Graph Neural Networks for HD EMG-based Movement Intention Recognition: An Initial Investigation

Massa, Silvia M.; Riboni, Daniele; Nazarpour, Kianoush

doi:10.1109/rasse54974.2022.9989657

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Recently, researchers have started using GNNs to analyze sEMG signals. For instance, Massa et al [9] applied GNNs to high-density sEMG data to recognize action intentions, reaching a low classification error rate of 8.75% over 65 gestures. Vijayvargiya et al [10] used a GNN based on Pearson correlation for analyzing lower limb sEMG signals and achieved a high accuracy of up to 99.36% in recognizing different activities.…”

Section: Graph Network In Semg Signal Analysismentioning

confidence: 99%

Sequential sEMG Recognition with Knowledge Transfer and Dynamic Graph Network based on Spatio-Temporal Feature Extraction Network

Li,

Chen,

et al. 2024

Preprint

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Surface electromyography (sEMG) signals are electrical signals released by muscles during movement, which can directly reflect the muscle conditions during various actions. When a series of continuous static actions are connected along the temporal axis, a sequential action is formed, which is more aligned with people's intuitive understanding of real-life movements. The signals acquired during sequential actions are known as sequential sEMG signals, including an additional dimension of sequence, embodying richer features compared to static sEMG signals. However, existing methods show inadequate utilization of the signals' sequential characteristics. Addressing these gaps, this paper introduces the Spatio-Temporal Feature Extraction Network (STFEN), which includes a Sequential Feature Analysis Module based on static-sequential knowledge transfer, and a Spatial Feature Analysis Module based on dynamic graph networks to analyze the internal relationships between the leads. The effectiveness of STFEN is tested on both modified publicly available datasets and on our acquired Arabic Digit Sequential Electromyography (ADSE) dataset. The results show that STFEN outperforms existing models in recognizing sequential sEMG signals. Experiments have confirmed the reliability and wide applicability of STFEN in analyzing complex muscle activities. Furthermore, this work also suggests STFEN's potential benefits in rehabilitation medicine, particularly for stroke recovery, and shows promising future applications.

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Section: Graph Network In Semg Signal Analysismentioning

confidence: 99%

Sequential sEMG Recognition with Knowledge Transfer and Dynamic Graph Network based on Spatio-Temporal Feature Extraction Network

Li,

Chen,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous studies investigated different machine learning methods to exploit HD EMG data for gesture recognition [12]. However, to the best of our knowledge, no previous study, except our preliminary investigation reported in [13], used a graph neural network (GNN) in conjunction with HD EMG signals to identify the gesture intention of an amputee.…”

Section: Introductionmentioning

confidence: 99%

“…Summarizing, the main contributions of our work with respect to the preliminary investigation presented in [13] are the following:…”

Section: Introductionmentioning

confidence: 99%

Explainable AI-Powered Graph Neural Networks for HD EMG-Based Gesture Intention Recognition

Massa,

Riboni,

Nazarpour

2024

IEEE Trans. Consumer Electron.

Self Cite

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The ability to recognize fine-grained gestures enables several applications in different domains, including healthcare, robotics, remote control, and human-computer interaction. Traditional gesture recognition systems rely on data acquired from cameras, depth sensors, or smart gloves. More recently, techniques for recognizing gestures based on signals acquired by high-density (HD) EMG electrodes worn on the forearm have been proposed. An advantage of these techniques is that they do not rely on the use of external devices, and they are feasible also to people who underwent amputation. Unfortunately, the extraction of complex features from raw HD EMG signals may introduce delays that deter the real-time requirements of the system. To address this issue, in a preliminary investigation we proposed to use graph neural networks for gesture recognition from raw HD EMG data. In this paper, we extend our previous work by exploiting Explainable AI algorithms to automatically refine the graph topology based on the data in order to improve recognition rates and reduce the computational cost. We performed extensive experiments with a large dataset collected from 20 volunteers regarding the execution of 65 fine-grained gestures, comparing our technique with state-of-the-art methods based on handcrafted features and different machine learning algorithms. Experimental results show that our technique outperforms the state of the art in terms of recognition performance while incurring significantly lower computational cost at run-time.

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Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition

Zhang,

Yu,

Wang

et al. 2024

Biomedical Signal Processing and Control

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Graph Neural Networks for HD EMG-based Movement Intention Recognition: An Initial Investigation

Cited by 8 publications

References 16 publications

Sequential sEMG Recognition with Knowledge Transfer and Dynamic Graph Network based on Spatio-Temporal Feature Extraction Network

Sequential sEMG Recognition with Knowledge Transfer and Dynamic Graph Network based on Spatio-Temporal Feature Extraction Network

Explainable AI-Powered Graph Neural Networks for HD EMG-Based Gesture Intention Recognition

Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition

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