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Found 7 papers, 2 papers with code
ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart.
EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement
Multimodal learning has been proven to be an effective method to improve speech enhancement (SE) performance, especially in challenging situations such as low signal-to-noise ratios, speech noise, or unseen noise types.
Instrumented shoulder functional assessment using inertial measurement units for frozen shoulder
Moreover, features of subtasks provided subtle information related to clinical conditions that have not been revealed in features of a complete task, especially the defined subtask 1 and 2 of each task.
Domain-adaptive Fall Detection Using Deep Adversarial Training
The proposed DAFD can transfer knowledge from the source domain to the target domain by minimizing the domain discrepancy to avoid mismatch problems.
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG.
Deep Learning Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems
However, the performance of FD systems is diminished owing to low-resolution (LR) accelerometer signals.
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application
The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC), allowing CITISEN to be used as a platform for utilizing and evaluating SE models and flexibly extend the models to address various noise environments and users.
