학술논문
Sensor2Vec: an Embedding Learning for Heterogeneous Sensors for Activity Classification
Document Type
Conference
Source
2020 International Symposium on Community-centric Systems (CcS) Community-centric Systems (CcS), 2020 International Symposium on. :1-6 Sep, 2020
Subject
Language
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.