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Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disea...

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Autores principales: Ortiz-Barrios, Miguel, Järpe, Eric, García-Constantino, Matías, Cleland, Ian, Nugent, Chris, Arias-Fonseca, Sebastián, Jaramillo-Rueda, Natalia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318990/
https://www.ncbi.nlm.nih.gov/pubmed/35891090
http://dx.doi.org/10.3390/s22145410
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author Ortiz-Barrios, Miguel
Järpe, Eric
García-Constantino, Matías
Cleland, Ian
Nugent, Chris
Arias-Fonseca, Sebastián
Jaramillo-Rueda, Natalia
author_facet Ortiz-Barrios, Miguel
Järpe, Eric
García-Constantino, Matías
Cleland, Ian
Nugent, Chris
Arias-Fonseca, Sebastián
Jaramillo-Rueda, Natalia
author_sort Ortiz-Barrios, Miguel
collection PubMed
description The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy ([Formula: see text]).
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spelling pubmed-93189902022-07-27 Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients Ortiz-Barrios, Miguel Järpe, Eric García-Constantino, Matías Cleland, Ian Nugent, Chris Arias-Fonseca, Sebastián Jaramillo-Rueda, Natalia Sensors (Basel) Article The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy ([Formula: see text]). MDPI 2022-07-20 /pmc/articles/PMC9318990/ /pubmed/35891090 http://dx.doi.org/10.3390/s22145410 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ortiz-Barrios, Miguel
Järpe, Eric
García-Constantino, Matías
Cleland, Ian
Nugent, Chris
Arias-Fonseca, Sebastián
Jaramillo-Rueda, Natalia
Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients
title Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients
title_full Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients
title_fullStr Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients
title_full_unstemmed Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients
title_short Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients
title_sort predicting activity duration in smart sensing environments using synthetic data and partial least squares regression: the case of dementia patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318990/
https://www.ncbi.nlm.nih.gov/pubmed/35891090
http://dx.doi.org/10.3390/s22145410
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