EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot

Abstract

(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

Integrating intention-based systems in human-robot interaction: a scoping review of sensors, algorithms, and trust

“…In Feleke et al (2021) , a recurrent fuzzy neural network (RFNN) is proposed to map sEMG signals to 3D hand positions without considering joint movements. The aim is to predict human motor intention for robotic applications.…”

The influence of smoothness and speed of stand-to-sit movement on joint kinematics, kinetics, and muscle activation patterns

“…Pre-processing of sEMG signal: Any DC offset was first eliminated using the “detrend” function in MATLAB. Then, a median filter was applied to the signal to remove noise ( Feleke et al, 2021 ), followed by the application of a 20–450-Hz bandpass filter to extract the frequency range where muscular energy is concentrated ( Altimari et al, 2012 ).…”

Novel near E-Field Topography Sensor for Human–Machine Interfacing in Robotic Applications

“…Although the accuracy achieved in this task can be high, it is limited by the serial single-degree-of-freedom nature of the model over a discrete output space. Some works use these sensors and the HMI differently and use these sensors for human–robot interactions by considering the 3D position of the hand as the output [ 29 ]. Other works seek to address the serial single-degree-of-freedom issue by considering individual degrees of freedom (DoFs) independently through regression for continuous motion [ 14 , 30 ].…”