Deep Learning‑Assisted Organogel Pressure Sensor for Alphabet Recognition and Bio‑Mechanical Motion Monitoring
- Journal
- Nano-Micro Letters
- Status
- Volume 18, Issue 1
- Page
- 63
- Year
- 2025
- Link
- http://doi.org/10.1007/s40820-025-01912-z 246회 연결
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, selfadhesive, 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.