtual Reality. 2017;21(2):91–107.
   Web of Science ®Google Scholar
• Azad R, Azad B, Khalifa NB, et al. Real-time human-computer interaction based on face and hand gesture recognition. arXiv Preprint arXiv. 1408.1549; 2014.
   Google Scholar
• Developer.LeapMotion. 2017. API reference. Available from: https://developer.leapmotion.com/documentation/csharp/api/Leap_Classes.html
   Google Scholar
• Mantecón T, del-Blanco CR, Jaureguizar F, et al. Hand gesture recognition using infrared imagery provided by leap motion controller. International Conference on Advanced Concepts for Intelligent Vision Systems. Cham: Springer; 2016. p. 47–57.
   Google Scholar
• Developer.arm. 2017. The Arm Cortex-M3 processor. Available from: https://developer.arm.com/products/processors/cortex-m/cortex-m3.
   Google Scholar
• Baker N. ZigBee and bluetooth: strengths and weaknesses for industrial applications. Comput Control Eng. 2005;16(2):20–25.
   Web of Science ®Google Scholar
• Craig A, Krishnan S. Fusion of leap motion and kinect sensors for improved field of view and accuracy for VR applications.
   Google Scholar
• Zhang Z. Microsoft kinect sensor and its effect. IEEE Multimedia Mag. 2012;19(2):4–10.
   Web of Science ®Google Scholar
• labviewmakerhub. 2017. Leap motion controller. Stanford University Course Report. Available from: https://www.labviewmakerhub.com
   Google Scholar
• CrustCrawler Robotics. Dynamixel AX-12A manual. USA: Gilbert; 2011. http://www.crustcrawler.com
   Google Scholar
• trossenrobotics. 2017. Dynamixel AX-12A robot actuator. Available from: http://www.trossenrobotics.com/dynamixel-ax-12-robot-actuator.aspx
   Google Scholar