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Capturing and analyzing wheelchair maneuvering patterns with mobile cloud computing.

Jicheng Fu, Wei Hao, Travis White

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary
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    This study introduces a mobile cloud computing framework to record and analyze power wheelchair maneuvering patterns using smartphone sensors. This approach enhances safety research and assistive technology development for wheelchair users.

    Area of Science:

    • Rehabilitation Engineering
    • Human-Computer Interaction
    • Mobile Cloud Computing

    Background:

    • Power wheelchairs enhance mobility for individuals with disabilities.
    • Frequent wheelchair-related accidents highlight the need for improved safety and maneuverability research.
    • Understanding wheelchair maneuvering patterns is crucial for developing safe assistive technologies.

    Purpose of the Study:

    • To propose and implement a mobile cloud computing framework for recording, storing, and analyzing power wheelchair maneuvering data.
    • To leverage smartphone sensors and cloud platforms for real-time data acquisition and analysis.
    • To enable further research in power wheelchair safety and robotic assistance.

    Main Methods:

    • Utilized smartphone accelerometer and gyroscope sensors for real-time data collection of wheelchair movements.

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  • Implemented a mobile cloud computing architecture for periodic data transmission to the cloud.
  • Employed Google App Engine on Android smartphones for a case study implementation.

    • Main Results:

    • Successfully recorded and stored real-time wheelchair maneuvering data.
    • Demonstrated the feasibility of the proposed mobile cloud computing framework through experimental results.
    • Made analyzed data accessible to diverse user groups via the cloud.

    Conclusions:

    • The mobile cloud computing framework effectively captures and analyzes power wheelchair maneuvering data.
    • This approach enhances smartphone capabilities for data storage and computation.
    • The framework provides a foundation for advancing power wheelchair safety and intelligent assistance systems.