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Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling

The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture en...

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Autores principales: Giannini, Paolo, Bassani, Giulia, Avizzano, Carlo Alberto, Filippeschi, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412376/
https://www.ncbi.nlm.nih.gov/pubmed/32664523
http://dx.doi.org/10.3390/s20143877
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author Giannini, Paolo
Bassani, Giulia
Avizzano, Carlo Alberto
Filippeschi, Alessandro
author_facet Giannini, Paolo
Bassani, Giulia
Avizzano, Carlo Alberto
Filippeschi, Alessandro
author_sort Giannini, Paolo
collection PubMed
description The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis.
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spelling pubmed-74123762020-08-26 Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling Giannini, Paolo Bassani, Giulia Avizzano, Carlo Alberto Filippeschi, Alessandro Sensors (Basel) Article The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis. MDPI 2020-07-11 /pmc/articles/PMC7412376/ /pubmed/32664523 http://dx.doi.org/10.3390/s20143877 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Giannini, Paolo
Bassani, Giulia
Avizzano, Carlo Alberto
Filippeschi, Alessandro
Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling
title Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling
title_full Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling
title_fullStr Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling
title_full_unstemmed Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling
title_short Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling
title_sort wearable sensor network for biomechanical overload assessment in manual material handling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412376/
https://www.ncbi.nlm.nih.gov/pubmed/32664523
http://dx.doi.org/10.3390/s20143877
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