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

By Xiaolong Zhai, Beth Jelfs, Rosa H. M. Chan, Chung Tin. Frontiers in Neuroscience, 2017

349 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/90cc542334019cd4925645a5e6023bb944ad3bbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Daohui Zhang
2. Xingang Zhao
3. Dezhen Xiong
4. Yiwen Zhao

In Cognitive Neuroscience

1. Oliver Faust
2. Yuki Hagiwara

Citations in the corpus

• Deep learning for healthcare applications based on physiological signals: A review (2018, background): “, 2017 [61] Neuroprosthesis control CNN NinaPro Database (DB)2&3 [62] accuracy: 83%”
• Deep Learning for EMG-based Human-Machine Interaction: A Review (2021, methodology): “It has shown better performance than machine learning methods with feature engineering, as shown for movement classification tasks in [79], [82], [89], [90], for angle regression in [51], [56], [70], for force prediction in [123], and so on.”