# Prevent Workers from Injuries in the Brewing Company via Using Digital Human Modelling Technology
## 1 Introduction  
Over one-third of workers in the U.S. labor force experienced musculoskeletal disorders (MSDs) [1]. MSDs in U.S. businesses cost USD 31 billion a year, and the highest percentage of cost is directed toward the material and stock workers [2]. According to the Pennsylvania Craft Brewers Association, there are nearly 500 breweries in Pennsylvania, and the number is increasing each year [3]. In 2020, there were over 7000 breweries in the US, and that number has grown by 300% from the last decade [4]. Therefore, breweries are fast-growing companies, and the safety of their workers is becoming a huge concern. MSDs are prevalent in the brewing industry because of the nature and type of tasks they perform within a workday [5]. From one report, more than 400 claims were identified. The lifting task (23.1%) was listed as one of the top three injury types, and the lower back (12.8%) was the most frequent anatomical area [6]. Given that the heavy lifting and repetitive tasks among the workers, including manually lifting empty half-barrel legs, which weigh approximately 14 kg, and full kegs weighing 72 kg, directly resulted in a significant increase in work-related MSDs [2, 5, 7]. The constant lifting, pulling, and pushing of these heavy weights and the high frequency and awkward postures typical of some tasks can also lead to a high risk of MSDs due to the excessive strain exerted on the lower back [2, 7]. Moreover, many of these breweries are small and scaled businesses; therefore, workers are facing the problem of repetitive work with no switching of activities [5]. Small businesses also have the issue of a potential deficit of materials, such as handling robotic equipment. Hence, much of the work is handled manually [7]. Numerous methods have been utilized to reduce the risks of injuries in brewery workers, such as physiotherapy, stretching, and strengthening exercises; informing workers of the postures and movements that can cause the injuries; and carrying out surveys. Physiotherapists and occupational therapists can help solve the non-neutral and awkward postures by providing education and training on correct ergonomics, such as demonstrating the proper way to lift heavy objects [8]. Free manual handling training, such as stretching and strengthening exercises, is being provided as a treatment option for reducing MSDs [9; 10]. The Brewers Association has also sponsored surveys where beer workers are able to convey commonplace injuries, safety program management, and the current state of safety in the Breweries [11]. These surveys help record the number of injuries every year so the breweries will be able to understand how the safety of the workers can be improved. However, there is no study that analyzed the lower back forces of the brewery workers for risk assessment. Digital human modeling (DHM) technology has attracted attention as it can be used to deliver realistic results to avoid injuries at workplaces [12; 13; 14]. The popular available DHM software packages in the market are JACK (Siemens PLM software, Plano, TX, USA) [15], SANTOS (SantosHuman Inc., Coralville, IA, USA) [16], DELMIA (Dassault Systemes, Paris, France) [17], SAMMIE (SAMMIE CAD Limited, Loughborough, UK) [18], Anybody (Anybody Technology, Aalborg, Denmark) [19], and OpenSim (Simbios, Stanford University, Stanford, CA, USA) [20]. DHM technology has been used to recognize acceptable postures in computer-related workplaces [21], enhance the productivity of the product by reducing the risk of MSDs [22], enable end-users to develop simulations and assess the injury risk in the manufacturing sector [13; 23], simulate different tasks performed at healthcare workplaces to evaluate the risk of MSDs by analysis of lower back injuries [24; 25; 26; 27; 28], evaluate the lower back compression and shear forces for materials handling tasks [29], assess the muscle activity and joint reaction force for physical strain reduction [30], and assess the joint and muscle forces by analyzing different walking speeds [31].