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  • From Simulation to Reality: Predicting Torque with Fatigue . . .
    Using the simulated dataset, we pre-trained a long short-term memory neural network (LSTM) to regress fatiguing elbow flexion torque from muscle activations We concatenated this pre-trained LSTM with a feedforward architecture, and fine-tuned the model on recorded muscle activations and static features to predict elbow flexion torques
  • Estimation of joint torque in dynamic activities using . . .
    Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound We first
  • Real-time torque and joint angle estimation using . . . - Springer
    Experimental validation was conducted using both real-time sEMG data and publicly available datasets The proposed model exhibited strong performance, attaining a root mean squared error (RMSE) of 0 87 Nm for torque estimation and 2 5° for joint angle prediction during slow flexion–extension tasks
  • Estimation of elbow flexion torque using equilibrium . . .
    This study utilized a random forest regression (RFR) model to estimate elbow flexion torque using mechanomyography (MMG) signals recorded during electrical stimulation of the biceps brachii (BB) muscle in 36 right-handed healthy subjects
  • A hill-based EMG-driven model to estimate elbow torque during . . .
    Six men and women participated in the current study, performing fully elbow flexion and extension for no and maximum load Surface EMG electrodes were placed to detect the activity of muscles in order to be used in the musculoskeletal model
  • Experimental Study on Time Series Analysis of Lower Limb . . .
    joint datasets (x×8×2) encompass bilateral hip adduction, hip flexion, knee flexion, and ankle flexion (4 joints per leg × 2 legs), with the final dimension quantifying joint angles (°) and torque (Nm) The types of feature normalization and extraction are detailed in Figure 2
  • Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal . . .
    In this paper, we estimated ankle dorsiflexion plantarflexion, knee flexion extension, hip flexion extension, and hip abduction adduction torques from electromyography (EMG) and kinematics during daily activities using neuromusculoskeletal (NMS) models and long short-term memory (LSTM) networks





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