Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network

Frontiers in Neuroscience

卷:11

Rosa H. M. Chan¹  Beth Jelfs¹  Chung Tin²  Xiaolong Zhai⁴ 

[1] Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong KongHong Kong, Hong Kong;

[2] Centre for Robotics and Automation, City University of Hong KongHong Kong, Hong Kong;

[3] Department of Electronic Engineering, City University of Hong KongHong Kong, Hong Kong;

[4] Department of Mechanical and Biomedical Engineering, City University of Hong KongHong Kong, Hong Kong;

关键词: myoelectric control;    non-stationary EMG;    classification;    hand gesture;    pattern recognition;    convolutional neural network;   

DOI  :  10.3389/fnins.2017.00379

来源: DOAJ

【 摘 要 】

Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

【 授权许可】

Unknown