Object Recognition Using Soft Sensors - Lim, Chu Chen (2021)
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.