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Long Short Term Memory LSTM-based structures have demonstrated theirefficiency for daily living recognition activities in smart homes by capturingthe 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 carrymeaning. Indeed, their nature and type of activation can translate variousactivities. Their logs are correlated with each other, creating a globalcontext. We propose to use and compare two Natural Language Processingembedding methods to enhance LSTM-based structures in activity-sequencesclassification tasks: Word2Vec, a static semantic embedding, and ELMo, acontextualized embedding. Results, on real smart homes datasets, indicate thatthis approach provides useful information, such as a sensor organization map,and makes less confusion between daily activity classes. It helps to betterperform on datasets with competing activities of other residents or pets. Ourtests show also that the embeddings can be pretrained on different datasetsthan the target one, enabling transfer learning. We thus demonstrate thattaking into account the context of the sensors and their semantics increasesthe classification performances and enables transfer learning.

Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes