Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 24; p. 11735
Main Authors Chae, Seungheon, Choi, Ahnryul, Jung, Hyunwoo, Kim, Tae Hyong, Kim, Kyungran, Mun, Joung Hwan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2021
Subjects
Actuators
Arthritis
Artificial intelligence
Bayesian analysis
Biomechanics
Classification
Control systems
Control systems design
Exoskeleton
Exoskeletons
Experiments
Extremities
Health risks
Hoisting
human motion analysis
insole system
Insoles
Joints (anatomy)
Kinematics
Learning algorithms
lifting task
lower body joint moment
Machine learning
Musculoskeletal diseases
Neural networks
Optimization
Posture
Sensors
work-related musculoskeletal disorders
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Summary:Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06~13.88% while the single LSTM showed 9.30~18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112411735