A Data-Driven Framework to Model Physical Fatigue in Industrial Environments Using Wearable Technologies

Industry 4.0 is the tendency towards automation and data exchange in manufacturing and the process sector. However, many manual material handling and repetitive operations can still cause the operators fatigue or exhaustion. Once the operator experiences physical fatigue, their performance decreases. The consequences may result in reduced production quality and efficiency and increased operational human errors that could give rise to incidents and accidents. Over time, physical fatigue can result in more adverse effects for the operators, such as Chronic Fatigue Syndrome (CFS) and Work-related Musculoskeletal Disorders (WSMD). For this reason, from an occupational health and safety point of view, the operator’s hysical fatigue must be managed. The increasing availability of wearable devices combined with health information can provide real-time measuring and monitoring of physical fatigue in the operational environment while minimally influencing the primary job. This paper presents a physiological signal-based approach using a non-intrusive wristband for continuous health monitoring to predict physical fatigue in industrial-related tasks. These data are used as input to classification algorithms to detect physical fatigue. Accurate and real-time physical fatigue detection helps to improve operator safety and prevent work accidents. Future work will deploy the model in a real-world environment in the industry

Exploring the Impact of Repetitive Exercise on Physical Fatigue: a Study of Industrial Task Simulation in a Controlled Fitness Setting

The human element plays a crucial role in industries, particularly manufacturing and process. Effective management of human factors improves product quality and efficiency while reducing the risk of operational errors that may result in incidents or accidents. Even with the advancements in technology and automation, repetitive manual work and challenging tasks still strain workers, resulting in physical fatigue and increasing the chances of errors, production delays, and potential accidents. For this reason, the significance of physical fatigue on industry operations and employee well-being cannot be overstated. The implementation of wearable technology to handle physical fatigue in the industry is a cutting-edge solution. Wearable devices provide real-time data on worker physical fatigue levels, allowing employers to respond quickly to any changes in conditions that may increase the risk of accidents in the wokplace. This paper aims to present a framework for a fitness setting that simulates the repetitive movements commonly seen in the industrial sector. The data collected in this controlled environment will be used to train a physical fatigue classification model, which can then be applied in real-world industrial facilities to advance operator safety and reduce the risk of workplace accidents in future applications

Optimizing Human Performance to Enhance Safety: A Case Study in an Automotive Plant

Human factors play a relevant role in the dynamic work environments of the manufacturing sector in terms of production efficiency, safety, and sustainable performance. This is particularly relevant in assembly lines where humans are widely employed alongside automated and robotic agents. In this situation, operators’ ability to adapt to different levels of task complexity and variability in each workstation has a strong impact on the safety, reliability, and efficiency of the overall production process. This paper presents an application of a theoretical and empirical method used to assess the matching of different workers to various workstations based on a quantified comparison between the workload associated with the tasks and the human capability of the workers that can rotate among them. The approach allowed for the development of an algorithm designed to operationalise indicators for workload and task complexity requirements, considering the skills and capabilities of individual operators. This led to the creation of human performance (HP) indices. The HP indices were utilized to ensure a good match between requirements and capabilities, aiming to minimise the probability of human error and injuries. The developed and customised model demonstrated encouraging results in the specific case studies where it was applied but also offers a generalizable approach that can extend to other contexts and situations where job rotations can benefit from effectively matching operators to suitable task requirements

Revising the “Ability Corners” Approach: A New Strategy to Assessing Human Capabilities in Industrial Domains

Human capabilities refer to an individual’s innate and acquired abilities that enable them to complete a given task. These capabilities contain physical, mental, and cognitive skills. In an industrial environment, the complexity and nature of duties vary, and different jobs require different levels and types of human capabilities. For example, in an assembly line, a task that demands assembling small and fragile parts would require a high level of manual skill and precision. Understanding the human capabilities necessary for a job and matching them with the worker’s capabilities is crucial for designing and implementing tasks in industrial settings. The term “ability corners” describes equipment (hardware and software) for evaluating and measuring human capabilities in industrial workplaces. The results of these tests are used to match workers with the specific abilities needed for a particular workstation. This study proposes improving the “ability corners” by addressing some limitations, such as the insufficient number of tests to assess human capabilities and the lack of consideration for workers’ motivation, personality traits, and other factors that might affect their performance on the task. Furthermore, the study in which they were adopted does not consider the dynamic nature of assembly line work or the possible changes in workers’ capabilities over time due to factors such as experience, training, or fatigue. The present revision aims to enhance the accuracy and effectiveness of the “ability corners” approach by integrating new techniques, devices, and benchmarks into the current method to guarantee that the worker is well-suited for the job and can execute it safel