Electromyography (EMG) signal is one of the widely used biological signals for human motor
intention prediction, which is an essential element in human-robot collaboration systems …

Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …

Objective. Deep learning models can learn representations of data that extract useful
information in order to perform prediction without feature engineering. In this paper, an …

An important barrier to commercialization of pattern recognition myoelectric control of
prostheses is the lack of robustness to confounding factors such as electrode shift, skin …

Myoelectric control is filled with potential to significantly change human–robot interaction
due to the ability to non-invasively measure human motion intent. However, current control …

The exoskeleton robots for the lower limb can help meet the necessary hip joint force to
rehabilitate people with movement disorders. This paper proposes a deep learning strategy …

The evolution of deep learning techniques has been transformative as they have allowed
complex mappings to be trained between control inputs and outputs without the need for …

In this paper, we present a simultaneous and continuous kinematics estimation method for
multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous …

Continuous online prediction of human joints angles is a key point to improve the
performance of man-machine cooperative control. In this study, a framework of online …

In myoelectric control, simultaneous and proportional (SP) control of multiple degrees of
freedom (DOFs) can realize a high level of dexterity. This study proposed a new control …