The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU signals for various activities. These data-driven mod...

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Bibliographic Details
Main Authors: Mohsen Sharifi Renani, Abigail M. Eustace, Casey A. Myers, Chadd W. Clary
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Sensors
Subjects:
deep learning
inertial sensors
joint kinematics
synthetic data
augmentation
Online Access:https://www.mdpi.com/1424-8220/21/17/5876

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https://www.mdpi.com/1424-8220/21/17/5876

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