Lower Body Joint Angle Prediction Using Machine Learning and Applied Biomechanical Inverse Dynamics

Extreme angles in lower body joints may adversely increase the risk of injury to joints. These injuries are common in the workplace and cause persistent pain and significant financial losses to people and companies. The purpose of this study was to predict lower body joint angles from the ankle to t...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 1; s. 228
Hlavní autoři: Choffin, Zachary, Jeong, Nathan, Callihan, Michael, Sazonov, Edward, Jeong, Seongcheol
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 26.12.2022
MDPI
Témata:
Accuracy
Ankle
Biomechanics
Calibration
Cellulose acetate
foot sensor
Human mechanics
inertial measurement unit
Injuries
joint angle detection
lower body
Machine learning
Motion capture
Physiological aspects
Prevention
Sensors
Shoes & boots
Software
Workers
Wounds and injuries
United States
United States > US
inertial measurement unit
joint angle detection
lower body
machine learning
foot sensor
ISSN:1424-8220, 1424-8220
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Shrnutí:Extreme angles in lower body joints may adversely increase the risk of injury to joints. These injuries are common in the workplace and cause persistent pain and significant financial losses to people and companies. The purpose of this study was to predict lower body joint angles from the ankle to the lumbosacral joint (L5S1) by measuring plantar pressures in shoes. Joint angle prediction was aided by a designed footwear sensor consisting of six force-sensing resistors (FSR) and a microcontroller fitted with Bluetooth LE sensors. An Xsens motion capture system was utilized as a ground truth validation measuring 3D joint angles. Thirty-seven human subjects were tested squatting in an IRB-approved study. The Gaussian Process Regression (GPR) linear regression algorithm was used to create a progressive model that predicted the angles of ankle, knee, hip, and L5S1. The footwear sensor showed a promising root mean square error (RMSE) for each joint. The L5S1 angle was predicted to be RMSE of 0.21° for the X-axis and 0.22° for the Y-axis, respectively. This result confirmed that the proposed plantar sensor system had the capability to predict and monitor lower body joint angles for potential injury prevention and training of occupational workers.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23010228