# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 7 Results
### Active Learning  
As introduced earlier, to evaluate the performance of the AL approach. Derived from the baseline performance achieved by the AR Image model which reached an average classification accuracy of 95.3%; representing a difference of 4.3% and 26.8% compared to the inertial-learner model and audio-learner model, respectively. The AR image model was defined as the teacher model underlined in this evaluation.  
Hence, to evaluate the performance of the Learner models, two experiments were undertaken. Firstly, one experiment where the AR inertial model played the role as a learner, followed by the second experiment, in which the AR audio model played the role as learner.  
The evaluation consisted of progressively queuing the learner model to retrieve its sensing features to fuse with the learner model. To illustrate the benefit of this mechanism, the study defined four sub-dataset of data split into segments of 25% each. In this context, the baseline results were compared against the performance of the classification model when utilising 100%, 75%, 50%, and 25% of labelled data retrieved from the oracle, as rationalised in Section 4.2.  
Results from the AL approach are shown in Table 9. The table contrasts the baseline against the performance from the AL classification accuracy when the inertial model adopts the role as a learner model and it is trained under the schema of AL. The overall difference as presented under the row 'Average', shows the best performance when 100% of the learner model labels are used, consisting of 96.1%.  
Similarly, Table 10 contrasts the baseline against the performance from the AL classification accuracy when the audio model adopts the role as a learner and gradually queried the oracle. The average difference is presented in the row 'Average', which shows the best performance when the learner model queried 100% of the labels provided by the learner model, consisting of 94%.  
The following tables compare the baselines from both learner models (inertial-learner and audio-learner), teacher model (image-teacher) against the performance achieved by the learner models when completing the proposed method training (column 'Fusion stage'). Specifically, Table 11 shows the performance when the inertial-learner model completed the TL-FmRADLs process, and Table 12 shows the performance when the audio-learner model completed the TL-FmRADLs process.  
Results of this study showed the benefit of utilising TL-FmRADLs, which relies upon feature fusion and T/L techniques to increase the performance of the classification accuracy underlined by an AL to enable an automatic approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subjects** & **Inertial-Learner Baseline** & **AL100** & **AL075** & **AL050** & **AL025** \\ \hline S01 & 95.2 & **97.2** & 97.0 & 93.1 & 91.0 \\ S02 & 96.2 & **97.0** & 98.6 & 98.5 & 96.3 \\ S03 & 91.7 & **97.2** & 93.4 & 92.8 & 91.6 \\ S04 & 90.2 & **98.6** & 96.7 & 94.3 & 91.1 \\ S05 & 88.8 & **96.6** & 92.6 & 89.3 & 88.6 \\ S06 & 92.9 & **98.4** & 97.0 & 95.0 & 93.7 \\ S07 & 89.9 & **93.2** & 92.6 & 91.7 & 90.8 \\ S08 & 90.6 & **92.0** & 91.5 & 90.1 & 88.8 \\ S09 & 86.8 & **96.1** & 95.6 & 89.3 & 86.7 \\ S10 & 87.5 & **94.4** & 94.8 & 90.8 & 88.9 \\ \hline Average & 91.0 & **96.1** & 95.0 & 92.5 & 90.8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Relationship to compare the performance of the Inertial average classification (expressed in percentage) model in contrast to the baseline and the four different alternatives over the AL approach.