# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 1 Introduction  
Hand gestures are a significant aspect of human communication, enriching interactions by offering additional expressive capabilities. They allow individuals to visually convey emotions or intentions that may be challenging to express through speech. In certain contexts, gestures even serve as an effective alternative to verbal communication or complement it. They are particularly useful in environments with high noise levels, where visual communication is more practical, and in settings that require silence. Furthermore, hand gestures provide a valuable means of communication for individuals with speech impairments or pronunciation difficulties. In recent years, hand gestures have been increasingly explored as a means of interaction between humans and computers (HCI). Automatic recognition of hand gestures enables the recording, analysis, and classification of specific hand or arm movements using computer algorithms. The most prevalent methodology for capturing hand gestures is thecamera-based approach, which employs an optical camera. This approach aims to detect entire hands, fingertips, pens, or handheld devices. Hand gestures for HCI are often designed without finger or wrist movements when the number of commands is limited. In such cases, the accuracy of hand gesture recognition systems in camera-based approaches primarily depends on the precise detection of pen tips or fingertips. Once the tips are accurately detected, hand positions can be easily tracked across consecutive video frames [1]. This tracking enables hand gesture recognition to be treated similarly to handwritten character recognition, which has achieved high levels of accuracy over the past few decades. Various techniques have been proposed, including linear discriminant analysis, multi-layer perceptron, tangent distance, and support vector machines [2,3]. Recent research indicates that convolutional neural networks can achieve accuracies exceeding 99% in handwritten character recognition [4,5]. Consequently, various techniques have been explored to enhance hand detection accuracies in camera-based systems. Color information was effectively utilized to identify the pen tip position in [6], assuming a fixed pen color during the experiments. A similar approach was employed to detect hand regions by predefining hand color in a controlled environment [7]. To further improve hand gesture recognition, the detection of hands or fingertips can be enhanced by replacing the optical camera with a depth sensor, which provides additional depth information about the object. For instance, the software development kit (SDK) of a Kinect sensor was utilized to detect hand positions, while the fingertip positions were calculated from hand and depth data [8,9]. The Kinect sensor has also been used to track fingertip positions and recognize tapping gestures, demonstrating its effectiveness in gesture recognition [10]. Similarly, an alternative approach to hand tracking involving depth measurement with an ultrasonic transceiver has been introduced [11]. Despite these advancements, camera- and depth sensor-based methods have inherent limitations, as they typically require users to remain within the field of view of the sensor. Although head-mounted cameras, as proposed in [12], alleviate this constraint to some extent, they still present practical challenges. An alternative method for recording hand gestures involves the use of accelerometer and gyroscope sensors, which provide users with greater freedom of movement. As these sensors are integrated into most modern smartwatches and fitness bands, users can utilize them as input devices with minimal effort. Furthermore, this approach remains stable regardless of lighting conditions. However, a major challenge associated with accelerometer- and gyroscope-based methods is the accurate determination of sensor positions within the global coordinate system. Due to sensor movement or rotation during data acquisition, accelerometer values are aligned with the local coordinate system of the sensors.