Real-Time Classification of Electromyographic Signals for Robotic Control

Advances in bioengineering have led to increasingly sophisticated prosthetic devices for amputees and paralyzed individuals. Control of such devices necessitates real-time classification of biosignals, e.g., electromyographic (EMG) signals recorded from intact muscles. In this paper, we show that a 4-degrees-of-freedom robotic arm can be controlled in real-time using non-invasive surface EMG signals recorded from the forearm. The innovative features of our system include a physiologically-informed selection of forearm muscles for recording EMG signals, intelligent choice of hand gestures for easy classification, and fast, simple feature extraction from EMG signals. Our selection of gestures is meant to intuitively map to appropriate degrees of freedom in the robotic arm. These design decisions allow us to build fast accurate classifiers online, and control a 4-DOF robotic arm in real-time. In a study involving 3 subjects, we achieved accuracies of 92-98% on an 8-class classification problem using linear SVMs. These classifiers can be learned on-line in under 10 minutes, including data collection and training. Our study also analyzes the issues and tradeoffs involved in designing schemes for robotic control using EMG. Finally, we present details of online experiments where subjects successfully solved tasks of varying complexity using EMG to control the robotic arm.