Abstract
Recent advances in nanomaterials render the possibility of fabricating tactile sensors suitable for electronic skin, haptic interfaces, and biomedical applications. Furthermore, the problem of performing complex inertial measurements generated during the tactile process of polymer composites can be tackled using artificial intelligence (AI). However, the implementation of AI-based signal processing on embedded devices still represents an open area of opportunity for the design of the next generation of smart wearable sensor systems. A novel nanostructured smart tactile sensing system for wearable applications is proposed, using a 3-D-printed structure with embedded electronic devices. The highly sensitive piezoresistive tactile sensor is based on multiwall carbon nanotube/polypropylene (MWCNT/PP) composites. The integration of electronic circuits for signal processing of an artificial neural network (NN) on a digital controller unit improves the tactile interpretation in the wearable embedded device. Experiments show that the pressure classification results on MWCNT/PP composites with 98% accuracy.
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