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Improving Actigraph Sleep/Wake Classification with Cardio-Respiratory Signals
Related publications (63)
Lecture Slides: Mathematical Foundations of Signal Processing
Matthieu Martin Jean-André Simeoni
Signal processing tools are presented from an intuitive geometric point of view which is at the heart of all modern signal processing techniques. The student will develop the mathematical depth and rigour needed for the study of advanced topics in signal p ...
2021Wearable systems for measuring sweat rate and methods of using the same
Fabien Patrick Wildhaber, Junrui Zhang, Hoël Maxime Guérin
Presented herein are systems and methods for measuring sweat rate of a subject using a wearable system. A sweat rate may be determined automatically based on one or more signals produced by a wetting sensor module in response to a presence of sweat in the ...
2021Microscale Liquid Metal Conductors for Stretchable and Transparent Electronics
Stéphanie Lacour, Ivan Furfaro, Giuseppe Schiavone, Laurent Mirko Dejace, Haotian Chen
Integrated wearable electronics capable of transducing and transmitting biophysical information on complex and dynamic systems are attracting high interest across the consumer electronics, clinical, and research domains. Gallium and gallium-based liquid me ...
2021Fourier Sampling in Signal Processing and Numerical Linear Algebra
This thesis focuses on developing efficient algorithmic tools for processing large datasets. In many modern data analysis tasks, the sheer volume of available datasets far outstrips our abilities to process them. This scenario commonly arises in tasks incl ...
EPFL2020Fourier movement primitives: an approach for learning rhythmic robot skills from demonstrations
Sylvain Calinon, Thibaut Antoine Kulak
Whether in factory or household scenarios, rhythmic movements play a crucial role in many daily-life tasks. In this paper we propose a Fourier movement primitive (FMP) representation to learn such type of skills from human demonstrations. Our approach take ...
2020Noise-Resilient and Interpretable Epileptic Seizure Detection
David Atienza Alonso, Amir Aminifar, Anthony Hitchcock Thomas
Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over ...
IEEE2020Microfluidics by Additive Manufacturing for Wearable Biosensors: A Review
Christian Enz, Sandro Carrara, Mahshid Alsadat Padash
Wearable devices are nowadays at the edge-front in both academic research as well as in industry, and several wearable devices have been already introduced in the market. One of the most recent advancements in wearable technologies for biosensing is in the ...
2020Fourier could be a data scientist: From graph Fourier transform to signal processing on graphs
Pierre Vandergheynst, Benjamin Ricaud, Nicolas Tremblay
The legacy of Joseph Fourier in science is vast, especially thanks to the essential tool that the Fourier transform is. The flexibility of this analysis, its computational efficiency and the physical interpretation it offers makes it a cornerstone in many ...
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER2019Audio Feature Extraction with Convolutional Neural Autoencoders with Application to Voice Conversion
Feature extraction is a key step in many machine learning and signal processing applications. For speech signals in particular, it is important to derive features that contain both the vocal characteristics of the speaker and the content of the speech. In ...
2019A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
Mikhail Kapralov, Amir Zandieh
Reconstructing continuous signals based on a small number of discrete samples is a fundamental problem across science and engineering. We are often interested in signals with "simple" Fourier structure - e.g., those involving frequencies within a bounded r ...
ASSOC COMPUTING MACHINERY2019