Decoding Multi-Finger Motions and Grasp Types with Grasp-Specific Models and Lightmyography Based Muscle-Machine Interfaces

Abstract

Efficiently decoding human movement and/or intention is essential for controlling advanced prosthetic and robotic systems. Various muscle-machine interfaces have been researched for this purpose, including electromyography and lightmyography based interfaces. However, the decoding effectiveness of lightmyography signals for multi-finger hand motions remains insufficiently explored. This study investigates the decoding of human multi-finger movements using different machine learning methods. Lightmyography and finger motion data were collected from six participants grasping five common objects. Data were preprocessed using the sliding window method and decoded using three machine learning algorithms: random forest, convolutional neural networks, and multi-layer perceptron. Moreover, models were trained in a grasp-specific manner increasing decoding accuracy. Finally, statistical analysis demonstrated that the random forest model significantly outperformed the other methods, establishing it as the most suitable technique for decoding multi-finger motions from lightmyography signals.