Deep Learning-Assisted Organogel Pressure Sensor for Alphabet Recognition and Bio-Mechanical Motion Monitoring
Kusum Sharma, Kousik Bhunia, Subhajit Chatterjee, Muthukumar Perumalsamy, Anandhan Ayyappan Saj, Theophilus Bhatti, Yung-Cheol Byun, Sang‐Jae Kim
Abstract
Abstract Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human–machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti–freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol–gelatin (PVA/GLE) matrix. Fabricated using a binary solvent system of water and ethylene glycol (EG), the CoN CNT/PVA/GLE organogel exhibits excellent flexibility, biocompatibility, and temperature tolerance with remarkable environmental stability. Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range (40%-95% RH). Freeze-tolerant conductivity under sub-zero conditions (−20 °C) is attributed to the synergistic role of CoN CNT and EG, preserving mobility and network integrity. The CoN CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 kPa −1 in the detection range from 0 to 20 kPa, ideal for subtle biomechanical motion detection. A smart human–machine interface for English letter recognition using deep learning achieved 98% accuracy. The organogel sensor utility was extended to detect human gestures like finger bending, wrist motion, and throat vibration during speech.