On EMG Based Dexterous Robotic Telemanipulation: Assessing Machine Learning Techniques, Feature Extraction Methods, and Shared Control Schemes

Abstract

Electromyography (EMG) signals are commonly used for the development of Muscle Machine Interfaces. EMG-based solutions provide intuitive and often hand-free control in a wide range of applications that range from the decoding of human intention in classification tasks to the continuous decoding of human motion employing regression models. In this work, we compare various machine learning and feature extraction methods for the creation of EMG based control frameworks for dexterous robotic telemanipulation. Various models are needed that can decode dexterous, in-hand manipulation motions and perform hand gesture classification in real-time. Three different machine learning methods and eight different time-domain features were evaluated and compared. The performance of the models was evaluated in terms of accuracy and time required to predict a data sample. The model that presented the best performance and prediction time trade-off was used for executing in real-time a telemanipulation task with the New Dexterity Autonomous Robotic Assistance (ARoA) platform (a humanoid robot). Various experiments have been conducted to experimentally validate the efficiency of the proposed methods. The robotic system is shown to successfully complete a series of tasks autonomously as well as to efficiently execute tasks in a shared control manner.

Keywords

• Muscle-machine interfaces
• electromyography
• shared control
• intention decoding
• telemanipulation
• machine learning