Wearable and Cost-Effective Pressure Sensor Based on a Carbon Nanotube/Polyurethane Sponge for Motion Detection and Gesture Recognition

Flexible pressure sensors are important for various fields including human–machine interaction, motion detection, and gesture recognition. In this study, a piezoresistive pressure sensor is developed using a composite material of carbon nanotubes and a polyurethane sponge. The sensor is fabricated t...

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Veröffentlicht in:ACS applied electronic materials 2023-12, Vol.5 (12), p.6704-6715
Hauptverfasser: Wang, Feilu, Zhang, Wangyong, Song, Yang, Jiang, Xiuli, Sun, Niuping
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creator Wang, Feilu
Zhang, Wangyong
Song, Yang
Jiang, Xiuli
Sun, Niuping
description Flexible pressure sensors are important for various fields including human–machine interaction, motion detection, and gesture recognition. In this study, a piezoresistive pressure sensor is developed using a composite material of carbon nanotubes and a polyurethane sponge. The sensor is fabricated through a dipping-drying method, which enables the carbon nanotubes (CNTs) to adhere to the skeleton of the polyurethane sponge (PUS). The sensor obtained displays superior features: excellent sensitivity (2.7% kPa–1), prompt response (response/recovery time of 60/100 ms), and remarkable long-term stability demonstrated by a consistent response signal during loading/unloading cycles with the range of 0–100 kPa at 0.1 Hz for a period of 18,000 s. In addition, the sensor was placed on different parts of the human body to detect human motion signals. It has been demonstrated that the sensor can effectively capture these diverse signals to distinguish between different motion states. Additionally, the sensor can accurately convey the Morse code of the 26 letters of the alphabet and the 10 Arabic numerals through regular pressing. Finally, a sensory glove was created using the sensors, which is used to express the gestures of Arabic numerals 0–9. A deep-learning algorithm based on the Inception Network has achieved a high-accuracy (99.5%) gesture recognition for 10 gestures. This work offers a cost-effective and simple way to produce flexible pressure sensors that can be employed in various applications, including human motion detection, wearable devices, gesture recognition, and human-machine interaction.
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Finally, a sensory glove was created using the sensors, which is used to express the gestures of Arabic numerals 0–9. A deep-learning algorithm based on the Inception Network has achieved a high-accuracy (99.5%) gesture recognition for 10 gestures. 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