Omnidirectional Assistive Wheelchair

Smart electric wheelchairs are becoming a natural substitute of the conventional wheelchairs as an assitive device for geriatric population and patients suffering from mobility disorders. There is a demand for developing powered wheelchairs with intelligent control to suit wide range of application in the field of assistive technology. This paper deals with the development of a 4 wheeled omnidirectional wheelchair and its control using a myoelectric user intention interface. Developed system is driven by holonomic drive system, exploring greater maneuverability compared to conventional powered wheelchairs. Myoelectric signals from forearm muscles are processed to extract some features for seven different wheelchair motion namely forward, backward, left, right, clock-wise and anticlockwise turn and stop. A neural network classifier classifies the user intention and maps the intention to wheelchair motion. The developed system finds its direct application in transporting people with locomotor disability, geriatric population as well as an indoor navigation vehicle.

[1]  Robert F. Cromp,et al.  The design of an autonomous vehicle for the disabled , 1986, IEEE J. Robotics Autom..

[2]  Huosheng Hu,et al.  Head gesture recognition for hands-free control of an intelligent wheelchair , 2007, Ind. Robot.

[3]  David P. Miller,et al.  Design and testing of a low-cost robotic wheelchair prototype , 1995, Auton. Robots.

[4]  Oishee Mazumder,et al.  Close loop control of non-holonomic WMR with augmented reality and potential field , 2014, 2014 Recent Advances in Engineering and Computational Sciences (RAECS).

[5]  Desney S. Tan,et al.  Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces , 2008, CHI.

[6]  Yoshiaki Shirai,et al.  Robotic wheelchair based on observations of both user and environment , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[7]  Kedar Sukerkar,et al.  Smart Wheelchair: A Literature Review , 2018 .

[8]  Paolo Fiorini,et al.  A robotics wheelchair for crowded public environment , 2001, IEEE Robotics Autom. Mag..

[9]  Oishee Mazumder,et al.  Holonomic wheelchair control using EMG signal and joystick interface , 2014, 2014 Recent Advances in Engineering and Computational Sciences (RAECS).

[10]  S P Levine,et al.  The NavChair Assistive Wheelchair Navigation System. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  Toshio Tsuji,et al.  A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..

[12]  M. Khezri,et al.  A Novel Approach to Recognize Hand Movements Via sEMG Patterns , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[14]  R. Simpson Smart wheelchairs: A literature review. , 2005, Journal of rehabilitation research and development.

[15]  Inhyuk Moon,et al.  Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[16]  Oishee Mazumder,et al.  Door negotiation of a omni robot platform using depth map based navigation in dynamic environment , 2014, 2014 Seventh International Conference on Contemporary Computing (IC3).

[17]  Ulrich Borgolte,et al.  Architectural Concepts of a Semi-autonomous Wheelchair , 1998, J. Intell. Robotic Syst..

[18]  M. Mazo,et al.  System for assisted mobility using eye movements based on electrooculography , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Yoichi Hori,et al.  A New Control Method for Power-Assisted Wheelchair Based on the Surface Myoelectric Signal , 2010, IEEE Transactions on Industrial Electronics.

[20]  Sanghyun Joung,et al.  Safe and reliable intelligent wheelchair robot with human robot interaction , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[21]  Huosheng Hu,et al.  Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..

[22]  Yeh-Liang Hsu,et al.  Mobility Assistance Design of the Intelligent Robotic Wheelchair , 2012 .

[23]  L Fehr,et al.  Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey. , 2000, Journal of rehabilitation research and development.

[24]  Othman Omran Khalifa,et al.  Advances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged People , 2010 .

[25]  Kazuo Tanaka,et al.  Electroencephalogram-based control of an electric wheelchair , 2005, IEEE Transactions on Robotics.

[26]  Oishee Mazumder,et al.  Multichannel fused EMG based biofeedback system with virtual reality for gait rehabilitation , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[27]  R. Hari Krishnan,et al.  Mobility assistive devices and self-transfer robotic systems for elderly, a review , 2014, Intell. Serv. Robotics.

[28]  Gene Shuman,et al.  Using Forearm Electromyograms to Classify Hand Gestures , 2009, 2009 IEEE International Conference on Bioinformatics and Biomedicine.

[29]  Oishee Mazumder,et al.  Design of Wearable, Low Power, Single Supply Surface EMG Extractor Unit for Wireless Monitoring , 2011 .