# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 4 Experimental Setup and Evaluation
### Model Interpretability
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Sensors\_Feature** & \multicolumn{3}{c}{**Feature Importance**} & \multicolumn{3}{c}{**F1-Score of \(N\) Runs**} \\ \hline
**Type\_Location\_Place of Sensors** & **LSTM** & **Rank** & **1D CNN** & **Rank** & **Mean \(\pm\) SD of LSTM** & **Mean \(\pm\) SD of CNN** \\ \hline PIR\_Shower\_Bathroom & 13.13 & 7 & 12.43 & 8 & 69.02 \(\pm\) 2.63 & 67.46 \(\pm\) 1.30 \\ PIR\_Basin\_Bathroom & 13.36 & 5 & 11.26 & 10 & 68.79 \(\pm\) 2.18 & 68.63 \(\pm\)3.35 \\ PIR\_Cooktop\_Kitchen & 13.79 & 1 & 10.63 & 12 & 68.36 \(\pm\) 2.71 & 69.26 \(\pm\) 4.58 \\ Magnetic\_Main door\_Entrance & 12.57 & 10 & 13.50 & 2 & 69.58 \(\pm\) 3.17 & 66.40 \(\pm\) 2.93 \\ Magnetic\_Fridge\_Kitchen & 13.72 & 2 & 12.67 & 6 & 68.44 \(\pm\) 2.96 & 67.22 \(\pm\) 1.53 \\ Magnetic\_Cabinet\_Bathroom & 13.17 & 6 & 13.55 & 1 & 68.98 \(\pm\) 5.04 & 66.34 \(\pm\) 2.82 \\ Magnetic\_Cupboard\_Kitchen & 12.55 & 11 & 11.99 & 9 & 69.60 \(\pm\) 2.14 & 67.90 \(\pm\) 2.25 \\ Electric\_Microwave\_Kitchen & 12.25 & 12 & 12.98 & 3 & 69.90 \(\pm\) 2.60 & 66.91 \(\pm\) 1.94 \\ Electric\_Toaster\_Kitchen & 12.81 & 8 & 12.83 & 5 & 69.34 \(\pm\) 2.43 & 67.06 \(\pm\) 3.35 \\ Pressure\_Bed\_Bedroom & 13.48 & 4 & 10.96 & 11 & 68.67 \(\pm\) 3.21 & 68.94 \(\pm\) 4.04 \\ Pressure\_Seat\_Living & 13.62 & 3 & 12.55 & 7 & 68.53 \(\pm\) 3.77 & 67.34 \(\pm\) 4.10 \\ Flush\_Toilet\_Bathroom & 12.74 & 9 & 12.86 & 4 & 69.41 \(\pm\) 2.10 & 67.03 \(\pm\) 1.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Permutation feature importance of Ordonez Home A.  
Figure 10: Results of mean F1-score for feature permutation of 12 sensors with \(N=12\) runs from Ordonez Home A. We further analyse the effect of the proposed method for the performance improvement to the minority classes : _Breakfast_, _Grooming_, _Lunch_, _showering_, _toileting_, _snack_, _Dinner_. For example, the feature PIR_Cooktop_Kitchen is the most important feature for LSTM but the least important feature for 1D CNN from smart home A, as shown in Table 7. In contrast, the feature Electric_Microwave_Kitchen is one of the most important features for 1D CNN, but the last ranked and least important feature for LSTM. In addition to the two aforementioned features, the features Magnetic_Fridge_Kitchen, Magnetic_Cupboard_Kitchen, and Electric_Toaster_Kitchen have the different rankings from LSTM and 1D CNN, where they contribute to the recognition of minority classes of the kitchen area, such Breakfast, Lunch and snack. Moreover, the PIR_Basin_Bathroom, Flush_Toilet_Bathroom, and PIR_Shower_Bathroom features have different rankings where they contribute to the recognition of minority activities of bathroom area, such as Grooming, showering, and toileting. Features from smart home B also have different rankings where they contribute to the recognition of minority activities from both kitchen and bathroom areas. For example, PIR_Shower_Bathroom is one of the most important features for LSTM, but the least important features for 1D CNN, as shown in Table 8. Moreover, the Magnetic_Fridge_Kitchen and Electric_Microwave_Kitchen are the most important features for 1D CNN where they can contribute to the recognition of the minority classes such as Breakfast, Lunch,  
Figure 11: Results of mean F1-score for feature permutation of 12 sensors with \(N=12\) runs from Ordonez Home B.