## 3 Research Methodology
### Data Analysis Strategy  
When analyzing the trend of the selected publications in the last ten years (see Figure 5), how more publications talk about the keywords that were used for the literature review  
\begin{table}
\begin{tabular}{l l} \hline Year & 2012-2022 \\ \hline \multirow{2}{*}{Document Type} & Article OR Review OR Book Chapter OR \\  & Conference paper \\ \hline Subject area & Engineering OR computer science, \\ \hline Language & English \\ \hline \end{tabular}
\end{table}
Table 3: Inclusion criteria for explorative literature review.  
is notorious; the foregoing indicates that the selected research gap is generating a lot of interest in recent years.  
A second reading was made of the titles and abstracts to obtain new insights from this analysis that allow us to know the most specific area of knowledge in which it is necessary to deepen to answer the research question more deeply and extensively. The insights obtained (see Table 4) in Keyword Group A can be grouped as solutions provided by Industry 4.0 [1, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57] since they are tools like AI, IoT, and WSN technologies to increase the productivity and efficiency of a predictive maintenance system, while the insights obtained from Keyword Groups B and C are tools that contribute more to Industry 5.0 [8, 11, 24], whose main objectives are a _human-centered_[44, 34, 49, 50, 51, 52, 53, 54, 55, 56, 57] approach, and _resilience_[69, 72, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Keywords** & **Papers** & **Cluster** \\ \hline \multirow{8}{*}{**A:** _maintenance, smart, intelligent_.} & Artificial Intelligence (AI), predictive, Machine learning, Reinforcement learning, Assessment, Manufacturing control, Cyber-physical Systems, Industry 4.0, Knowledge-based approach, Prognosis, Prognostics and Health Management (PHM), Internet of Things (IoT), Industrial Internet of Things Maintenance policies, simulation, Wireless Sensor Networks (WSN), Machine tool, Maintenance 4.0 & \\ \hline \multirow{8}{*}{**B:** _human, machine, HMI, interaction, interface, human-in-the-loop_} & Work assistance, Augmented Reality (AR), Extended Reality (XR), Virtual Reality (VR), Situational Awareness, Digital Twins, Condition-based Maintenance (CBM), Human-centered approach, Collaborative systems, Industry 5.0, Sensemaking approach, _Supervisory control, and data acquisition_ (SCADA), human-AI interaction, Human-robot Collaboration. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Insights from the literature scoping review.  
Figure 5: Selected papers by year of publication.  
While Industry 4.0 is technology-driven and productivity-centered [33], Industry 5.0 is considered value-driven and human-centered. Industry 5.0 does not replace Industry 4.0 but enhances it, broadening its horizons and adapting it to the new needs of continuous changes [106].  
This search in the literature has served to elaborate the hypothesis: if physical asset maintenance had the human-in-the-loop, it would be possible to improve the resilience of physical assets. To make the proposal shown in the next section, the Keywords obtained in Table 4 were deepened by surveying experts in industrial maintenance forums. This publication is supported by a previous experiment carried out with IWSNs [107] that validates the detection part of the proposed Maintenance 5.0 framework. The results of this research are presented below.