## 1 Introduction  
Defining the biomechanic risk level produced by an applied force developed by upper limbs during task performance can be challenging due to several factors [1]. Firstly, the level of force exerted is measured to define which movement patterns lead to musculoskeletal disorders (MSDs) [2]. Secondly, which movement patterns create a greater risk of injury during repetitive tasks is identified [3]. Finally, the anthropometric characteristics of each worker are different; thus [4], the biomechanical stress could be different for two workers who are developing the same task. Addressing this issue in this paper, a Decision Support System (DSS) is proposed that contains a biomechanical fuzzy model for analysing the ergonomic risk level associated with upper limb movements. A case study to determine the ergonomic risk levels of 123 female workers across three automatic high-speed production lines was used for validating the fuzzy model. The Decision Support System (DSS) is an automated information system designed [5; 6] to enhance ergonomics' decision-making and improve organisational safety and health activities. It analyses and processes large amounts of biomechanic and anthropometric data, helping decision-makers to compile useful information [7] to classify the level of risk present in the workplace. This enables one to identify and solve work-related illnesses and make informed decisions to prevent them [8]. Risk refers to the effect of uncertainty related to understanding an event and its potential consequences [9; 10]. From a fuzzy point of view, the risk uncertainty is non-statistical; namely, it is not a random imprecision, and the vagueness is respect to its meaning [11], e.g., the properties of occupational low risk and high risk are different, both can produce injuries and work-related illnesses [12]--the difference lies in the severity [13; 14]. Therefore, to formulate a function for the fuzzy concept of risk, a membership function must be defined [15] to satisfy imprecise properties to varying the degree of risk. The membership function values are the unit interval [0.0, 1.0]. Namely, this function assigns a value between 0.0 and 1.0 to each individual in a universal set known as the Universe of Discourse \"S\", indicating the membership grade of these elements in the relevant set [16]. Thus, one has a binary relationship between a set and an object; we say the object is part of the set. In fuzzy set theory, set membership is expressed in degrees [17]. On the other hand, occupational biomechanics studies tasks performed by different individuals to define the characteristics of biomechanical systems at work [18; 19; 20], namely, the study of how workers interact with tools, machines, and materials that can lead to musculoskeletal disorders. In biomechanical systems, workers' bodies are viewed as a system of mechanical links [21] supported by statistical data that describe anthropometric relationships between the human body and the design of workplaces [22; 23]. Therefore, anthropometric data are the foundation for developing biomechanical models that predict the forces applied during task performance; consequently, they are used to support ergonomic risk evaluations. Measuring the ergonomic risk level from a biomechanical perspective in workplaces is essential for ensuring the health and safety of workers [20; 24; 25]. Unsafe tasks and environments can lead to occupational illnesses [26; 27]. Ergonomic evaluations that utilise fuzzy logic or a biomechanical approach have been discussed in the research literature. Some scholars have implemented a combination of anthropometrics and fuzzy logic focused on ergonomic designs; for example, [PERSON] [28] implemented a fuzzy method to design school furniture using anthropometric parameters. [PERSON] et al. [29] proposed a fuzzy logic approach to predict MSDSs, using MATLAB software and strain index to define the biomechanics of the task through 64 rules.