  Long Short Term Memory LSTM-based structures have demonstrated their
efficiency for daily living recognition activities in smart homes by capturing
the order of sensor activations and their temporal dependencies. Nevertheless,
they still fail in dealing with the semantics and the context of the sensors.
More than isolated id and their ordered activation values, sensors also carry
meaning. Indeed, their nature and type of activation can translate various
activities. Their logs are correlated with each other, creating a global
context. We propose to use and compare two Natural Language Processing
embedding methods to enhance LSTM-based structures in activity-sequences
classification tasks: Word2Vec, a static semantic embedding, and ELMo, a
contextualized embedding. Results, on real smart homes datasets, indicate that
this approach provides useful information, such as a sensor organization map,
and makes less confusion between daily activity classes. It helps to better
perform on datasets with competing activities of other residents or pets. Our
tests show also that the embeddings can be pretrained on different datasets
than the target one, enabling transfer learning. We thus demonstrate that
taking into account the context of the sensors and their semantics increases
the classification performances and enables transfer learning.
