Even though much research has been conducted on body worn sensors, little research has been performed on specialized (non-ear-worn) headworn sensors. These sensors could be used to perform a variety of health related research. Therefore, this thesis focusses on a new low-power specialized (non-ear-worn) headworn sensor platform and explores how it can be improved.

In this research, a learner’s sustained attention in the remote learning context will be studied by collecting data from different sensors. By combining the results of these sensors in a multi-modal analytics tool, the estimation of the learner’s sustained attention can hopefully be improved. This research will mainly focus on microphone recordings of ambient sound in a learners room. The main research question of this research was "How can ambient noise sensing aid in a multi-modal analytics tool to track sustained attention?". The multi-modal learning analytics tool, if accurate enough, could potentially be used by teachers to make their material more engaging and could help learner’s to keep their focus while performing a learning task (Schneider et al., 2015). The research resulted in a model with 61% accuracy. This percentage needs to be further researched, since because of the COVID situation, not enough data could be collected to train the model. Because of the relatively low accuracy of the model, it was found that ambient noise sensing can aid the multi-modal analytics tool to some extent by adding some data-points it is certain about, when the mobile movement tracking model does not detect a distraction. If the model improves in future research, the model could be able to help mobile movement tracking model, even if the mobile movement tracking model already predicts a distraction with bigger then 50% certainty.