Continuous Prediction of Human Joint Mechanics Using EMG Signals: A Review of Model-Based and Model-Free Approaches

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

“…Studies [4], [5] and [6] categorized patients' motion intention into biological and non-biological signals. Biological signals include Electroencephalography (EEG), Electromyography (EMG), Force Myography (FMG), and Mechanomyography (MMG), while non-biological signals consist of video, Inertial Measurement Units (IMU), and force sensors.…”

“…Current sEMG-based motion intention prediction research can be categorized into discrete classification and continuous regression. However, as discussed in the review [6], only 11.6% of studies from 1996-2017 focused on continuous regression, and the first review on sEMG-based continuous motion intention estimation was not published until 2019 [4]. Moreover, according to the prediction methods used in previous studies, sEMG-based continuous regression can be divided into modelbased (MB) and model-free (MF) approaches.…”

“…In contrast, DL can automatically extract advanced features from sEMG and utilize the neural network's potent fitting capacity to approximate the highly nonlinear relationship between features and motion intention, thereby avoiding the reliance on optimal feature sets based on empiricism similar to ML [5]. Although end-to-end MF methods are convenient to train and quick to deploy, their inherently 'black box' nature may overlook the potential causal relationships between input and output data, consequently struggling to generalize beyond the training data and risking overfitting [6], [7], [8]. In contrast, MB methods with inherent physiological causality can convert sEMG signals to muscle-tendon forces according to the neuralphysiological mechanisms of muscle activation and contraction dynamics, before predicting joint kinematics and dynamics from joint torques using musculoskeletal (MSK) geometry and Newtonian motion equations [6].…”

“…Although end-to-end MF methods are convenient to train and quick to deploy, their inherently 'black box' nature may overlook the potential causal relationships between input and output data, consequently struggling to generalize beyond the training data and risking overfitting [6], [7], [8]. In contrast, MB methods with inherent physiological causality can convert sEMG signals to muscle-tendon forces according to the neuralphysiological mechanisms of muscle activation and contraction dynamics, before predicting joint kinematics and dynamics from joint torques using musculoskeletal (MSK) geometry and Newtonian motion equations [6]. Additionally, most MB studies employed Hill-type MSK models with series elastic (SE), contractile (CE), parallel elastic (PE), and viscoelastic (VE) components.…”

Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setuping Setup

“…Therefore, literature underlined the necessity for improving user-driven control strategies in lower limb prostheses, to allow amputees to regain their natural locomotion abilities [1], [3], [6], [7]. Surface electromyography (sEMG) and mechanical signals serve as the main sources of information for the high-level control of prostheses and exoskeletons [4], [8]. Mechanical signals are deterministic signals that appear as a result of the motion, while the myoelectric activity has a stochastic nature and precedes the occurrence of the motion, thus reflecting the user intention [3], [9]- [12].…”