## 5 Results  
For the performance evaluation, the standard ML metrics (_accuracy_, _recall_, _precision_, and _F1 score_) were selected. Due to the class imbalance, i.e., unequal sample size between the activity of _falling_ (index 1) and the other activities, the measures of accuracy, recall, and precision were expected to have slight errors, which is of little relevance, since the relations were the main interest. The F1 score has the robustness to overcome this issue since it compensates for the tendency of the recall to underestimate and the precision to overestimate using the harmonic mean calculated from both. Two additional metrics (the macro-averaged _Matthew Correlation Coefficient_ (_MCC_) and _Cohen Kappa_) are also robust against class imbalances and, along with the F1 score, form the basis for the assessment. The MCC, which has its origin in binary classification, can be used to evaluate classifiers' performance in a multiclass classification task when a one-vs-all strategy is pursued. In this case, a classifier's performance is computed using the average of the performance of every classifier, where each one can only classify a sample as belonging to the class assigned or, conversely, as belonging to any of the remaining classes. The Cohen Kappa measures the degree of agreement between different classifiers, where in this case, the probabilities of agreement between the classifiers in a one-vs-all strategy, along with the probabilities for a random-driven agreement, are considered. The metrics of the results of the classification studies are listed in Table 3. The learning curves, consisting of the loss and accuracy functions, as well as the resulting confusion matrices, are displayed in Figures 14-18. The learning curves of the CNN (Figure 14) show a moderate learning pace with decreasing variance and the likelihood of sudden spikes that tend to appear when using the Adam optimizer. The decreasing gap between the training and validation curves indicates the absence of overfitting. From the confusion matrix, it is evident that there is a higher probability of the network confusing the activity of _picking up objects_ with _drinking_, while the other tasks remain unaffected. The learning curves of the RNN-based networks show varying performances. The LSTM network has similar learning curves to the CNN with regard to the learning pace and generalization, and the confusion matrix shows the same issue as the CNN. The learning  
Figure 13: Structure of the selected CAE-based model. curves of the Bi-LSTM show significantly faster convergence but suffer from higher variance, although the confusion is significantly smaller compared to the aforementioned models. The GRU network shows a higher tendency toward overfitting, with comparably small variances in accuracy progress. Last but not least, the CAE network shows the biggest tendency to overfit and, besides the confusion between tasks 0 and 2, has an increased risk of confusing task 0 (_drinking_) with task 4 (_standing up_). Figure 14: Loss and accuracy during training and confusion matrix for CNN-based classification. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **Accuracy\%** & **Precision\%** & **Recall\%** & **F1\%** & **MCC\%** & \
\begin{tabular}{c} **Cohen** \\ **Kappa\%** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Execution** \\ **Time/s** \\ \end{tabular} \\ \hline CNN & 88.0 & 89.7 & 88.0 & 87.9 & 86.0 & 91.0 & 3251 \\ LSTM & 82.3 & 85.6 & 82.9 & 83.0 & 80.1 & 85.5 & 2966 \\ Bi-LSTM & 86.3 & 88.7 & 86.3 & 86.8 & 83.7 & 90.2 & 10,780 \\ GRU & 82.3 & 82.6 & 82.3 & 82.0 & 78.8 & 86.0 & 2569 \\ CAE & 81.7 & 84.2 & 81.7 & 82.5 & 78.1 & 78.1 & 7349 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results and execution times for validation for the investigated methods. Figure 15: Loss and accuracy during training and confusion matrix for LSTM (RNN)-based classification.