Intent Prediction of Multi-axial Ankle Motion Using Limited EMG Signals

Abstract

Background: In this study, different intent prediction strategies were explored with the objective of determining the best approach to predicting continuous multi-axial user motion based solely on surface EMG (electromyography) data. These strategies were explored as the first step to better facilitating control of a multi-axis transtibial powered prosthesis.Methods: Based on data acquired from gait experiments, different data sets, prediction approaches and classification algorithms were explored. The effect of varying EMG electrode positioning was also tested. EMG data measured from three lower leg muscles was the sole data type used for making intent predictions. The motions to be predicted were along both the sagittal plane (foot dorsiflexion and plantarflexion) and the frontal plane (foot eversion and inversion).Results: The deviation of EMG data from its optimal pattern led to a decrease in prediction accuracy of up to 23%. However, using features that were calculated based on a participant's specific walking pattern limited this loss of prediction accuracy as a result of EMG electrode placement. A decoupled data set, one wherein the terrain type was accounted for beforehand, yielded the highest intent prediction accuracy of 77.2%.Conclusions: The results of this study highlighted the challenges faced when using very limited EMG data to predict multi-axial ankle motion. They also indicated that approaches that are more user-centric by design could led to more accurate motion predictions, possibly enabling more intuitive control.

Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

“…Surface EMG signals is a non-invasive technique for measuring the electrical activity of muscle groups on the skin surface, which makes it a simple and straightforward method that allows the user to actively control the prosthesis (Takaiwa et al, 2011;Gregory and Ren, 2019;Wu et al, 2017). The basic principle of the human-machine interface based on surface EMG signals is to convert sEMG into controllable signals through algorithms such as machine learning.…”

Recognizing Continuous Multiple Degrees of Freedom Foot Movements With Inertial Sensors

“…where N = samples. We tried many features, such as the difference and mean absolute values (MAVs) [12], [17], and five time-domain features were chosen for minimal computational cost and memory consumption to be calculated from each sample of IMU signals. The features are defined as follows:…”

“…Although the Berkeley Lower Extremity Exoskeleton (BLEEX) featuring three DOFs at the ankle, only one DOF, the ankle plantar/dorsiflexion, is actuated [15], [16]. Furthermore, there is limited human motion mode recognition research on continuous foot gestures or multiaxial foot movements [1], [17]- [19]. Scott et al designed a study performing foot gesture recognition involving 2-DOF foot movement under static conditions [19].…”

“…Gregory and Ren tried different strategies to recognize continuous multiaxial anklefoot motions based on surface electromyography (sEMG) data. The motions to be predicted were in two DOFs, the sagittal plane (foot dorsiflexion and plantarflexion), and the frontal plane (foot eversion and inversion) [17].…”

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

“…Gregory and Ren [ 26 ] developed multi-class classifiers to predict continuous multi-axial user motion using surface EMG signals. The method collected the data based on gait experiments and applied the various FS techniques for predicting the motion of the patient along with the frontal plane and sagittal plane.…”