Research on Gesture Recognition System Using Multiple Sensors Based on Earth’s Magnetic Field and 1D Convolution Neural Network

Abstract

A commonly used method of gesture recognition is the use of sensor technology. Typically, technology detecting Earth’s magnetic field is used in indoor positioning, and magnetic detection technology serves as a redundant method for gesture recognition devices. In this paper, we propose a novel system that utilizes multiple sensors measuring Earth’s magnetic field to collect data and perform gesture recognition through a one-dimensional convolutional neural network algorithm. By applying the detection of Earth’s magnetic field to gesture recognition, our system significantly improves the accuracy of recognition through a one-dimensional (1D) neural network algorithm. We conducted experiments where we collected and recognized American Sign Language standard letters, and achieved an accuracy rate close to 97%. Our experimental results demonstrate that this gesture recognition system using magnetic field sensors and a one-dimensional neural network algorithm is feasible for practical applications. Furthermore, our approach reduces the complexity of the device compared to the gesture recognition method based on artificial magnetic fields, while maintaining high recognition accuracy and not limiting the user’s hand movements. This technology holds great promise for the field of human–computer interaction.

Keywords

• human–computer interaction
• magnetic detection
• gesture recognition
• deep learning
• sign language recognition