EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization
Muscle forces and joint moments estimated by electromyography (EMG)-driven musculoskeletal models are sensitive to the wrapping surface geometry defining muscle-tendon lengths and moment arms. Despite this sensitivity, wrapping surface properties are typically not personalized to subject movement da...
Saved in:
Main Authors: | Di Ao (Author), Geng Li (Author), Mohammad S. Shourijeh (Author), Carolynn Patten (Author), Benjamin J. Fregly (Author) |
---|---|
Format: | Book |
Published: |
IEEE,
2023-01-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning
by: Joseph Berman, et al.
Published: (2024) -
Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model
by: Xiaolei Xu, et al.
Published: (2023) -
Surface EMG in China: a report on the 2023 surface EMG symposium
by: Ping Zhou
Published: (2024) -
Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG
by: Jie Zhang, et al.
Published: (2023) -
Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal Models and LSTM Networks
by: Longbin Zhang, et al.
Published: (2023)