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

Last update: Oct 18, 2021

Related tags

Deep Learning Using-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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