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Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Advances in machine learning and low-cost, ubiquitous sensors offer a practical method for understanding the predictive relationship between behavior and health. In this study, we analyze this relationship by building a behaviorome, or set of digital behavior markers, from a fusion of data collected...

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Detalles Bibliográficos
Autores principales: COOK, DIANE J., SCHMITTER-EDGECOMBE, MAUREEN
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132971/
https://www.ncbi.nlm.nih.gov/pubmed/34017671
http://dx.doi.org/10.1109/access.2021.3076362
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author COOK, DIANE J.
SCHMITTER-EDGECOMBE, MAUREEN
author_facet COOK, DIANE J.
SCHMITTER-EDGECOMBE, MAUREEN
author_sort COOK, DIANE J.
collection PubMed
description Advances in machine learning and low-cost, ubiquitous sensors offer a practical method for understanding the predictive relationship between behavior and health. In this study, we analyze this relationship by building a behaviorome, or set of digital behavior markers, from a fusion of data collected from ambient and wearable sensors. We then use the behaviorome to predict clinical scores for a sample of n = 21 participants based on continuous data collected from smart homes and smartwatches and automatically labeled with corresponding activity and location types. To further investigate the relationship between domains, including participant demographics, self-report and external observation-based health scores, and behavior markers, we propose a joint inference technique that improves predictive performance for these types of high-dimensional spaces. For our participant sample, we observe correlations ranging from small to large for the clinical scores. We also observe an improvement in predictive performance when multiple sensor modalities are used and when joint inference is employed.
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spelling pubmed-81329712021-05-19 Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores COOK, DIANE J. SCHMITTER-EDGECOMBE, MAUREEN IEEE Access Article Advances in machine learning and low-cost, ubiquitous sensors offer a practical method for understanding the predictive relationship between behavior and health. In this study, we analyze this relationship by building a behaviorome, or set of digital behavior markers, from a fusion of data collected from ambient and wearable sensors. We then use the behaviorome to predict clinical scores for a sample of n = 21 participants based on continuous data collected from smart homes and smartwatches and automatically labeled with corresponding activity and location types. To further investigate the relationship between domains, including participant demographics, self-report and external observation-based health scores, and behavior markers, we propose a joint inference technique that improves predictive performance for these types of high-dimensional spaces. For our participant sample, we observe correlations ranging from small to large for the clinical scores. We also observe an improvement in predictive performance when multiple sensor modalities are used and when joint inference is employed. 2021-04-28 2021 /pmc/articles/PMC8132971/ /pubmed/34017671 http://dx.doi.org/10.1109/access.2021.3076362 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
COOK, DIANE J.
SCHMITTER-EDGECOMBE, MAUREEN
Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores
title Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores
title_full Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores
title_fullStr Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores
title_full_unstemmed Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores
title_short Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores
title_sort fusing ambient and mobile sensor features into a behaviorome for predicting clinical health scores
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132971/
https://www.ncbi.nlm.nih.gov/pubmed/34017671
http://dx.doi.org/10.1109/access.2021.3076362
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