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 6 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.

Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning

“…Back-propagation was used to track hand trajectories using an inertial sensor with 89% accuracy [25]. A smart electronic-skin comprising an array of detectors and LSTM processing was proposed [26].…”

Design of a constant loop bandwidth phase-locked loop based on artificial neural network

“…In [21], ANN also enlightened RF Power Amplifier. And [22] utilized the brain-like behavior of ANN to realize the gesture recognition.…”

Design of a Neural Network-Based VCO With High Linearity and Wide Tuning Range

“…In [13], RF Power Amplifier was also enlightened by ANN. And [14] utilized the brain-like behavior of ANN to finish the gesture recognition function.…”