## 4 Results and Discussions
### Emerging Digital Technologies
#### 4.1.5 Artificial Intelligence & Big Data Analytics  
AI and robotics are indispensable in modern industries, and operations without them are almost unimaginable today. AI-driven architecture for Industry 5.0 enhances manufacturing by integrating active learning, explainable AI, and simulated reality, prioritizing human-machine collaboration and safety [6; 35]. AI enables machines to learn from past data, adapt to new inputs, perform complex human-like tasks, and retain information for future optimization [52]. Integrating historical and real-time data enhances manufacturing decisions, leading to improved performance, safety, reliability, and sustainability of industrial systems [36]. Furthermore, ML and big data analytics are key to predicting and preventing equipment failure in production systems [37]. Big data analytics aggregates and analyzes diverse information to enable superior real-time decision-making compared to traditional methods [8], providing organizations with a competitive advantage [43]. The study by [38] identified IoT, cloud computing, AR, and big data as key technologies closely linked to safety management. Data acts as an essential connection between the physical and digital worlds, especially in virtual HRC scenarios, driving the HRC DT [46]. Data can be collected using  
Figure 11: Brief outline of the applications and functions of IoT, sensors, and motion capture in the OHS. various methods, such as sensors, data acquisition cards, and similar technologies. Human behavioral data can be collected efficiently via apps on smartphones or smartwatches, as well as through specialized wearable devices [76]. Effective data acquisition, including communication platforms, storage, and management, is essential for linking the physical and cyber domains, allowing feedback and decision support from the cyber world to the physical realm [15]. Wearable devices are compact electronic tools equipped with sensors and computational power that can be worn on various body parts to store diverse information [34; 72]. For instance, wearable sensors can monitor human physiological data like heart rate and blood pressure, while a myoelectric cuff analyzes electromyographic signals to assess fatigue [46]. Digital transformation using IoT and big data analytics in health and safety systems has improved employees' heart health, as seen in Shanghai's BYD Manufacturing Enterprise [37]. Big Data enhances the ability to analyze human behavior and predict errors, thereby improving safety. Additionally, these wearable devices offer significant advantages such as high precision, flexibility, and adaptability [28]. When integrated with DTs, AI enhances automation, such as visual object recognition and decision-making through data analysis, and enables systems to interpret operator behavior and environmental conditions to deliver personalized robotic assistance [22; 59]. For example, AI integrated with DTs can automate detection tasks, such as text extraction, damage assessment, inspection, and inventory management [59]. DTs, as real-time virtual models of physical assets, rely on AI and big data for diagnostics, forecasting, and optimization to inform decision-making and enhance performance [3]. Additionally, AI and data analytics are vital for identifying patterns in workplace accidents and illnesses, enabling companies to continuously improve their health and safety protocols [19]. Combined with AR, these technologies facilitate quick data exchange, proactively minimizing occupational risks during operational, maintenance, and complex tasks [18]. In summary, AI and big data analytics transform the way industrial systems forecast hazards, manage stress, and automate tasks. They are central to Industry 5.0, making modern manufacturing systems more intelligent, adaptive, and human-centric than before. AI and big data analytics play crucial roles in improving workplace safety by enabling advanced hazard prediction, recognizing worker behaviors and activities, and supporting informed decision-making.