Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Abstract

This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis

“…Even on a flat ground, the metabolic energy of the patient's body is 60% higher than that of the healthy subject. Therefore, to solve the limitations of this mechanical prosthesis, the researchers started the research on the intelligent power lower limb prosthesis [ 5 ]. The movement of the lower limbs is a complex movement, but the movement of the lower limbs also has certain regularity and periodicity.…”

Recurrent Convolutional Neural Networks as an Approach to Position-Aware Myoelectric Prosthesis Control

“…Electromyography (EMG) is currently the most commonly used input source for prosthesis control [2], whereby EMG signals generated by muscle contractions in a user's residual limb are captured by electrodes embedded in a device socket. Despite yielding reliable device movements in research environments, precise decoding of movement intent from EMG signals can be unreliable when a wide range of limb positions are introduced by users during daily activities [3].…”

Deep Cross-User Models Reduce the Training Burden in Myoelectric Control

“…• The TDPSD feature set was developed to increase robustness to limb position and contraction intensity by adding a combination of statistical moments and non-linear scaling; the 0th, 2nd, and 4th order statistical moments, sparseness, irregularity factor, and waveform length ratio (Khushaba et al, 2014). This feature set was originally proposed to improve robustness to limb position and contraction intensity confounding factors; however, TDPSD has since been shown to also outperform the TD feature set on controlled gesture recognition studies (Campbell et al, 2020c). • Low sampling frequency (LSF) feature sets have been proposed…”

“…Following this procedure, high gesture recognition accuracy can be obtained under controlled settings, as was demonstrated by Côté-Allard et al, who obtained 98.3% for a 7 class system (Côté-Allard et al, 2019 ). When systems are used in real-world conditions, however, performance tends to degrade substantially due to confounding factors, such as limb position, contraction intensity, and electrode shift (Scheme and Englehart, 2011 ; Campbell et al, 2020c ; Phinyomark et al, 2020 ).…”