Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control Article Swipe
Ali Tabasi
,
Maria Lazzaroni
,
Niels P. Brouwer
,
Idsart Kingma
,
Wietse van Dijk
,
M.P. de Looze
,
Stefano Toxiri
,
Jesús Ortiz
,
Jaap H. van Dieën
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s22010087
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s22010087
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22010087
- https://www.mdpi.com/1424-8220/22/1/87/pdf?version=1640335078
- OA Status
- gold
- Cited By
- 8
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200482555
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4200482555Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22010087Digital Object Identifier
- Title
-
Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton ControlWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-23Full publication date if available
- Authors
-
Ali Tabasi, Maria Lazzaroni, Niels P. Brouwer, Idsart Kingma, Wietse van Dijk, M.P. de Looze, Stefano Toxiri, Jesús Ortiz, Jaap H. van DieënList of authors in order
- Landing page
-
https://doi.org/10.3390/s22010087Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/1/87/pdf?version=1640335078Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/1/87/pdf?version=1640335078Direct OA link when available
- Concepts
-
Calibration, Moment (physics), Exoskeleton, Mean squared error, Workspace, Computer science, Simulation, Set (abstract data type), Artificial intelligence, Mathematics, Statistics, Robot, Physics, Classical mechanics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.characteristics, | 180 |
| abstract_inverted_index.pulling/pushing. | 122 |
| abstract_inverted_index.regression-based | 45 |
| abstract_inverted_index.root-mean-square | 124 |
| abstract_inverted_index.subject-specific | 68 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5046621757 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I865915315 |
| citation_normalized_percentile.value | 0.78466904 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |