  Activity recognition in smart homes is essential when we wish to propose
automatic services for the inhabitants. However, it poses challenges in terms
of variability of the environment, sensorimotor system, but also user habits.
Therefore, endto-end systems fail at automatically extracting key features,
without extensive pre-processing. We propose to tackle feature extraction for
activity recognition in smart homes by merging methods from the Natural
Language Processing (NLP) and the Time Series Classification (TSC) domains. We
evaluate the performance of our method on two datasets issued from the Center
for Advanced Studies in Adaptive Systems (CASAS). Moreover, we analyze the
contributions of the use of NLP encoding Bag-Of-Word with Embedding as well as
the ability of the FCN algorithm to automatically extract features and
classify. The method we propose shows good performance in offline activity
classification. Our analysis also shows that FCN is a suitable algorithm for
smart home activity recognition and hightlights the advantages of automatic
feature extraction.
