9 research outputs found
Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network
In this research, we present an innovative method known as a physics-informed
neural network (PINN) model to predict multi-joint kinematics using
electromyography (EMG) signals recorded from the muscles surrounding these
joints across various loads. The primary aim is to simultaneously predict both
the shoulder and elbow joint angles while executing elbow flexion-extension
(FE) movements, especially under varying load conditions. The PINN model is
constructed by combining a feed-forward Artificial Neural Network (ANN) with a
joint torque computation model. During the training process, the model utilizes
a custom loss function derived from an inverse dynamics joint torque
musculoskeletal model, along with a mean square angle loss. The training
dataset for the PINN model comprises EMG and time data collected from four
different subjects. To assess the model's performance, we conducted a
comparison between the predicted joint angles and experimental data using a
testing data set. The results demonstrated strong correlations of 58% to 83% in
joint angle prediction. The findings highlight the potential of incorporating
physical principles into the model, not only increasing its versatility but
also enhancing its accuracy. The findings could have significant implications
for the precise estimation of multi-joint kinematics in dynamic scenarios,
particularly concerning the advancement of human-machine interfaces (HMIs) for
exoskeletons and prosthetic control systems
Modeling and parametric optimization of 3D tendon-sheath actuator system for upper limb soft exosuit
This paper presents an analysis of parametric characterization of a motor
driven tendon-sheath actuator system for use in upper limb augmentation for
applications such as rehabilitation, therapy, and industrial automation. The
double tendon sheath system, which uses two sets of cables (agonist and
antagonist side) guided through a sheath, is considered to produce smooth and
natural-looking movements of the arm. The exoskeleton is equipped with a single
motor capable of controlling both the flexion and extension motions. One of the
key challenges in the implementation of a double tendon sheath system is the
possibility of slack in the tendon, which can impact the overall performance of
the system. To address this issue, a robust mathematical model is developed and
a comprehensive parametric study is carried out to determine the most effective
strategies for overcoming the problem of slack and improving the transmission.
The study suggests that incorporating a series spring into the system's tendon
leads to a universally applicable design, eliminating the need for individual
customization. The results also show that the slack in the tendon can be
effectively controlled by changing the pretension, spring constant, and size
and geometry of spool mounted on the axle of motor
IUPS Physiology Education Workshop series in India: organizational mechanics, outcomes, and lessons
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