## 2 Literature Review
### Synthesis and Rationale for the Proposed MHMM Framework  
This section synthesizes the literature review's findings, identifying critical gaps in existing proficiency assessment models and providing a rationale for the proposed Multimodal Hidden Markov Model (MHMM) as a dynamic, multimodal solution for Industry 5.0. The examination of existing empirical work on human proficiency assessment reveals a consistent pattern: No current model successfully integrates physiological, behavioral, and subjective data within a dynamic temporal framework specifically designed for the real-time classification of human proficiency. To address this gap, we propose the Multimodal Hidden Markov Model (MHMM) as a framework with distinct advantages uniquely suited to address this challenge:  
* Superior Temporal Modeling: Unlike static classifiers, such as Artificial Neural Networks (ANNs) and k-Nearest Neighbors (KNN), which consider each data point independently, the MHMM is built to model sequences of proficiency states through \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Model** & **Data Modalities** & **Approach** & **Key Contributions** & **Limitations/Gaps** \\ \hline
[20] & HMM & Behavioral (real-time-strategy-game logs: actions, APM, MMR) & Unimodal & \
\begin{tabular}{c} Differentiated novice/expert \\ status in human-automation tasks \\ by modeling interaction patterns \\ \end{tabular} &
\begin{tabular}{c} Lacks physiological and \\ subjective data, limiting \\ cognitive load insights \\ \end{tabular} \\ \hline
[61] & HMM & \begin{tabular}{c} Multimodal (Logs, \\ eye tracking, EEG, \\ wristband, facial \\ expressions) \\ \end{tabular} & Multimodal & \
\begin{tabular}{c} Classified learners’ effort in \\ adaptive assessments to predict \\ future effort levels \\ \end{tabular} &
\begin{tabular}{c} Focuses on classifying \\ learner effort rather than \\ directly assessing \\ proficiency \\ \end{tabular} \\ \hline
[27] & HMM & Physiological (HRV) & Unimodal & \
\begin{tabular}{c} Assessed vigilance levels during \\ work tasks using HRV data \\ \end{tabular} &
\begin{tabular}{c} Relics on single modality \\ (HRV), missing broader \\ complexity of user state \\ \end{tabular} \\ \hline
[62] & EMHMM & \begin{tabular}{c} Behavioral (Eye \\ tracking) \\ \end{tabular} & Unimodal & \
\begin{tabular}{c} Predicted reading performance by \\ analyzing eye movement patterns \\ \end{tabular} &
\begin{tabular}{c} Limited to eye tracking, \\ physiological/subjective \\ metrics \\ \end{tabular} \\ \hline
[63] & HMM & \
\begin{tabular}{c} Behavioral (facial \\ video/key-point \\ features) \\ \end{tabular} & Unimodal & Identified driving fatigue &
\begin{tabular}{c} Lacks physiological (e.g., \\ EEG, HRV) and subjective \\ workload metrics, so \\ cognitive-state coverage is \\ incomplete. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of HMM-based proficiency assessment models. its transition matrix. This allows it to track the evolution of proficiency, capturing learning curves and fatigue onset. Research on vigilance and driving fatigue underscores the importance of such capabilities for accurate assessments of human performance, showing that dynamic modeling is key in tracking changes in performance over time rather than relying on static assessments [27]. * Increased Robustness: By relying on multiple data streams, the MHMM provides state estimation that is more resilient to noise or artifacts in any single channel. For example, if HRV data are temporarily unreliable, the model can still utilize TCT and subjective data from validated measures, such as the NASA-TLX, to maintain stable and accurate proficiency assessments. This resilience to data quality fluctuations is supported by literature that recognizes the importance of integrating multimodal data to enhance the robustness of assessments in complex environments. * Predictive and Diagnostic Power: Beyond classification, the transition probabilities inherent in the MHMM enable the prediction of proficiency changes.