EMGCipher: Decoding Electromyography for Upper-limb Gesture Classification with Explainable AI for Resource Optimization.

Assistive limb devices often employ surface electromyography (sEMG) and deep learning (DL) models for gesture classification. While DL models effectively classify diverse upper-limb gestures, their decision-making mechanisms often lack transparency. To address this, we introduce EMGCipher, an interpretable DL framework for upper-limb gesture classification using sEMG.

FedAssist: Federated Learning in AI-Powered Prosthetics for Sustainable and Collaborative Learning.

This paper explores the integration of federated learning in developing deep learning-powered surface electromyography decoding methods for AI-controlled prosthetics. Our proposed FL framework, FedAssist, aims to preserve data ownership while fostering decentralized collaborative modeling. Specifically, it focuses on mitigating the non-independent and identically distributed (non-IID) nature of sEMG datasets.

Decoding Gestures in Electromyography: Spatiotemporal Graph Neural Networks for Generalizable and Interpretable Classification.

In recent years, significant strides in deep learning have propelled the advancement of electromyography (EMG)-based upper-limb gesture recognition systems, yielding notable successes across a spectrum of domains, including rehabilitation, orthopedics, robotics, and human-computer interaction. Despite these achievements, prevailing methodologies often overlook the intrinsic physical configurations and interconnectivity of multi-channel sensory inputs, resulting in a failure to adequately capture relational information embedded within the connections of deployed EMG sensor network topology. This oversight poses a significant challenge, impeding the extraction of crucial features from collaborative multi-channel EMG inputs and subsequently constraining model performance, generalizability, and interpretability.

HeatGSNs: Integrating Eigenfilters and Low-Pass Graph Heat Kernels into Graph Spectral Convolutional Networks for Brain Tumor Segmentation and Classification.

Recent studies on graph representation learning in brain tumor learning tasks have garnered significant interest by encoding and learning inherent relationships among the geometric features of tumors. There are serious class imbalance problems that occur on brain tumor MRI datasets. Impressive deep learning models like CNN- and Transformer-based can easily address this problem through their complex model architectures with large parameters.

drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts.

Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification.

Upper limb loss can profoundly impact an individual's quality of life, posing challenges to both physical capabilities and emotional well-being. To restore limb function by decoding electromyography (EMG) signals, in this paper, we present a novel deep prototype learning method for accurate and generalizable EMG-based gesture classification. Existing methods suffer from limitations in generalization across subjects due to the diverse nature of individual muscle responses, impeding seamless applicability in broader populations.

Antimetastatic activity of seongsanamide B in γ-irradiated human lung cancer.

Lung cancer, which has a high incidence and mortality rates, often metastasizes and exhibits resistance to radiation therapy. Seongsanamide B has conformational features that suggest it has therapeutic potential; however, its antitumor activity has not yet been reported. We evaluated the possibility of seongsanamide B as a radiation therapy efficiency enhancer to suppress γ-irradiation-induced metastasis in non-small cell lung cancer.

Epidemic Vulnerability Index for Effective Vaccine Distribution Against Pandemic.

COVID-19 vaccine distribution route directly impacts the community's mortality and infection rate. Therefore, optimal vaccination dissemination would appreciably lower the death and infection rates. This paper proposes the Epidemic Vulnerability Index (EVI) that quantitatively evaluates the subject's potential risk.