Cargando…
Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature
Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, throu...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776838/ https://www.ncbi.nlm.nih.gov/pubmed/36553054 http://dx.doi.org/10.3390/diagnostics12123048 |
_version_ | 1784855957325479936 |
---|---|
author | Donisi, Leandro Cesarelli, Giuseppe Pisani, Noemi Ponsiglione, Alfonso Maria Ricciardi, Carlo Capodaglio, Edda |
author_facet | Donisi, Leandro Cesarelli, Giuseppe Pisani, Noemi Ponsiglione, Alfonso Maria Ricciardi, Carlo Capodaglio, Edda |
author_sort | Donisi, Leandro |
collection | PubMed |
description | Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility. |
format | Online Article Text |
id | pubmed-9776838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97768382022-12-23 Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature Donisi, Leandro Cesarelli, Giuseppe Pisani, Noemi Ponsiglione, Alfonso Maria Ricciardi, Carlo Capodaglio, Edda Diagnostics (Basel) Systematic Review Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility. MDPI 2022-12-05 /pmc/articles/PMC9776838/ /pubmed/36553054 http://dx.doi.org/10.3390/diagnostics12123048 Text en © 2022 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 | Systematic Review Donisi, Leandro Cesarelli, Giuseppe Pisani, Noemi Ponsiglione, Alfonso Maria Ricciardi, Carlo Capodaglio, Edda Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature |
title | Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature |
title_full | Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature |
title_fullStr | Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature |
title_full_unstemmed | Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature |
title_short | Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature |
title_sort | wearable sensors and artificial intelligence for physical ergonomics: a systematic review of literature |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776838/ https://www.ncbi.nlm.nih.gov/pubmed/36553054 http://dx.doi.org/10.3390/diagnostics12123048 |
work_keys_str_mv | AT donisileandro wearablesensorsandartificialintelligenceforphysicalergonomicsasystematicreviewofliterature AT cesarelligiuseppe wearablesensorsandartificialintelligenceforphysicalergonomicsasystematicreviewofliterature AT pisaninoemi wearablesensorsandartificialintelligenceforphysicalergonomicsasystematicreviewofliterature AT ponsiglionealfonsomaria wearablesensorsandartificialintelligenceforphysicalergonomicsasystematicreviewofliterature AT ricciardicarlo wearablesensorsandartificialintelligenceforphysicalergonomicsasystematicreviewofliterature AT capodaglioedda wearablesensorsandartificialintelligenceforphysicalergonomicsasystematicreviewofliterature |