## 4 Conclusions  
The MMD-MSD dataset is a unique resource in support of research and technology development activities oriented towards MSD prevention. It creates opportunities for a thorough evaluation of different factors contributing to pain and performance reduction. For each participant in the dataset, there are self-assessment data obtained via questionnaire and discussions, direct measurements (including pictures and postural angle calculations), observational assessments (via video recordings during work activities), motion data (three-axis accelerometers), and registrations of peripheral physiological signals (EDA, PPG, skin temperature) captured during assignments involving mental activity (Stroop test). Some important highlights include the following:  
* The MMD-MSD dataset provides a unique combination of sensor recordings in support of multidisciplinary RTD in support of MSD prevention. Here, we reported  
Figure 5: Significant person-specific differences in heart rate variability (HRV) were observed. HRV ranges for the initial Baseline Recording #1 (blue), Stroop Test #1 (orange), Stroop Test #2 (grey), and Baseline Recording #2 (yellow). \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy \(\pm\) Std [\%]**} \\ \cline{2-3}
**Classifier** & **Raw Features** & **With Z-Norm** \\ \hline Naïve Bayes & 67.0 \(\pm\) 3.9 & 67.0 \(\pm\) 3.9 \\ Decision tree & 64.0 \(\pm\) 9.4 & 64.0 \(\pm\) 4.8 \\ Random forest & 66.0 \(\pm\) 3.9 & 63.0 \(\pm\) 5.0 \\ SVM with RBF & 77.5 \(\pm\) 9.8 & 74.0 \(\pm\) 4.4 \\ Generalized linear model & 79.0 \(\pm\) 4.8 & 77.2 \(\pm\) 5.0 \\ FFNN (33-50-50-2) & 78.3 \(\pm\) 4.3 & 76.0 \(\pm\) 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Averaged accuracy and standard deviation (std) for cognitive activity vs. baseline activity, based on feature vectors computed from PPG and GSR. experimental results on the detection of (i) improper neck posture and (ii) task-specific cognitive activity, which illustrate two distinct use cases. * MMD-MSD stands out as a publicly available resource comprising the recordings of 100 volunteers. It is among the largest publicly available datasets that are currently available in support of automated postural angle detection research, ergonomic assessment of sitting body postures in an office environment, and cognitive load detection. The dataset's size permits person-independent modeling of bad postures, but also further research on person-specific model adaptations. * Based on the success rates on the Stroop test, we can confirm that active sitting on an air-cushioned stability disk improves both the sitting posture and performance when compared to sitting on a desk chair. This is in excellent agreement with previous studies investigating stability disks' effectiveness. * For completeness, we need to point out the lack of postural annotations for the video recordings as a major limitation for the wider use of MMD-MSD. Thus, researchers interested in making use of the video recordings will need to invest additional effort in their annotation, either automatically or semi-automatically. In this regard, one would have to use unsupervised training to create a person-specific model of sitting postures and then try to discover and automatically track the ten marker positions (cf. Table 2 and Figure 3), and then, based on these, to estimate the posture angles shown in Table 3 and Figure 4. These angles would need to be manually inspected for at least a portion of the video before they could be considered as annotations of the videos. Finally, one would need to measure the accuracy of video-based models based on the gold-standard (ground-truth) angles specified by the medical experts for the person-specific picture set (cf. Figure 1). We are aware of the significant effort required for video annotation, so we decided to share the MMD-MSD dataset as soon as possible and seek support from the research community.