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

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Deep LearningUsing-Language-Model-To-Bootstrap-Human-Activity-Recognition-In-Smart-Homes
Overview

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

This repository is the official implementation of Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes.

Requirements

To install requirements:

To use this repository you should download and install SmartHomeHARLib package

git clone [email protected]:dbouchabou/SmartHomeHARLib.git
pip install -r requirements.txt
cd SmartHomeHARLib
python setup.py develop

Embeddings Training

To train Embedding model(s) of the paper, run this command:

To train a Word2Vec model on a dataset, run this command:

python Word2vecEmbeddingExperimentations.py --d cairo

To train a ELMo model on a dataset, run this command:

python ELMoEmbeddingExperimentations.py --d cairo

Activity Sequences Classification Training And Evaluation

To train Classifier(s) model(s) of the paper, run this command:

python PretrainEmbeddingExperimentations.py --d cairo --e bi_lstm --c config/no_embedding_bi_lstm.json

python PretrainEmbeddingExperimentations.py --d cairo --e liciotti_bi_lstm --c config/liciotti_bi_lstm.json

python PretrainEmbeddingExperimentations.py --d cairo --e w2v_bi_lstm --c config/cairo_bi_lstm_w2v.json

python PretrainEmbeddingExperimentations.py --d cairo --e elmo_bi_lstm --c config/cairo_bi_lstm_elmo_concat.json

Results

Our model achieves the following performance on :

Three CASAS datasets

Aruba Aruba Aruba Aruba Milan Milan Milan Milan Cairo Cairo Cairo Cairo
No Embedding Liciotti W2V ELMo No Embedding Liciotti W2V ELMo No Embedding Liciotti W2V ELMo
Accuracy 95.01 96.52 96.59 96.76 82.24 90.54 88.33 90.14 81.68 84.99 82.27 90.12
Precision 94.69 96.11 96.23 96.43 82.28 90.08 88.28 90.20 80.22 83.17 82.04 88.41
Recall 95.01 96.50 96.59 96.69 82.24 90.45 88.33 90.31 81.68 82.98 82.27 87.59
F1 score 94.74 96.22 96.32 96.42 81.97 90.02 87.98 90.10 80.49 82.18 81.14 87.48
Balance Accuracy 77.73 79.96 81.06 79.98 67.77 74.31 73.61 78.25 70.09 77.52 69.38 87.00
Weighted Precision 79.75 82.30 82.97 88.64 79.6 82.03 84.42 87.56 68.45 80.03 77.56 86.83
Weighted Recall 77.73 80.71 81.06 79.17 67.77 75.51 73.62 78.75 70.09 73.82 69.38 84.78
Weighted F1 score 77.92 81.21 81.43 82.93 71.81 77.74 76.59 82.26 68.47 74.84 70.95 84.71
Owner
Damien Bouchabou
PhD Candidate in Machine Learning and Human Activities Recognition
Damien Bouchabou
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