# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 1 Introduction
To address this issue, an automated coordinate conversion methodology was proposed in [12]. However, this approach requires the availability of ground truth traces, which are often not readily accessible. The accuracy of accelerometer- and gyroscope-based methods is inferior to that of camera-based methods. For instance, camera-based approaches have achieved a maximum accuracy of 99.571% for Arabic numbers and 16 directional symbols [7] and 95% for English letters and Arabic numbers [8]. In particular, 98.1% accuracy was achieved for six directive gestures at distances of up to 25 m. This result was obtained using a Graph Vision Transformer (GViT) with an RGB camera and HQ-Net for super-resolution [13]. Additionally, an accuracy of 77% was recorded for 15 sign language gestures using SNN with STBP on an event-camera dataset (DVS_Sign_v2e) [14]. In contrast, accelerometer- and gyroscope-based methods achieved 79.6% accuracy for Japanese Katakana (46 letters) using the K-nearest neighbor (K-NN) method [15], 88.1% for Japanese Hiragana (46 letters)with the hidden Markov model (HMM) [16], and 89.2% by combining K-NN with dynamic time warping (DTW) techniques [17]. Recently, sensor-based approaches have been improved through deep learning models, such as ABSDA-CNN with a 64-channel EMG sensor, achieving an accuracy of 88.91% for inter-session gesture recognition [18]. Some sensor-based approaches have achieved high accuracy by integrating multiple devices. For instance, [PERSON] et al. achieved an accuracy of 95.3% using a combination of RGB cameras, depth sensors, EMG, and EEG [19]. Sensor-based gesture recognition systems are often expected to operate asynchronously without the need for explicit triggers. In such cases, distinguishing between target and non-target classes is crucial to prevent unintended activations and ensure accurate recognition. Specifically, non-target classes consist of actions that resemble target gestures but commonly occur in everyday activities, making them more challenging to differentiate. Proper identification of these patterns is essential to minimize false positives and ensure reliable system performance in real-world scenarios. Previous research has predominantly ignored non-target actions, collecting and training data exclusively for target actions. However, it is also important to prevent non-target actions from being classified as target actions in addition to recognizing target actions. Classifying non-target actions can be considered a form of out-of-distribution (OOD) detection or anomaly detection, which has been widely studied in recent decades. Table 1 summarizes representative works [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32] in these domains. Much of the prior research has focused on image-based tasks [20; 21; 22; 26; 30], while studies on time-series data, such as sensors and gestures, have been relatively limited. These studies on time-series signals have primarily addressed anomaly detection by identifying irregularities within temporal patterns [20; 23; 24; 25; 31]. In addition, some efforts have been made to improve the robustness of classification models to abnormal variations [28; 29]. Recent findings further suggest that conventional OOD detection approaches are insufficient for handling complex time-series scenarios involving unseen semantic categories [32]. [PERSON] et al. addressed this by treating certain time-series classes as OOD and evaluating the model's ability to recognize them [29]. In their study, a subset of classes within a multi-class dataset was designated as OOD, and the model was tested on its ability to classify these unseen OOD classes.