Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review

Abstract

Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.

Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage

“…The results obtained and their interpretation indicate that, from the perspective of previous research and working hypotheses, the use of AI in PV systems is a technology expected by designers, manufacturers and users (both private and institutional), and the current trends are in line with the Industry 4.0 paradigm, as well as Industry 5.0, in which human and natural needs are placed at the center of the production process, also emphasizing the sustainability of the supply chain [23][24][25]. However, the concept of Industry 5.0 is still vaguely defined in the specialist literature and there is still a lot of debate in the area of applications of the Industry 4.0 paradigm.…”

“…Directions for further research include multi-domain approaches, based on the potential of combined analyses (including instrumental ones), offering research results and new AI methods, techniques and tools to wider user groups of their potential users to ensure the translation of pilot studies into real world practice in PV grids and cooperating with the power systems. Only universality, integration and cooperation of solutions will allow the intended effect to be achieved [23][24][25].…”

“…Despite this, this is how the EU, governments, companies and other stakeholders want to improve employee wellbeing, reduce workrelated illnesses and raise awareness of the importance of motor fitness, fatigue, strain and effort. To date, such approaches have typically been developed in laboratories and only sometimes translated into field applications [23][24][25][26], which is an important aspect of the research to date. Effective AI-based modelling and optimization, appropriate task allocation, ergonomics and safety, based on data from motion and biological sensors, interfaces, Human Digital Twins and other simulation and visualization tools are becoming crucial [23][24][25][26].…”

“…To date, such approaches have typically been developed in laboratories and only sometimes translated into field applications [23][24][25][26], which is an important aspect of the research to date. Effective AI-based modelling and optimization, appropriate task allocation, ergonomics and safety, based on data from motion and biological sensors, interfaces, Human Digital Twins and other simulation and visualization tools are becoming crucial [23][24][25][26]. The key is harmonistic and synergistic cooperation of people and machines.…”

“…In developed economies, the importance of engineering competencies and skills, supply chain and efficient implementation of emerging technologies: AI, the Internet of Things and big data sets is growing. In emerging economies, training and skills assessment, organizational sustainability and structure as well as new technologies (digitization, internet of things) are key elements [23][24][25]. Currently, the prevailing view, supported by scientific research, is that an energy sector based on fossil fuels has a devastating impact on the natural environment, which makes it necessary to shift the main energy burden to alternative energy sources (including PV microgrids) [27].…”