# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 8 Discussion  
This paper presented and evaluated TL-FmRADLs; a transfer learning framework for recognising activities of daily living across sensing technology. Three different AR models were designed defined by its sensing data as the AR image model, AR inertial model and AR audio model. Derived from the baseline experiment conducted, the AR image model was empirically determined as the teacher model, whereas the AR inertial and AR audio models adopted the role as learner. Two experiments were presented, one showcasing the benefit of each of the main components implemented in TL-FmRADLs. Regarding the T/L phase, results showed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research Method** & **Teacher** & **Learner** & **Baseline** & **Achieved** & **Improvement** \\ \hline [PERSON] and [PERSON] [69] & Image & Inertial & 95.4 & 97.1 & 1.7 \\ [PERSON] et al. [70] & Image & Inertial & 85.0 & 90.0 & 5.0 \\ [PERSON] and [PERSON] [22] & CPP & CIA & 47.3 & 59.8 & 12.5 \\ [PERSON] and [PERSON] [22] & CIA & CPP & 42.8 & 61.2 & 18.4 \\ [PERSON] et al. [71] & Inertial & Image & 72.3 & 75.1 & 2.9 \\ [PERSON] et al. [71] & Inertial & Audio & 84.3 & 87.8 & 3.5 \\ [PERSON] et al. [71] & Image & Audio & 84.1 & 90.4 & 6.2 \\ [PERSON] et al. [71] & Image & Inertial & 70.7 & 94.4 & 23.6 \\
**TL-FmRADLs** & **Image** & **Inertial** & **91.0** & **96.1** & **5.1** \\
**TL-FmRADLs** & **Image** & **Audio** & **68.5** & **94.0** & **25.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of TL-FmRADLs’ performance against state-of-the-art approaches, where CPP stands for contact movement, passive infrared, pressure, and CIA stands for contact movement, image, audio. that the highest average improvement in accuracy of 93.4% and 72.2% when training the inertial-learner model and the audio-learner model, respectively. It was observed that there was a fluctuation on the accuracy achieved when increasing the amount of training data from the alternative T/L075 to T/L100 (as shown in Tables 7 and 8). Results suggested that transferring 100% of knowledge from the image-teacher model to the learners models (inertial and audio) trained the DNN models' process to the extent of overfitting the model. In this regard, the performance diminished the classification accuracy of the objective AR subject's model. Nevertheless, this finding can represent a positive finding, since one can assume that the performance of the AR model benefited from this method over a small portion of fusion labelled data, thereby implying a reduced computational cost. As for the second experiment, the AL technique contributed by fusing features from a complementary sensing technology. Results showed the highest classification accuracy when converging the Image-teacher model and inertial-learner model, which reached 96.1%, representing an increase of 2.7% compared to the achievement under the T/L phase (93.4%). However, the highest gain was reflected when converging the image-teacher model and audio-learner model as shown by its classification accuracy, which increased from 72.2% (achieved at the T/L phase) to 94% (achieved at the AL phase), thereby reflecting an improvement of 21.8%. In general, one can rationalise that the learner model benefits from the teacher's knowledge over converging T/L and AL techniques since the Learner models showed important improvements in both the experiment. Finally, TL-FmRADLs was compared against five different methods reported in the literature. Such a comparison showed a more significant improvement for the learner model when transferring knowledge from an image-based model to an audio-based model with an improvement rising to 25.5%. It also showed improvement in models that had already achieved high performance in their baseline.