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Activity recognition in smart homes is essential when we wish to proposeautomatic services for the inhabitants. However, it poses challenges in termsof 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 foractivity recognition in smart homes by merging methods from the NaturalLanguage Processing (NLP) and the Time Series Classification (TSC) domains. Weevaluate the performance of our method on two datasets issued from the Centerfor Advanced Studies in Adaptive Systems (CASAS). Moreover, we analyze thecontributions of the use of NLP encoding Bag-Of-Word with Embedding as well asthe ability of the FCN algorithm to automatically extract features andclassify. The method we propose shows good performance in offline activityclassification. Our analysis also shows that FCN is a suitable algorithm forsmart home activity recognition and hightlights the advantages of automaticfeature extraction.

Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes