PC-GNN: Pearson Correlation-Based Graph Neural Network for Recognition of Human Lower Limb Activity Using sEMG Signal

• Published in: IEEE transactions on human-machine systems Vol. 53; no. 6; pp. 945 - 954
• Main Authors: Vijayvargiya, Ankit, Kumar, Rajesh, Sharma, Parul
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Artificial intelligence; Biomedical signal processing; Correlation; Electromyography; Empirical mode decomposition; ensemble empirical mode decomposition (EMD); Exoskeletons; Feature extraction; graph neural network (GNN); Graph neural networks; handcrafted feature extraction; Human activity recognition; human lower limb activity recognition; Image processing; Medical imaging; Muscles; Neural networks; Pattern recognition; Sensors; wavelet denoising; Wearable sensors
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2023.3319356

Artificial intelligence has a plethora of applications in the realm of biomedical sciences, such as pattern recognition, diagnosis of disease, human-machine interaction, medical image processing, robotic limbs, or exoskeletons. Robotic limbs, or exoskeletons, are widely employed to assist with lower limb movement. To increase the exoskeleton's flexibility in the lower extremities, it is critical to recognize the diverse motion intents of the lower limbs of the human body. In this investigation, sEMG signals from lower limb muscles are used for a computer-aided recognition system to correctly identify the lower limb activities because these signals can identify movement ahead of time and enable faster detection of signal fluctuation than other wearable sensors. Several types of noise are introduced into the signal during collection. A multistage classification strategy is proposed to overcome the processing challenges associated with these sEMG signals. Initially, nine time-domain handcrafted features are retrieved using a hybrid of wavelet denoising and ensemble empirical mode decomposition approach with a sliding window of 256 ms and a 25% overlap. Next, a Pearson correlation-based graph is formed from the extracted features and applied to a graph neural network (GNN). GNN not only captures individual information but also makes use of information from other samples to form a graph. The combination of a Pearson correlation-based graph with a GNN is referred to as Pearson correlation-based GNN. The observation states that the approach proposed in the research achieved an accuracy of 99.19%, 99.02%, 96.21% for the walking, sitting, and standing of healthy subjects, while 99.29%, 97.97%, 99.36% for the subjects comprising knee abnormalities, respectively.