Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures

Hand gestures serve as a fundamental means of communication, and extensive research has been conducted to develop automated recognition systems. These systems are expected to improve human/computer interaction, particularly in environments where verbal communication is limited. A key challenge in these systems is the classification of non-target actions, as everyday movements are often not included in the training set, but resembling target gestures can lead to misclassification. Unlike previous studies that primarily focused on target action recognition, this study explicitly addresses the unseen non-target classification problem through an experiment to distinguish target and non-target activities based on movement characteristics. This study examines the ability of deep learning models to generalize classification criteria beyond predefined training sets. The proposed method was validated with arm movement data from 20 target group participants and 11 non-target group participants, achieving an average F1-score of 84.23%, with a non-target classification score of 73.23%. Furthermore, we confirmed that data augmentation and incorporating a loss factor significantly improved the recognition of unseen non-target gestures. The results suggest that improving classification performance on untrained, non-target movements will enhance the applicability of gesture recognition systems in real-world environments. This is particularly relevant for wearable devices, assistive technologies, and human/computer interaction systems.