# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 4 Results and Evaluation
### Experiments and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Experiment** & **Training Set** & **Test Set** & **Accuracy** \\ \hline \multirow{3}{*}{\(1\)} & Ours\_101 to105 & \(1\times\) HH101 + Fine-tuning(0.2 \(\times\) HH105) & 0.8 \(\times\) HH105 & 0.6899 \\ \cline{2-5}  & DANN\_101 to105 & \(1\times\) HH101 + 0.2 \(\times\) HH105 & 0.8 \(\times\) HH105 & 0.6312 \\ \hline \multirow{3}{*}{\(2\)} & Ours\_101 to109 & \(1\times\) HH101 + Fine-tuning(0.2 \(\times\) HH109) & 0.8 \(\times\) HH109 & 0.7413 \\ \cline{2-5}  & DANN\_101 to109 & \(1\times\) HH101 + 0.2 \(\times\) HH109 & 0.8 \(\times\) HH109 & 0.7265 \\ \hline \multirow{3}{*}{\(3\)} & Ours\_101 to110 & \(1\times\) HH101 + Fine-tuning(0.2 \(\times\) HH110) & 0.8 \(\times\) HH110 & 0.6673 \\ \cline{2-5}  & DANN\_101 to110 & \(1\times\) HH101 + 0.2 \(\times\) HH110 & 0.8 \(\times\) HH110 & 0.6343 \\ \hline \multirow{3}{*}{\(4\)} & Ours\_105 to101 & \(1\times\) HH105 + Fine-tuning(0.2 \(\times\) HH101) & 0.8 \(\times\) HH101 & 0.7664 \\ \cline{2-5}  & DANN\_105 to101 & \(1\times\) HH105 + 0.2 \(\times\) HH101 & 0.8 \(\times\) HH101 & 0.5437 \\ \hline \multirow{3}{*}{\(5\)} & Ours\_105 to109 & \(1\times\) HH105 + Fine-tuning(0.2 \(\times\) HH109) & 0.8 \(\times\) HH109 & 0.7339 \\ \cline{2-5}  & DANN\_105 to109 & \(1\times\) HH105 + 0.2 \(\times\) HH109 & 0.8 \(\times\) HH109 & 0.5656 \\ \hline \multirow{3}{*}{\(6\)} & Ours\_105 to110 & \(1\times\) HH105 + Fine-tuning(0.2 \(\times\) HH110) & 0.8 \(\times\) HH110 & 0.6129 \\ \cline{2-5}  & DANN\_105 to110 & \(1\times\) HH105 + 0.2 \(\times\) HH110 & 0.8 \(\times\) HH110 & 0.5156 \\ \hline \multirow{3}{*}{\(7\)} & Ours\_109 to101 & \(1\times\) HH109 + Fine-tuning(0.2 \(\times\) HH101) & 0.8 \(\times\) HH101 & 0.7468 \\ \cline{2-5}  & DANN\_109 to101 & \(1\times\) HH109 + 0.2 \(\times\) HH101 & 0.8 \(\times\) HH101 & 0.7296 \\ \hline \multirow{3}{*}{\(8\)} & Ours\_109 to105 & \(1\times\) HH109 + Fine-tuning(0.2 \(\times\) HH105) & 0.8 \(\times\) HH105 & 0.6256 \\ \cline{2-5}  & DANN\_109 to105 & \(1\times\) HH109 + 0.2 \(\times\) HH105 & 0.8 \(\times\) HH105 & 0.4656 \\ \hline \multirow{3}{*}{\(9\)} & Ours\_109 to110 & \(1\times\) HH109 + Fine-tuning(0.2 \(\times\) HH110) & 0.8 \(\times\) HH110 & 0.6631 \\ \cline{2-5}  & DANN\_109 to110 & \(1\times\) HH109 + 0.2 \(\times\) HH110 & 0.8 \(\times\) HH110 & 0.5625 \\ \hline \multirow{3}{*}{\(10\)} & Ours\_110 to101 & \(1\times\) HH110 + Fine-tuning(0.2 \(\times\) HH101) & 0.8 \(\times\) HH101 & 0.7877 \\ \cline{2-5}  & DANN\_110 to101 & \(1\times\) HH110 + 0.2 \(\times\) HH101 & 0.8 \(\times\) HH101 & 0.5546 \\ \hline \multirow{3}{*}{\(11\)} & Ours\_110 to105 & \(1\times\) HH110 + Fine-tuning(0.2 \(\times\) HH105) & 0.8 \(\times\) HH105 & 0.5837 \\ \cline{2-5}  & DANN\_110 to105 & \(1\times\) HH110 + 0.2 \(\times\) HH105 & 0.8 \(\times\) HH105 & 0.6343 \\ \hline \multirow{3}{*}{\(12\)} & Ours\_110 to109 & \(1\times\) HH110 + Fine-tuning(0.2 \(\times\) HH109) & 0.8 \(\times\) HH109 & 0.8069 \\ \cline{2-5}  & DANN\_110 to109 & \(1\times\) HH1110 + 0.2 \(\times\) HH1109 & 0.8 \(\times\) HH109 & 0.6687 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental setup and results of our method and the DANN method.