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Object Recognition Using Soft Sensors

Lim, Chu Chen (2021) Object Recognition Using Soft Sensors. Final Year Project, UTAR.

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    The development of soft sensors with high sensitivities and good response time is currently researched in great interest, especially in healthcare and soft robotics systems. However, there is a lack of study to equip the soft sensors with a smart feature. Therefore, this study proposes a smart glove that can recognise objects using a support vector machine (SVM), a supervised machine learning algorithm. The input to the smart glove is obtained from the integrated resistive strain-based flexible sensors. The characterisation of the resistive sensor was done, and the sensitivity was found to be 0.0145 kΩ/°. The glove is able to recognise three distinct object shapes with an accuracy of up to 92%. Through AI-based object recognition and its high accuracy, this glove provides a promising solution for a low-cost soft sensor solution for the area of soft robotics.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Lee Kong Chian Faculty of Engineering and Science > Bachelor of Engineering (Honours) Electrical and Electronic Engineering
    Depositing User: Sg Long Library
    Date Deposited: 12 Jun 2021 05:28
    Last Modified: 12 Jun 2021 05:28
    URI: http://eprints.utar.edu.my/id/eprint/4056

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