Study And Testing Of The Use Of Wearable Devices For The Evaluation Of Ergonomic Parameters In The Workplace

Introduction

The increase in the average age of workers (Rodà & Sica, 2020) represents a significant risk factor in the field of Occupational Health and Safety (OSH), as it increases the odds of provoking work-related health problems, such as musculoskeletal diseases, stress, depression and anxiety.

This rapidly growing phenomenon can be partially prevented by applying ergonomic risk assessment methods, including NIOSH, OCRA, INAIL guidelines for work-related stress, etc.

However, due to the variability of tasks, these methodologies are not always sufficient to understand the real working conditions. From this lack arises the necessity to integrate innovative tools, such as wearable devices, with traditional evaluation methods (Papetti et al., 2018).

Wearable devices are a wide range of technological devices worn near and/or on the surface of the skin, that are able to detect, store and exchange data of different types, such as body movements, vital signs and environmental data, allowing in some cases an immediate biofeedback to the wearer (Düking et al., 2018).

In-depth studies, conducted by EU-OSHA (Report EU-OSHA, 2017) at European level and by INAIL at national level, emphasize the beneficial effects that digital technologies (including wearable devices) can have, especially on the delicate theme of health and safety at work.

Therefore, the objective of this study is to undertake a more collaborative approach between man and machine, which makes wearable devices a means not only of productive improvement, but also of worker protection.

Materials And Methods

The case study was carried out within a furniture manufacturing company and specifically in two activities: the drilling of the doors performed by numerical control machines (FA) and the assembly of the drawers (AC) (Figure 1).

The tools used for the case study were the following:

Camera for the evaluation of the ergonomic parameters of biomechanical overload of the spine and biomechanical overload of the upper limbs, obtained by NIOSH methods and OCRA Checklist (Waters et al., 2011, 2015; Colombini, 1998; Occhipinti & Colombini, 2004; UNI ISO 11228, 2009);

Wearable devices (heart strap and smart glasses) for the analysis of physiological parameters such as heart rate, heart rate, blink rate (De Rivecourt et al., 2008; Causse et al., 2010; Veltman & Gaillard, 1996; Bentivoglio et al., 2004; ISO 11226, 2019; Lindh et al., 2009);

NASA TLX questionnaire to determine the workload perceived by the worker (Hart, 2006).

By analyzing and monitoring some of these physiological factors through the use of wearable devices and integrating them with information on the characteristics of the work and the worker, it is possible to obtain information on the working conditions of the operator (physical load, mental workload and back posture) (Scafà et al., 2019) (Figure 2a). 

The monitoring through wearable devices was carried out during the work shift in order to significantly appreciate the progress of the physical and mental response of the workers. 

The collected data was then processed through algorithms (Hu.DO S.r.l. ‘s proprietary) and entered within the Oper.AI platform in order to obtain the aforementioned parameters (Figure 2b).

Parallel to the analysis of the data obtained from wearable devices, there were performed also the assessment of the biomechanical overload of the back (through the NIOSH – VLI method), the assessment of repetitive movements (through the checklist-OCRA method) and the assessment of the workload (through the NASA-TLX method), thanks to the contribution of the questionnaire and of the filming carried out during the case study.

Results And Discussion

The results of the analyzes obtained through “classic” methods and through wearable devices were compared and synergistically correlated with each other in order to obtain a general summary of the ergonomic conditions of the workstations (Figure 3).

In the first station observed (FA) a high risk of biomechanical overload of the spine was highlighted due both to the excessive weight of some wings lifted by the operator, and to the age of the worker, who is considered a “subject at risk”, because he is more than 45 years old. The posture of the operator’s back was good even though there were some incongruous movements that could have led to injury, if related to the lifting of a weight. On the other hand, no relevant risks were observed for overload of the upper limbs, due to repetitive movements, nor for physiological overload due to physical workload. In the end, the result of the NASA-TLX questionnaire concerning cognitive ergonomic revealed an important presence of mental workload, an aspect which was partly confirmed by the results obtained through wearable devices which, however, did not detect alarm values ​​for the health of the worker.

In the second station observed (AC), a very slight risk of biomechanical overload of the upper limbs was highlighted, mainly due to the high frequency of repetitive actions carried out during the assembly of the drawers.  The posture of the operator’s back was good even though there were some incongruous movements that could have led to injury, if related to the lifting of a weight. On the other hand, no relevant risks were observed for the biomechanical overload of the spine. Furthermore, even though the activity was purely physical, the set of actions and activities carried out during working hours were found to be suitable and proportionate to the characteristics of the operator. Lastly, the NASA-TLX questionnaire showed a normal amount of mental workload, an aspect also confirmed by the results obtained through wearable devices.

Despite the small number of data analyzed (only two workers subjected to the test), the case study confirmed that wearable devices are actually useful for the ergonomic risk assessment process. Thanks to the acquisition of physiological parameters concerning the workers, the application of these technologies has made the risk assessment more detailed and consequently more representative of the real working conditions.

If they are used and developed correctly, wearable devices will bring concrete benefits for both workers and companies, improving working conditions and increasing company productivity.

Acknowledgments

Special thanks go to Monica Pandolfi and Lorenzo Cavalieri, founders of the startup Hu.DO S.r.l., for providing the instrumentation (heart strap and glasses), the algorithms and the “Oper.AI” platform essential for processing the final results.

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