arXiv AI
Design and Embedded Validation of Compact ML Models for Affective Touch Classification in a Soft Interactive Companion
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arXiv AI
Jul 21
Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions
arXiv:2607. 16196v1 Announce Type: new Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect.
arXiv Machine Learning
Sep 21
From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition Across Wearable Sensor Modalities
The study compares temporal deep learning models—Bidirectional LSTM, Temporal Convolutional Network, and Transformer—for physiological emotion recognition using two multimodal wearable datasets, WESAD and EmoWear. Experiments evaluate wrist-only, chest-only, and multimodal sensor configurations with participant-independent leave-one-subject-out cross-validation, and also explore ensembles, sensor ablation, sampling frequency, and saliency analysis. Results show that the best architecture varies by dataset, multimodal sensing consistently outperforms single-site configurations, and a 4 Hz sampling rate offers a cost-effective operating point.
arXiv AI
Jul 28
Multimodal Surface EMG Hand Gesture Recognition Using Query-Based Transformers for Prosthetic Control
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
arXiv AI
Jun 30
Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
arXiv:2508. 12435v2 Announce Type: replace-cross Abstract: While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors.
arXiv AI
Sep 1
Toward Postural State Classification in Immersive VR with Multimodal Data and Explainability Analysis
This study evaluates machine learning and deep‑learning models for classifying balanced versus imbalanced postural states in immersive virtual reality using a multimodal dataset of kinematic, EMG, and EDA signals. The Mamba‑inspired CNN (MI‑CNN) achieved the highest accuracy (96.76%) and, through SHapley Additive exPlanations (SHAP), identified kinematic features as the most influential for detecting imbalance. Even after reducing input dimensionality by 33% based on SHAP importance, the model maintained near‑optimal performance (0.957 accuracy and F1‑score).
arXiv Machine Learning
Jul 20
An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification
arXiv:2607. 15972v1 Announce Type: new Abstract: Accurate hand gesture recognition using surface electromyography (sEMG) typically relies on multichannel sensor arrays and computationally intensive models.