Methods for assessing exposure to biomechanical factors: what guidelines for the design and redesign of workplaces and tasks

M.P. Cavatorta | Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

Introduction. Technological innovation is transforming work environments and activities, expanding the concept of human-machine interaction with the introduction of collaborative robotics and wearable assistive devices like exoskeletons.¹ Technological innovation has also introduced new tools for assessing exposure to biomechanical factors: wearable sensors can provide useful insights into the risk of biomechanical overload in an activity by monitoring the worker’s motor behavior, postures assumed, and muscle engagement in real-time.² Mobile device applications based on machine learning algorithms are now used to calculate synthetic risk indices and provide immediate feedback to workers.³

Objectives. Analyze the insights that methods for assessing exposure to biomechanical factors can provide in the current context of rapid technological evolution, particularly for the design and redesign of a task and a workstation.

Methods. Starting from technical standards, some of which has been recently revised, and publications by public health and safety authorities, some applications of standard methods recently published in the scientific literature are analyzed to exemplify the impact of technological developments on new ways of working and measuring exposure to biomechanical factors, and to reflect on what guidelines for the design and redesign of work tasks and activities can be derived from these methods.

Results and Conclusions. In an era of great technological innovations that are transforming work environments, adopting a holistic approach to risk assessment is becoming increasingly relevant, as is recognizing the multifactorial nature of musculoskeletal disorders and the specificity of each worker with respect to categories of risk exposure defined for the working population.

Recent standard revisions emphasize the importance of preserving the informative and in-depth role of risk assessment, suggesting the adoption of a gradual approach that begins with a preliminary analysis aimed at examining the various risk factors and is not limited to the calculation of a synthetic risk index. In this regard, as highlighted by the International Labour Organization (ILO) report,¹ it is fundamental that innovative technologies and digital applications do not replace, but rather complement, the assessment of ergonomists, supporting the identification and reduction of risk factors and worker exposure.

A data-driven approach to risk assessment and machine learning algorithms can be very relevant from a preventive standpoint, providing useful information for workstation design and supporting companies in taking a proactive approach to ergonomics. The large amount of data that can be obtained today from sensors, especially in laboratories and on individual workers, provides valuable support for increasing knowledge and awareness of risks, including those related to emerging technologies. Alongside its informative and in-depth role, it will be crucial to maintain and increase the effectiveness of risk assessment in contexts of continuous technological evolution.