In this study, we investigate a method for automatically extracting universal and meaningful features that are applicable across similar time series-based learning tasks such as activity recognition and fall detection. We propose creating a sequence-to-sequence (seq2seq) model to perform this feature learning. Beside avoiding feature engineering, the meaningful features learned by the seq2seq model can also be utilized for semi-supervised learning. We evaluate both of these benefits on datasets collected fromwearable and ambient sensors.
This program is written in Python==3.7. You can run the code conda install --file requirements.txt to install all the requirements.
You can find an example in the experiment section.
arxiv link: https://arxiv.org/pdf/1907.05597.pdf If you found this library useful in your research, please consider citing:
@article{ghods2019activity2vec,
title={Activity2vec: Learning adl embeddings from sensor data with a sequence-to-sequence model},
author={Ghods, Alireza and Cook, Diane J},
journal={arXiv preprint arXiv:1907.05597},
year={2019}
}
This project is licensed under the MIT License - see the LICENSE.md file for details# Activity2Vec