Dual-mode superhydrophobic, highly breathable proximity-tactile cellulose nonwoven sensor for speech recognition via machine learning
摘要
While smart textile sensors have made significant progress in the fields of health monitoring, human–computer interaction, and speech recognition, they also face many challenges, including low sensitivity, breathability, and hydrophobicity. In this study, we prepared nonwoven based sensors using ultrasound-assisted modification and dip-drying method. They have high air permeability (505 mm/s) and superhydrophobic property, with a water contact angle of 164.4°, and can monitor proximity and tactile signals simultaneously. During proximity detection, the sensing distance is 13 cm with a maximum relative change of 8%, a maximum sensitivity of 3.16%/cm, and a response time of 250 ms. As far as tactile sensing performance is concerned, the sensor has a pressure sensing range of 118 kPa, high sensitivity of 1.95 kPa−1 (0–0.28 kPa), excellent cycle durability in over 2,000 pressure cycle tests, and a rapid response and recovery time (70 ms/70 ms). Additionally, the sensor is capable of detecting voice vibration signals with an accuracy of 97.5% through machine learning technology. Due to these excellent performances, it is believed that the sensor has a wide range of application prospects, including health monitoring, motion monitoring, and speech recognition.