###### 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  
accelerometer and magnetic field sensor composites [14]; accelerometer, gyroscope, and acoustic sensor composites [15]; and myoelectricity and accelerometer composites [16]. The data collected by sensors are processed through setting thresholds, machine learning [17], and other algorithms to achieve gesture recognition.  
Magnetic field sensors are commonly used as a supplementary feature in gesture recognition devices [18]. Some devices are designed to turn off magnetic field sensors during actual use to conserve power consumption [19]. However, as magnetic field detection technology continues to advance, positioning [20] and gesture recognition technology [21] based entirely on magnetic field sensors have been proposed, offering practical levels of accuracy for gesture recognition. Gesture recognition technology based on magnetic field detection has two main detection systems: artificial magnetic fields and geomagnetic fields. Artificial magnetic field sources include permanent magnets worn on the user's fingers [14] and energized coils that generate specific magnetic fields [22; 23]. However, a permanent magnet [14] or electromagnet [24] worn on the user's finger or wrist can complicate the system and impact the user's experience. On the other hand, a magnetic field sensor placed at a fixed location may limit the range of motion of the user's hand [21]. Technology based on detecting Earth's magnetic field is widely used for indoor positioning [20], but it is less commonly used in the field of gesture recognition. Nonetheless, with the development of magnetic field detection technology, it is possible that gesture recognition systems based on the Earth's magnetic field could become prevalent in the future.  
A Convolutional Neural Network (CNN) is a deep neural network that utilizes a convolutional structure. This algorithm is widely regarded as one of the core components of deep learning and is commonly applied in diverse fields such as computer vision [25; 26] and language processing [27; 28; 29; 30]. In a classic CNN, the convolution kernel is two-dimensional, which enables it to perform a sliding window operation on both the width and height of the feature map simultaneously. This technique is commonly employed in computer vision and image processing. In contrast, a one-dimensional CNN functions similarly to a two-dimensional CNN [31] but takes in one-dimensional data and outputs one-dimensional data after convolution and pooling operations. This type of CNN is mainly used in sequence models and natural language processing. The one-dimensional CNN has fewer parameters compared to the two-dimensional CNN, making it less dependent on large-scale datasets. The one-dimensional CNN is utilized for detecting Alzheimer's disease via online handwriting analysis [32], as well as for EEG-based motor imagery classification [33].  
In this paper, we propose a novel gesture recognition system based on the detection of Earth's magnetic field. The advantage of the system is that it does not require the use of artificial magnetic field sources, which simplifies the system and does not limit the user's mobility. To achieve accurate gesture recognition, we employ a 1D convolutional neural network algorithm to process the sensor data. This system has demonstrated promising results and offers a promising alternative to existing gesture recognition technologies.