# Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review
## 5 Discussion  
After studying the journal articles and conference papers selected for this state-of-the-art review, in this section, a discussion is open to answer the original research questions presented by this work and for establishing possible areas for further research. It is worth mentioning that during this state-of-the-art review, three \"literature reviews\" were found in the scientific body of knowledge on _learning curves_. These reviews were excluded from the results presented in Section 4 since their focus was more on providing a retrospective analysis of the learning curves literary corpus rather than addressing its current research \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article/Paper** & **Data Collection** & **Monitored** & **Software** & **Application** & **Focus** \\ \hline Section 4.2.1 & Motion & Joint Position, & & Laboratory & \\ [PERSON] et al. (2019)[31] & Tracking & Picking Duration and Frequency, and Travel Distance & Human Factor & Production Setting & Ergonomics, and \\  & Depth cameras & Picking Duration and Frequency, and Travel Distance & (Proprietary) & for Picking & Workstation Design \\ \hline Section 4.2.2 & Surface & EMG & & Manual Assembly & Fatigue \\ [PERSON] et al. (2019)[32] & Electromyography & MNcle Signals & None & of & Management \\ \hline Section & Eye Tracking & Eye Gaze \& Pupil & & \\  & Eye Tracking & Dilatation, & Heart Rate, & & \\  & System, & Breathing Rate, & & & \\  & Vials Monitoring & Temperature, & & & \\ Section 4.2.3 & Wearable Sensor, & Posture Angle, & & & \\ [PERSON] et al. (2020)[34] & Personal Video & Ergonomic Scores, & & & \\  & Camera, and & and Human & & & \\  & Motion Tracking & & Positions, & & \\  & Cameras & Postures and & & & \\ \hline Section 4.2.4 & & Nutritional Income, & & & \\  & & Daily User Activity, & & & \\  & Parcker & Duration and Quality & & & \\  & & of Sleep, and User’s & & & \\  & & Mood & & & \\ \hline Section 4.2.5 & Inventory Weight & & & & \\  & Sensor, and Head & Material Handling & Image Based Scenery & Laboratory & \\  & [PERSON] et al. (2020)[36] & Mounted Display & Tracking & Classification & Environment & \\  & Lenses & & & & \\ \hline Section 4.2.6 & Proprietary Shear & Manual Shear Force & None & Manual Hose & Manual Operation \\  & [PERSON] et al. (2021)[37] & Force Sensor & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Domain 2—Reviewed journal articles and conference papers for data collection methods. matters and future directions as intended by this \"state-of-the-art review\". Nevertheless, a summary of the highlights of these three literature reviews found is discussed next. [PERSON] & [PERSON] [11] presented in the year 2011 a literature review on applications, validations, and modifications to existing learning curve models to provide a theoretical background of these to the scientific community. Eight years later, [PERSON] et al. [38] in the year 2019 offered a similar literature review, updating the work of [PERSON] & [PERSON] [11] once again with a retrospective vision. Later in the same year, 2019, [PERSON] & [PERSON] [24] also presented a literature review on the applications of learning curves but once again the focus was on the past and present rather than the future. In the following subsections, each research question will be discussed in order. _RQ1. What Calculation Methods for Estimating the Learning Curve of a Worker Exist in the Recent Scientific Literature?_  
As demonstrated by the journal articles and conference papers reviewed, _new learning curve models are starting to emerge._ These models deliver higher complexity, considering decreasing learning rates, making these models multivariate models. This review shows that these models are more precise than previous models though they are harder to estimate. These new learning curve models, still tested with real-life data, are yet to be implemented for long-term parameterization in industry settings. Advancements in the learning curves field have emerged, such as _[PERSON] et al.