A broad, HRI-driven multimodal dataset, a rigorous evaluation emphasizing generalization, and practical evidence that personalization is likely required for robust deployment in practical HRI are contributed.
DMD is established as an effective, interpretable feature-extraction method for sEMG-based gesture classification, with applications in rehabilitation engineering, prosthetic control, and human-computer interaction.
Electromyography is a bioelectric signal recorded during muscle contraction or relaxation. The EMG signal is subject‐specific, reflecting which muscle is contracting at what intensity. In recent years, measuring electromyography has become easier with measuring devices designed as bracelets. Previous studies focused on...
Objective. Human–machine interfaces (HMIs) based on surface electromyography (sEMG) and A-mode ultrasound have achieved high gesture recognition accuracy; however, most studies have been conducted under a single static arm position. In practice, arm position variation degrades performance and limits translation beyond...
An affordable, multi-channel wearable FMG armband that reliably captures forces at multiple forearm muscle locations during different gestures and such a simple, low-cost, and effective scheme based on FMG can help control prosthetic and robotic devices using human intentions.
A systematic evaluation of sEMG processing pipelines for three-gesture recognition using a Bagged Trees classifier under Leave-One-Subject-Out (LOSO) cross-validation across ten participants provides empirically grounded guidelines for designing computationally efficient sEMG-based gesture recognition systems for colla...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
