sEMG-Based Tremor Severity Evaluation for Parkinson's Disease Using a Light-Weight CNN

We propose a deep learning based approach for quantifying the tremor severity of Parkinson's disease (PD) based on surface electromyography (sEMG). We design the S-Net, a light weight and computational efficient convolutional neural network that learns the similarity between sEMG signals in terms of the tremor severity. Labeled sEMG samples are used for jointly voting for the final results. Experiments on 147 PD patients demonstrate that our approach outperforms traditional methods by a significant margin. In addition, our approach is simple and has potentials in real applications.