Human Activity Recognition (HAR) from wearable sensors has gained significant attention in the last few decades, largely because of the potential healthcare benefits. For many years, HAR was done using classical machine learning approaches that require extraction of features. With the resurgence of deep learning, a major shift happened and at the moment, HAR researchers are mainly investigating different kinds of deep neural networks. However, deep learning comes with the challenge of having access to large amounts of labeled examples, which in the field of HAR is considered an expensive task, both in terms of time and effort. Another challenge is the fact that the training and testing data in HAR can be different due to the personal preferences of different people when performing the same activity. In order to try and mitigate these problems, in this paper we explore transfer learning, a paradigm for transferring knowledge from a source domain, to another related target domain. More specifically, we explore the effects of transferring knowledge between two open-source datasets, the Opportunity and JSI-FOS datasets, using weight-transfer for the DeepConvLSTM architecture. We also explore the performance of this transfer at different amounts of labeled data from the target domain. The experiments showed that it is beneficial to transfer the weights of fewer layers, and that deep transfer learning can perform better than a domain-specific deep end-to-end model in specific circumstances. Finally, we show that deep transfer learning is a viable alternative to classical machine learning approaches as it produces comparable results and does not require feature extraction.