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The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

Authors :
Alberto Ranavolo
Arash Ajoudani
Andrea Cherubini
Matteo Bianchi
Lars Fritzsche
Sergio Iavicoli
Massimo Sartori
Alessio Silvetti
Bram Vanderborght
Tiwana Varrecchia
Francesco Draicchio
Source :
Sensors, Vol 20, Iss 20, p 5750 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Due to the epochal changes introduced by “Industry 4.0”, it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human–robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative “direct instrumental evaluations” and “rating of standard methods”, allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers’ awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.

Details

Language :
English
ISSN :
14248220
Volume :
20
Issue :
20
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.0b67def037c647c7a10ad3f51df484c9
Document Type :
article
Full Text :
https://doi.org/10.3390/s20205750