Your browser doesn't support javascript.
loading
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.
Afiliación
  • Tiwari A; Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada.
  • Cassani R; Myant Inc., Toronto, ON M9W 1B6, Canada.
  • Kshirsagar S; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada.
  • Tobon DP; Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada.
  • Zhu Y; Faculty of Engineering, Universidad de Medellín, Medellín 050026, Colombia.
  • Falk TH; Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada.
Sensors (Basel) ; 22(12)2022 Jun 17.
Article en En | MEDLINE | ID: mdl-35746361
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.
Asunto(s)
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Dispositivos Electrónicos Vestibles Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Dispositivos Electrónicos Vestibles Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article