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Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡
While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing—significantly increasing both size and power consumption. Here, a wearable biosensor...
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/PMC6603545/ https://www.ncbi.nlm.nih.gov/pubmed/31146358 http://dx.doi.org/10.3390/s19112450 |
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author | Pope, Gunnar C. Halter, Ryan J. |
author_facet | Pope, Gunnar C. Halter, Ryan J. |
author_sort | Pope, Gunnar C. |
collection | PubMed |
description | While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing—significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term storage within the limited memory of a 16-bit microcontroller. The distortion of the compressed signal and errors in extracting common EDA features is evaluated across 253 independent EDA signals acquired from human volunteers. At a compression ratio (CR) of 23.3×, the root mean square error (RMSErr) is below 0.016 [Formula: see text] S and the percent root-mean-square difference (PRD) is below 1%. Tonic EDA features are preserved at a CR = 23.3× while phasic EDA features are more prone to reconstruction errors at CRs > 8.8×. This compression method is shown to be competitive with other compressive sensing-based approaches for EDA measurement while enabling on-board access to raw EDA data and efficient signal reconstructions. The system and compression method provided improves the functionality of low-resource microcontrollers by limiting the need for external memory devices and wireless connectivity to advance the miniaturization of wearable biosensors for mobile applications. |
format | Online Article Text |
id | pubmed-6603545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66035452019-07-19 Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡ Pope, Gunnar C. Halter, Ryan J. Sensors (Basel) Article While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing—significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term storage within the limited memory of a 16-bit microcontroller. The distortion of the compressed signal and errors in extracting common EDA features is evaluated across 253 independent EDA signals acquired from human volunteers. At a compression ratio (CR) of 23.3×, the root mean square error (RMSErr) is below 0.016 [Formula: see text] S and the percent root-mean-square difference (PRD) is below 1%. Tonic EDA features are preserved at a CR = 23.3× while phasic EDA features are more prone to reconstruction errors at CRs > 8.8×. This compression method is shown to be competitive with other compressive sensing-based approaches for EDA measurement while enabling on-board access to raw EDA data and efficient signal reconstructions. The system and compression method provided improves the functionality of low-resource microcontrollers by limiting the need for external memory devices and wireless connectivity to advance the miniaturization of wearable biosensors for mobile applications. MDPI 2019-05-29 /pmc/articles/PMC6603545/ /pubmed/31146358 http://dx.doi.org/10.3390/s19112450 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 Pope, Gunnar C. Halter, Ryan J. Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡ |
title | Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡ |
title_full | Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡ |
title_fullStr | Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡ |
title_full_unstemmed | Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡ |
title_short | Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡ |
title_sort | design and implementation of an ultra-low resource electrodermal activity sensor for wearable applications ‡ |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603545/ https://www.ncbi.nlm.nih.gov/pubmed/31146358 http://dx.doi.org/10.3390/s19112450 |
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