# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 7 Results
### Comparison  
To allow a better comparison of TL-FmRADLs performance against state-of-the-art research in AR, this Section compares four different studies presenting different approaches to transfer knowledge across sensors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subjects** & **Audio-Learner Baseline** & **AL100** & **AL075** & **AL050** & **AL025** \\ \hline S01 & 78.9 & **95.2** & 87.6 & 80.7 & 74.3 \\ S02 & 76.7 & **93.5** & 88.1 & 79.2 & 75.5 \\ S03 & 62.7 & **94.6** & 86.1 & 77.7 & 72.0 \\ S04 & 57.0 & **96.5** & 86.8 & 79.5 & 73.7 \\ S05 & 71.3 & **95.5** & 87.1 & 80.5 & 73.8 \\ S06 & 70.1 & **96.4** & 89.2 & 81.2 & 73.5 \\ S07 & 62.8 & **93.4** & 86.1 & 78.8 & 75.5 \\ S08 & 67.2 & **89.2** & 84.2 & 77.8 & 70.7 \\ S09 & 69.0 & **93.0** & 87.4 & 79.8 & 73.2 \\ S10 & 69.0 & **93.1** & 87.9 & 79.9 & 71.5 \\ \hline Average & 68.5 & **94.0** & 87.0 & 79.5 & 73.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Relationship to compare the performance of the audio average classification (expressed in percentage) model in contrast to the baseline and the four different alternatives over the AL approach. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subjects** & **Inertial-Learner** & **Image-Teacher** & **Inertial-Learner (Fusion Stage)** \\ \hline S01 & 95.2 & 99.0 & **97.2** \\ S02 & 96.2 & 98.9 & **97.0** \\ S03 & 91.7 & 99.0 & **97.2** \\ S04 & 90.2 & 95.4 & **98.6** \\ S05 & 88.8 & 92.5 & **96.6** \\ S06 & 92.9 & 97.9 & **98.4** \\ S07 & 89.9 & 94.4 & **93.2** \\ S08 & 90.6 & 93.5 & **92.0** \\ S09 & 86.8 & 91.4 & **96.1** \\ S10 & 87.5 & 90.9 & **94.4** \\ \hline Average & 91.0 & 95.3 & **96.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of the average classification accuracy (expressed in percentage) of the performance achieved when the Inertial-Learner model is trained over TL-FmRADLs against the baseline performance of the image-teacher model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subjects** & **Audio-Learner** & **Image-Teacher** & **Audio-Learner (Fusion Stage)** \\ \hline S01 & 78.9 & 99.0 & **95.2** \\ S02 & 76.7 & 98.9 & **93.5** \\ S03 & 62.7 & 99.0 & **94.6** \\ S04 & 57.0 & 95.4 & **96.5** \\ S05 & 71.3 & 92.5 & **95.5** \\ S06 & 70.1 & 97.9 & **96.4** \\ S07 & 62.8 & 94.4 & **93.4** \\ S08 & 67.2 & 93.5 & **89.2** \\ S09 [PERSON] and [PERSON] [69] presented a method to transfer knowledge where the teacher consisted of mainstream sensing (video) to provide labelled data to the inertial sensors embracing the role as learners;
* [PERSON] et al. [70] proposed a method that works by extracting 2D poses from video frames and then used a regression model to map them to inertial sensors signal;
* [PERSON] and [PERSON] [22] presented a paper in which they used Web knowledge as a bridge to help link the different label spaces between two smart environments. Their method consisted of an unsupervised approach where features were clustered based in their distribution; hence, even though the feature's space were different, their distribution distance helped to build an association to transfer knowledge. * [PERSON] et al. [71] presented two methods (PureTransfer and Transfer+LimitedTrain) for transferring knowledge. For the purposes of this state-of-the-art comparison, only the method reporting highest performance was taken into account (i.e., Transfer+LimitedTrain). Transfer+LimitedTrain method consisted of a DNN which added higher layers to the new targeted model, where only the higher layer is retrained. Notice that, since the algorithms described in the aforementioned research have different settings compared to those adopted in this evaluation, we cannot use their algorithms to compare their performance when training over InSync. Hence, the comparison should be made based upon the improvement (i.e., baseline \(\
eq\) accuracy achieved). The different studies mentioned above are enlisted in Table 13, where different sensing technology has been used.