12 citations · 21 across the 10 of their papers we have counts for
10 papers
SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising
Cheng-Han Shih, Kuan-Chen Wang, Kai-Chun Liu +2
Electrocardiogram (ECG) artifact contamination frequently occurs in surface electromyography (sEMG) when muscles are recorded near the heart. Existing neural network (NN)-based app…
Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data
Prithviraj Tarale, Kiet Chu, Abhishek Varghese +4
Wearable accelerometers enable large-scale health monitoring, yet learning robust human-activity representations has been constrained by scarce labeled data. While self-supervised…
A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment
Jiyue Jiang, Yanyu Chen, Pengan Chen +7
Cognitive impairment is becoming a major public health challenge. Cognitive Stimulation Therapy (CST) is an effective intervention for cognitive impairment, but traditional methods…
MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Distillation
Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou +2
The rising aging population has increased the importance of fall detection (FD) systems as an assistive technology, where deep learning techniques are widely applied to enhance acc…
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images
Wei-Lun Chen, Chia-Yeh Hsieh, Yu-Hsiang Kao +3
This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Time…
MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution
Jie Lin, I Chiu, Kuan-Chen Wang +4
Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monito…
TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement
Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh +2
Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remo…
MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal
Kuo-Hsuan Hung, Kuan-Chen Wang, Kai-Chun Liu +4
Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical i…
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising
Yu-Tung Liu, Kuan-Chen Wang, Kai-Chun Liu +2
Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. Several existing methods use…
A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals
Cho-Yuan Lee, Kuan-Chen Wang, Kai-Chun Liu +4
In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination…