# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 2 Materials and Methods
### Dataset  
In this study, data were collected from 20 participants aged between 19 and 27. All participants were right-handed and wore a smartwatch on their right wrist during the experiment. The study protocol was approved by the Institutional Review Board (IRB) of Pukyong National University (#1041386-202104-HR-12-01). The collected data captured right-arm movements in a three-dimensional space, encompassing motion characteristics in the anterior/posterior, medial/lateral, and vertical directions. The target movements were performed according to predefined directional instructions, which included upper-left, upper-right, lower-left, lower-right, and triangular patterns. Each movement consisted of three distinct sub-movements, with a brief pause of approximately 0.5 s between sub-movements to emphasize its distinctive features. All target patterns incorporated pauses and directional changes at either right or acute angles. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Year** & **Data Type** & **Main Contribution** & **Detection Type** & **Nature of Abnormal Data** \\ \hline
[26] & 2024 & Medical images (CT, MRI) & \begin{tabular}{c} Survey of OOD detection \\ techniques in medical \\ image analysis \\ \end{tabular} & \
\begin{tabular}{c} OOD \\ classification \\ \end{tabular} &
\begin{tabular}{c} Unseen semantic \\ categories \\ \end{tabular} \\ \hline
[27] & 2024 & \begin{tabular}{c} Time-series \\ (particle traces) \\ \end{tabular} & \begin{tabular}{c} Reliable deep learning under \\ OOD dynamics in anomalous \\ diffusion systems \\ \end{tabular} & \
\begin{tabular}{c} Anomaly \\ detection \\ \end{tabular} &
\begin{tabular}{c} Anomalies \\ within sequences \\ \end{tabular} \\ \hline
[28] & 2024 & \begin{tabular}{c} Time-series \\ (health records) \\ \end{tabular} & \begin{tabular}{c} Invariant learning for \\ generalization in time-series \\ forecasting \\ \end{tabular} & \
\begin{tabular}{c} Non-OOD \\ classification \\ \end{tabular} &
\begin{tabular}{c} Distributional \\ shift across \\ environments \\ \end{tabular} \\ \hline
[29] & 2024 & \begin{tabular}{c} Time-series (gesture, \\ speech, wearable signals) \\ \end{tabular} & \begin{tabular}{c} Framework for OOD detection \\ and robust classification under \\ distributional shift \\ \end{tabular} & \
\begin{tabular}{c} OOD detection \\ and non-OOD \\ classification \\ \end{tabular} &
\begin{tabular}{c} Unseen semantic \\ categories \\ \end{tabular} \\ \hline
[30] & 2024 & \begin{tabular}{c} Image (CIFAR-10, \\ CIFAR-100, SVHN, etc.) \\ \end{tabular} & \begin{tabular}{c} Ensemble-based OOD \\ detection \\ \end{tabular} & \
\begin{tabular}{c} OOD \\ classification \\ \end{tabular} &
\begin{tabular}{c} Unseen semantic \\ categories \\ \end{tabular} \\ \hline
[31] & 2024 & \begin{tabular}{c} Time-series (ECG, Power \\ Demand, Space \\ Shuttle, etc.) \\ \end{tabular} & \begin{tabular}{c} Contrastive learning with \\ distributional augmentation \\ \end{tabular} & \
\begin{tabular}{c} Anomaly \\ detection \\ \end{tabular} &
\begin{tabular}{c} Anomalies \\ within sequences \\ \end{tabular} \\ \hline
[32] & 2025 & \begin{tabular}{c} Time-series (epilepsy, \\ handwriting, etc.) \\ \end{tabular} & \begin{tabular}{c} Survey of OOD methods for \\ time-series signals \\ \end{tabular} & \
\begin{tabular}{c} OOD \\ classification \\ \end{tabular} &
\begin{tabular}{c} Unseen semantic \\ categories \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The movements were labeled using alphabetical notations, where \"F\" denotes a forward movement, \"R\" and \"L\" indicate rightward and leftward movements, and \"U\" and \"D\" represent upward and downward movements. The target movement set included four fundamental patterns (FRU, FRD, FLU, FLD) along with a triangular movement pattern (TRA) (Figure 1a). The forward movement was added to the four fundamental patterns to serve as a potential trigger for future studies.