# Towards an Assembly Support System with Dynamic Bayesian Network
## 4 Experimental Results
We selected the features having a \(p\)-value less than 0.1, because a \(p\)-value greater than 0.1 indicates insufficient evidence [35]. Thus, we chose to be used as additional input data in the prediction models and binarized the following features: gender (male/female), sleep quality (good/bad), eyeglass wearer (yes/no), and height (tall/small--with the threshold of 174 cm, the average height of the participants). Next, the proposed DBN predictor is compared with the order 1 Markov model presented in [2], the long short-term memory (LSTM) recurrent neural network presented in [32], and the order 3 prediction by partial matching (PPM) with neighbor exploration presented in [12]. Figure 7 presents the prediction rate of the selected methods. As can be observed, the LSTM network has the highest prediction rate across all runs, averaging 96%. Compared with LSTM, PPM predicts 23% less and DBN 46% less. Even though LSTM has a high prediction rate, as can be observed in Figure 8, the accuracy of its predictions is lower compared with the other methods, having an accuracy of only 28%. Both the PPM and Markov models have a very high accuracy of 49%. The DBN has the highest prediction accuracy of 50%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **\(F\)-Value** & **\(p\)-Value** \\ \hline Assembly Experience & 0.00079 & 0.97762 \\ Age & 0.10553 & 0.74631 \\ Stress level before the assembly & 0.36950 & 0.54535 \\ Hungry & 0.55439 & 0.45917 \\ Under influence of medication & 0.69261 & 0.40827 \\ Preferred hand & 2.40527 & 0.12570 \\ Gender & 2.86426 & 0.09528 \\ Sleep quality & 2.87701 & 0.09456 \\ Eyeglass wearer & 3.99500 & 0.04975 \\ Height & 6.98954 & 0.01023 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature significance. Figure 7: Prediction rate without the mood variable through k-fold cross-validation. The coverage is strongly correlated with the prediction rate (see Figure 9). Surprisingly, the best coverage of 36% is obtained by using the PPM implementation. The LSTM has a coverage of only 27%, despite its high prediction rate. The DBN has a coverage of 26%, slightly higher than the Markov model's 24%. Besides the four core human characteristics (height, gender, sleep quality, and if the worker wears glasses), all the above methods have been further enhanced to take into consideration the mood of the user in the prediction process, thus obtaining a better representation of the human worker. The mood of the user is recorded with the pretrained emotion recognition model from Intel's OpenVino toolkit [36]. It is able to identify five states (neutral, happy, sad, surprise, and anger), which were binarized into either a positive or negative mood of the user. In Figure 10, a decrease in prediction rate across all the prediction methods was observed, except for LSTM, which has a slightly higher prediction rate of 97%. The DBN has a decrease of 3% in the predictions made, while PPM predicts 14% less steps. The Markov predictor is the most influenced by the addition of the mood variable, being able to predict only 33% of the assembly steps, which is a decrease of 16% compared to the implementation without the mood variable. Figure 8: Prediction accuracy without the mood variable through k-fold cross-validation. Figure 9: Coverage without the mood variable through k-fold cross-validation. A decrease in accuracy can be observed for all the methods in Figure 11. While, for the PPM and LSTM, the decrease is small, of only 3% and 4%, respectively, the Markov and DBN predictors seem to lose considerable percentages in their prediction accuracy. The Markov predictor has a 10% decrease compared to the one that does not take the mood into consideration, thus having an effective prediction accuracy of 39%. The DBN seems to take a big impact to its prediction accuracy, with a negative change of 14%, thus obtaining a prediction accuracy of 37%, being very close to that obtained by using the Markov model. Figure 12 presents the coverage of the evaluated methods, including the mood variable.