# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 4 Results and Evaluation
### Experiments and Results
Figure 12: Results of our method and comparison experiments for accuracy, precision, recall and F1-score at each epoch, based on the source domain as HH109, target domain as HH110. Figure 13: Results of our method and comparison experiments for accuracy, precision, recall and F1-score at each epoch, based on the source domain as HH110, target domain as HH101. For each set of experiments, the results are shown in Figures 4-15. The following analysis of the improvement of each indicator is based on the indicators that work best in our method and in the comparison method. As shown in Figure 4, HH101 is the source domain and HH105 is the target domain. Our method achieved better activity recognition at the 50 th iteration and tended to stabilize thereafter, peaking at the 348 th iteration; the comparison method stabilized at around the 200 th iteration, peaking at the 259 th. However, our method was able to approach the peak of the comparison method at an early stage and remained stable. Additionally, our method improved 3.07% in accuracy, 9.48% in precision, 8.61% in recall and 9.91% in F1-score over that of the comparison method. As shown in Figure 5, HH101 is the source domain and HH109 is the target domain. Our method achieved better activity recognition at the 100 th iteration, peaking at the 434 th iteration; the comparison method achieved better activity recognition at the 250 th iteration, peaking at the 256 th. However, on every iteration, our method outperformed the comparison method. Our method improved 4.08% in accuracy, 10.94% in recall and 10.23% in F1-score over those of the comparison method. In addition, the precision was on a par with that of the comparison method. As shown in Figure 6, HH101 is the source domain and HH110 is the target domain. Our method led the comparison method from the beginning to the end and remained stable throughout, peaking at the 119 th iteration; the comparison method peaked at the 378 th iteration. Our method improved 5.43% in accuracy and 1.58% in F1-score over those of the comparison method. Additionally, precision and recall were on par with that of the comparison method. As shown in Figure 7, HH105 is the source domain and HH101 is the target domain. Our method achieved better recognition at the 50-150 th iteration and remained stable after the 250 th iteration; the comparison method remained stable only after the 250 th iteration, but the accuracy results were consistently lower than those of our method. Our method improved 4.26% in accuracy, 5.35% in recall and 3.46% in F1-score over those of the comparison method. In addition, the precision was on a par with that of the comparison method. As shown in Figures 8 and 9, HH105 is the source domain and HH109/HH110 is the target domain. Our method achieved a high level of accuracy after a smaller number of iterations and consistently led the comparison methods with a high level of accuracy. In Figure 8, our method improved 3.96% in accuracy, 4.30% in recall and 3.34% in F1-score over those of the comparison method. In Figure 9, our method improved 7.53% in accuracy, 12.13% in precision, 8.93% in recall and 9.98% in F1-score over those of the comparison method. In addition, the performance of the fine-tuned model was very stable, which demonstrates the effectiveness of our migration method on LSTMs. As shown in Figures 10-12, our method achieved a high level of accuracy in less than 50 iterations and remained stable, whereas the comparison method took 200-300 iterations to reach stability. Additionally, our method generally improved accuracy by 3.07-6.27%  
Figure 15: Results of our method and comparison experiments for accuracy, precision, recall and F1-score at each epoch, based on the source domain as HH110, target domain as HH109. over that of the comparison method. In Figure 10, our method improved the precision by 6.12% and the F1-score by 6.05%; in Figure 12, our method improved the precision by 7.63%; the rest of the indicators were at the same level as those of the comparison method.