## 3 Results and Discussion
### Transfer Learning  
Transfer learning aims to leverage knowledge acquired from one, or more, so-called source domains (or tasks) to improve performance on a target domain (or task) where data are scarce. In many real-world applications, the machine learning model trained for a particular setup fails to maintain a high performance level once deployed in a similar, yet different, environment. Consider, for instance, the task of recognizing activities in a smart home environment. A classifier trained based on a dataset provided by one home will generally deliver lower performance when it is directly used in another home. There can be various reasons for this, such as the different distribution of sensors in the home, differences in the plan of the house, different human behaviours, and a varying number of people or sensors in the house, to name a few. Moreover, the problem is exacerbated in situations where a model learned in one environment cannot be directly reused in another environment due to differences in sensing modalities or class labels. Although there could be significant overlap and only slight variation between the two setups, it is often the case that a new model needs to be learned from scratch. The role of transfer learning in this case is to reduce the labelling effort and reuse knowledge acquired from different setups, while, at the same time, maintaining a high performance level. This aspect is particularly important in the case of IDIVS, where sensing tasks need to be performed in dynamic environments over varying subsets of sensors, which can include new sensing modalities or even augmented target tasks, e.g., new class labels. To this end, we propose a transfer layer that extends the basic functionality of IDIVS, in the sense that it is designed to learn a robust feature representation that can be shared across different related domains. In other words, we aim to learn high-level features that can be used to represent both the source and the target domains. More specifically, we investigate the use of autoencoder-based methods to learn a feature-based transformation where the instances from both the source and target domains are projected to a shared latent space, such that the distances between them are reduced [31]. This approach falls into the category of feature-based transfer learning schemes, as opposed to instance-based methods, where the goal is to reduce the discrepancy between the source and target by reweighting the source samples before training. (For a more detailed review of transfer learning, we refer the reader to [PERSON] et al. [10]). Scenarios can include situations where IDIVSs only have access to (i) unlabelled training data or (ii) few labelled training instances, from the target domain. In the case of the former, the dynamic aspects of IDIVS in particular play a key role. That is, the transfer learning layer incorporates the ability to select those sensors from the target domain, which remain relevant in relation to the sensing task performed by the IDIVS in the source domain(s). For the latter, the interactive feedback mechanism of IDIVS can further be leveraged to select those instances that can contribute most to the domain adaptation phase using active learning (as seen in the Section 3.3). Practically, suppose we aim to transfer an IDIVS model from a source domain S to a target domain T. In order to align our IDIVS to the new domain, in addition to the general input of the IDIVS introduced in Section 2.3, we also need to provide input to the model, i.e., to the transfer layer of our IDIVS, the differences between the two domains.