IEEE CopyrightAlexandre CaladoPaolo RoselliVito ErricoNathan MagrofuocoJean VanderdoncktGiovanni Saggio2025-06-172025-06-172022-10-012168-22162168-223210.1109/tsmc.2021.3138589https://ror.circle-u.eu/handle/123456789/902017Arm-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 μ C¹ and μ C², two geometric model-based approaches to gesture recognition which support the visualization and geometrical interpretation of the recognition process. We compare μ C¹ and μ C² 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 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.OPENexplainable artificial intelligence (XAI)gyroscopetraininggesture recognitionmachine learning (ML)006algebrageometric algebraComputer Science ApplicationsAccelerometerHuman-Computer InteractionAccelerometers; algebra; assistive technologies; deep learning (DL); explainable artificial intelligence (XAI); geometric algebra; gesture recognition; gyroscopes; machine learning (ML); nearest neighbor classification (NNC); training; trajectoryassistive technologiedeep learning (DL)Control and Systems EngineeringtrajectorySettore ING-INF/01 - ELETTRONICAnearest neighbor classification (NNC)Electrical and Electronic EngineeringSoftwarepublication02 engineering and technology0202 electrical engineering, electronic engineering, information engineeringdoi_dedup___:9e3f109c2470b5758b1232554c9c4c0d2078.1/2558252108/289168