NeuroEdge: Real-Time Hand Gesture Recognition with High-Density EMG Using Deep Learning at the Edge
arXiv:2605.29326v1 Announce Type: new Abstract: High-density electromyography (HD-EMG) has emerged as a powerful modality for decoding fine-grained neuromuscular activity, enabling real-time neural-machine interfaces (NMIs) for applications such as prosthetic control, rehabilitation, and augmented interaction. While deep learning approaches such as convolutional neural networks (CNNs)have demonstrated high classification accuracy for EMG-based gesture recognition, their deployment on embedded hardware remains a major challenge due to computational and memory constraints. This paper presents Ne
The proliferation of deep learning and the increasing demand for real-time, on-device AI inference are driving innovation in efficient edge computing solutions.
This development allows for more sophisticated and responsive neural-machine interfaces to be deployed in resource-constrained environments, expanding applications in prosthetics, rehabilitation, and human-computer interaction.
The ability to run complex deep learning models for gesture recognition directly on edge devices reduces latency, improves privacy, and broadens the accessibility of advanced BCI technologies.
- · Medical technology companies
- · Deep learning hardware developers
- · Wearable technology manufacturers
- · Patients with motor impairments
- · Cloud-dependent NMI solutions
- · Traditional bulky EMG systems
- · Software without efficient edge deployment strategies
Improved performance and decreased cost of prosthetic limbs and rehabilitation devices utilizing advanced gesture recognition.
Accelerated development of augmented reality and virtual reality interfaces controlled by intuitive hand gestures.
The commercialization of general-purpose humanoid robots with more natural and precise interaction capabilities via advanced sensory input.
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Read at arXiv cs.LG