Flexible Pressure Sensors Based on Molybdenum Disulfide/Hydroxyethyl Cellulose/Polyurethane Sponge for Motion Detection and Speech Recognition Using Machine Learning

Flexible pressure sensors with excellent performance have broad application potential in wearable devices, motion monitoring, and human-computer interaction. In this paper, a flexible pressure sensor with a porous structure is proposed by coating molybdenum disulfide (MoS2) and hydroxyethyl cellulose (HEC) on a polyurethane (PU) sponge skeleton. The obtained sensor has excellent sensitivity (0.746 kPa-1), a wide detection range (250 kPa), fast response (120 ms), and outstanding repeatability over 2000 cycles. It is proven that the sensor can realize human motion detection and distinguish the touch of varying strength. In addition, a pressure sensing array was fabricated to reflect the pressure distribution and recognize the writing of Arabic numerals. Finally, the sensor performs speech detection through throat muscle movements, and high-accuracy (97.14%) speech recognition for seven words was achieved by a machine learning algorithm based on the support vector machine (SVM). This work provides an opportunity to fabricate simple flexible pressure sensors with potential applications in next-generation electronic skin, health detection, and intelligent robotics.