@inproceedings{a3cf7ef78513479e94b86009ec02924d,
title = "Sensor2Vec: An Embedding Learning for Heterogeneous Sensors for Activity Classification",
abstract = "Based on the idea of word2vec embedding method in NLP, this paper presents a novel idea called sensor2vec which captures the contextual information of the heterogeneous sensory information in the ambient assisted living setting. The contextual information is essential in order to classify and understand the human activity using multi-modal sensory data. In the activity classification, the sensor2vec embedding method is able to do the pre-processing which produce the embedding layer which represents the semantic value in the high-dimensional space. The preliminary experiment based on LSTM shows that the sensor2vec performs better classification result than the one-hot inputs. ",
keywords = "Activity Classification, Ambient Assisted Living, Embedding Learning, LSTM",
author = "Zhong, {Junpei Joni} and Ahmad Lotfi",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Symposium on Community-Centric Systems, CcS 2020 ; Conference date: 23-09-2020 Through 26-09-2020",
year = "2020",
month = sep,
doi = "10.1109/CcS49175.2020.9231478",
language = "English",
series = "2020 International Symposium on Community-Centric Systems, CcS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 International Symposium on Community-Centric Systems, CcS 2020",
}