Smart electronic skin having gesture recognition function by LSTM neural network

Abstract

Rapid growth of soft electronics has enabled various approaches for developing artificial skin. However, currently existing electronic skin is still facing some problems such as high fabrication complexity, high production cost, and smartness of recognizing the stimulus automatically. In this work, we report a simple, low-cost Polydimethylsiloxane (PDMS)-based smart electronic skin system, consisting of a sensor array and a data processing system. The sensor array can be easily mounted on the human body or robot hand as a result of excellent softness, stretchability, and bendability of PDMS. Signals from the sensor array are processed by a Long and Short Term Memory neural network algorithm in the data processing system. The trained data processing system can recognize four types of gestures at an accuracy of 85 ± 5%, even taking into account environmental variations including folding, curvature, tensile strength, temperature, and endurance cycles. This work proves that this type of skin can be endowed with intelligence with a proper neural network algorithm and fabricated at low cost and reduced complexity.