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• W4386159322 abstract "Soft stretch sensors are increasingly used in wearable devices and flexible exoskeleton. This paper presents a novel sensing-actuation integrated unit for elastic tension transmission and force estimation. The unit consists of a capacitive sensor, four elastic bands, which can provide enough stiffness, and two stretchable paper-cut fabric shielding layers, which can greatly shield the external interference. The mechanical and electrical properties of the unit were tested on a universal material testing machine and then a simulation test platform was designed to generate the sine curve with different travels and stretch rates. A great amount of data with a total of 35 cases were collected to train our models. Results demonstrated mean square error (MSE) less than 0.21 N <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> , normalized root mean square error (NRMSE) less than 1.7% for the selected calibration model, and MSE less than 0.28 N <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> , NRMSE less than 2.0% for the selected prediction model. Our unit together with its calibration and prediction methods in this paper holds great promise in applications such as lightweight flexible exoskeletons." @default.
• W4386159322 created "2023-08-26" @default.
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• W4386159322 date "2023-07-08" @default.
• W4386159322 modified "2023-09-26" @default.
• W4386159322 title "Force Calibration and Prediction of Soft Stretch Sensor Based on Deep Learning" @default.
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• W4386159322 doi "https://doi.org/10.1109/icarm58088.2023.10218756" @default.
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