Cargando…

Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control

The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end,...

Descripción completa

Detalles Bibliográficos
Autores principales: Tabasi, Ali, Lazzaroni, Maria, Brouwer, Niels P., Kingma, Idsart, van Dijk, Wietse, de Looze, Michiel P., Toxiri, Stefano, Ortiz, Jesús, van Dieën, Jaap H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747305/
https://www.ncbi.nlm.nih.gov/pubmed/35009627
http://dx.doi.org/10.3390/s22010087
_version_ 1784630802593611776
author Tabasi, Ali
Lazzaroni, Maria
Brouwer, Niels P.
Kingma, Idsart
van Dijk, Wietse
de Looze, Michiel P.
Toxiri, Stefano
Ortiz, Jesús
van Dieën, Jaap H.
author_facet Tabasi, Ali
Lazzaroni, Maria
Brouwer, Niels P.
Kingma, Idsart
van Dijk, Wietse
de Looze, Michiel P.
Toxiri, Stefano
Ortiz, Jesús
van Dieën, Jaap H.
author_sort Tabasi, Ali
collection PubMed
description The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 nm (9% of peak moment) and increased up to 35.0 nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 nm (10% of peak moment), and not substantially different than that of the extensive calibration.
format Online
Article
Text
id pubmed-8747305
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-87473052022-01-11 Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control Tabasi, Ali Lazzaroni, Maria Brouwer, Niels P. Kingma, Idsart van Dijk, Wietse de Looze, Michiel P. Toxiri, Stefano Ortiz, Jesús van Dieën, Jaap H. Sensors (Basel) Article The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 nm (9% of peak moment) and increased up to 35.0 nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 nm (10% of peak moment), and not substantially different than that of the extensive calibration. MDPI 2021-12-23 /pmc/articles/PMC8747305/ /pubmed/35009627 http://dx.doi.org/10.3390/s22010087 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tabasi, Ali
Lazzaroni, Maria
Brouwer, Niels P.
Kingma, Idsart
van Dijk, Wietse
de Looze, Michiel P.
Toxiri, Stefano
Ortiz, Jesús
van Dieën, Jaap H.
Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control
title Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control
title_full Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control
title_fullStr Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control
title_full_unstemmed Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control
title_short Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control
title_sort optimizing calibration procedure to train a regression-based prediction model of actively generated lumbar muscle moments for exoskeleton control
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747305/
https://www.ncbi.nlm.nih.gov/pubmed/35009627
http://dx.doi.org/10.3390/s22010087
work_keys_str_mv AT tabasiali optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT lazzaronimaria optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT brouwernielsp optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT kingmaidsart optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT vandijkwietse optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT deloozemichielp optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT toxiristefano optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT ortizjesus optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol
AT vandieenjaaph optimizingcalibrationproceduretotrainaregressionbasedpredictionmodelofactivelygeneratedlumbarmusclemomentsforexoskeletoncontrol