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Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note

Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, w...

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Autores principales: Tiwari, Abhishek, Cassani, Raymundo, Kshirsagar, Shruti, Tobon, Diana P., Zhu, Yi, Falk, Tiago H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229858/
https://www.ncbi.nlm.nih.gov/pubmed/35746361
http://dx.doi.org/10.3390/s22124579
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author Tiwari, Abhishek
Cassani, Raymundo
Kshirsagar, Shruti
Tobon, Diana P.
Zhu, Yi
Falk, Tiago H.
author_facet Tiwari, Abhishek
Cassani, Raymundo
Kshirsagar, Shruti
Tobon, Diana P.
Zhu, Yi
Falk, Tiago H.
author_sort Tiwari, Abhishek
collection PubMed
description Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.
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spelling pubmed-92298582022-06-25 Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note Tiwari, Abhishek Cassani, Raymundo Kshirsagar, Shruti Tobon, Diana P. Zhu, Yi Falk, Tiago H. Sensors (Basel) Technical Note Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms. MDPI 2022-06-17 /pmc/articles/PMC9229858/ /pubmed/35746361 http://dx.doi.org/10.3390/s22124579 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 Technical Note
Tiwari, Abhishek
Cassani, Raymundo
Kshirsagar, Shruti
Tobon, Diana P.
Zhu, Yi
Falk, Tiago H.
Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
title Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
title_full Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
title_fullStr Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
title_full_unstemmed Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
title_short Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
title_sort modulation spectral signal representation for quality measurement and enhancement of wearable device data: a technical note
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229858/
https://www.ncbi.nlm.nih.gov/pubmed/35746361
http://dx.doi.org/10.3390/s22124579
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