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Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses
Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patien...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387359/ https://www.ncbi.nlm.nih.gov/pubmed/30678188 http://dx.doi.org/10.3390/s19030441 |
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author | Starliper, Nathan Mohammadzadeh, Farrokh Songkakul, Tanner Hernandez, Michelle Bozkurt, Alper Lobaton, Edgar |
author_facet | Starliper, Nathan Mohammadzadeh, Farrokh Songkakul, Tanner Hernandez, Michelle Bozkurt, Alper Lobaton, Edgar |
author_sort | Starliper, Nathan |
collection | PubMed |
description | Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype. |
format | Online Article Text |
id | pubmed-6387359 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63873592019-02-26 Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses Starliper, Nathan Mohammadzadeh, Farrokh Songkakul, Tanner Hernandez, Michelle Bozkurt, Alper Lobaton, Edgar Sensors (Basel) Article Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype. MDPI 2019-01-22 /pmc/articles/PMC6387359/ /pubmed/30678188 http://dx.doi.org/10.3390/s19030441 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Starliper, Nathan Mohammadzadeh, Farrokh Songkakul, Tanner Hernandez, Michelle Bozkurt, Alper Lobaton, Edgar Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses |
title | Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses |
title_full | Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses |
title_fullStr | Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses |
title_full_unstemmed | Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses |
title_short | Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses |
title_sort | activity-aware wearable system for power-efficient prediction of physiological responses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387359/ https://www.ncbi.nlm.nih.gov/pubmed/30678188 http://dx.doi.org/10.3390/s19030441 |
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