A Geometric Model-Based Approach to Hand Gesture Recognition
Alexandre Calado, Paolo Roselli, Vito Errico, Nathan Magrofuoco, Jean M. Vanderdonckt, Giovanni Saggio · IEEE Transactions on Systems Man and Cybernetics Systems · 2022
Arm-and-hand tracking by technological means allows gathering data that can be elaborated for determining gesture meaning. To this aim, machine learning (ML) algorithms have been mostly investigated looking for a balance between the highest recognition rate and the lowest recognition time. However, this balance comes mainly from statistical models, which are challenging to interpret. In contrast, we present$\mu C^{1}$and$\mu C^{2}$, two geometric model-based approaches to gesture recognition which support the visualization and geometrical interpretation of the recognition process. We compare$\mu C^{1}$and$\mu C^{2}$with respect to two classical ML algorithms, k-nearest neighbor (k-NN) and support vector machine (SVM), and two state-of-the-art (SotA) deep learning (DL) models, bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU), on an experimental dataset of ten gesture classes from the Italian Sign Language (LIS), each repeated 100 times by five inexperienced non-native signers, and gathered with wearable technology (a sensory glove and inertial measurement units). As a result, we achieve a compromise between high recognition rates ($>90\%$) and low recognition times ($ < 0.1 {\mathrm{ s}}$) that is adequate for human–computer interaction. Moreover, we elaborate on the algorithms’ geometric interpretation based on geometric algebra, which supports some understanding of the recognition process.