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Improving Human-Machine Interaction for a Powered Wheelchair Driver by Using Variable-Switches and Sensors that Reduce Wheelchair-Veer

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

The integration of proportional switches for human-computer interaction and sensors with veer correction systems are presented. The transducers and sensors improve control, assist wheelchair drivers and reduced wheelchair veer, especially on slopes. The systems also reduce effort. The proportional switches are particularly useful for disabled people who do not have enough skill to use a joystick, or who lack sufficient hand-grasp and release ability, or who have movement disorders. The new systems were tested using laboratory test rigs. The test rigs were reused later to teach human users. A rolling road was then built to test the systems before user trials were undertaken. The angle of the wheelchair casters provided feedback and that feedback was used to reduce drift. A new electronic system matched the caster angles to the driver input. A case study is described. Results are presented, and they suggest there are advantages to using variable rather than digital or binary switches. The veer correction system can assist when a user is traversing a slope. The transducers and systems have been tested at Chailey heritage and are proved to be useful in assisting powered-wheelchair users. The proportional switches isolate the gross motor functions and filter out uncontrolled movement. The sensor system helps users to steer on uneven or sloping ground. The transducers also provide more control during turning and can reduce the turn radius as well as can lower frustration and conserving energy.

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Author information

Authors and Affiliations

  1. School of Mechanical and Design Engineering, University of Portsmouth, Portsmouth, UK

    David Sanders, Ya Huang, Sergey Khaustov & Sarinova Simandjunta

  2. Chailey Heritage Foundation, Lewes, UK

    Martin Langner

  3. School of Energy and Electronic Engineering, University of Portsmouth, Portsmouth, UK

    Nils Bausch

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  1. David Sanders
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  2. Martin Langner
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  3. Nils Bausch
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  4. Ya Huang
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  5. Sergey Khaustov
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  6. Sarinova Simandjunta
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Correspondence to David Sanders .

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Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Sanders, D., Langner, M., Bausch, N., Huang, Y., Khaustov, S., Simandjunta, S. (2020). Improving Human-Machine Interaction for a Powered Wheelchair Driver by Using Variable-Switches and Sensors that Reduce Wheelchair-Veer. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_84

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