Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. In this paper, we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI. An overview of typical network structures and processing schemes will be provided.

2023年4月12日 · Here, we report an array of bipolar stretchable sEMG electrodes with a self-attention-based graph neural network to recognize gestures with high accuracy. The array is designed to spatially cover...

2023年12月27日 · This narrative review covers the fundamentals of AI, highlights current advancements and the utilization of AI in electrodiagnostic (EDX) medicine (EDX being a term encompassing nerve conduction studies [NCSs] and needle electromyography [EMG], collectively) and neuromuscular medicine (NM), including aspects pertinent to neuromuscular ...

Electromyography (EMG) is the superposition of motor unit action potentials (MUAPs) in muscle fibers. Their main role is to be the link between the person's musculoskeletal and neurological systems. Hence, EMG signals have been used in health care to detect neurodegenerative diseases that affect motor functions such as Parkinson's and stroke.

2023年3月24日 · In this paper, we propose a dynamic hand-gesture-based industrial robot control system using the edge AI platform. The proposed system can perform both robot operating-system-based control and edge AI control through an embedded board without requiring an external personal computer.

2023年3月23日 · Here, we describe an EMG simulation method, based on the numerical solution of the forward equations suitable for deep learning data augmentation. It produces highly realistic EMG recordings,...

2024年3月1日 · This scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals.

Neuro-Sense AI identifies complex neuromuscular signatures with unprecedented accuracy. Using deep learning algorithms trained on extensive clinical data, we can detect subtle patterns that may indicate early onset of conditions or unique patient characteristics. This enables faster, more accurate diagnostics and personalized treatment planning.

2023年5月25日 · EMGSense可以通过使用人工智能自我训练技术，为新用户提供高度灵敏及准确的感应效能，为开发更先进及准确的可穿戴EMG器件、以应用在神经康复和虚拟现实等领域，开辟了新道路。

2021年3月1日 · Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. In this paper, we analyze recent papers and present a literature review describing the role that deep...