Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Teijeiro, Tomás"'
Autor:
Albini, Stefano, Orlandic, Lara, Dan, Jonathan, Thevenot, Jérôme, Teijeiro, Tomas, Constantinescu, Denisa Andreea, Atienza, David
Continuous cough monitors can greatly aid doctors in home monitoring and treatment of respiratory diseases. Although many algorithms have been proposed, they still face limitations in data privacy and short-term monitoring. Edge-AI offers a promising
Externí odkaz:
http://arxiv.org/abs/2410.24066
Autor:
Orlandic, Lara, Dan, Jonathan, Thevenot, Jerome, Teijeiro, Tomas, Sauty, Alain, Atienza, David
Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Learning (ML) algorithms are promising for quantifying daily coughs, provi
Externí odkaz:
http://arxiv.org/abs/2406.01529
Autor:
Kechris, Christodoulos, Thevenot, Jerome, Teijeiro, Tomas, Stadelmann, Vincent A., Maffiuletti, Nicola A., Atienza, David
Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical
Externí odkaz:
http://arxiv.org/abs/2405.15085
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over uniform and
Externí odkaz:
http://arxiv.org/abs/2304.01802
Epilepsy is a chronic neurological disorder with a significant prevalence. However, there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing i
Externí odkaz:
http://arxiv.org/abs/2303.14745
Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients. Despite advances in machine learning and IoT, small, nonstigmatizing wearable devices for
Externí odkaz:
http://arxiv.org/abs/2302.10672
Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with such contagious diseases, many research teams have turned to crowdsourci
Externí odkaz:
http://arxiv.org/abs/2209.04360
Autor:
Baghersalimi, Saleh, Amirshahi, Alireza, Forooghifar, Farnaz, Teijeiro, Tomas, Aminifar, Amir, Atienza, David
Integrating low-power wearable Internet of Things (IoT) systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple b
Externí odkaz:
http://arxiv.org/abs/2208.00885
Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverabl
Externí odkaz:
http://arxiv.org/abs/2207.01856
HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other Machine Learning (ML) a
Externí odkaz:
http://arxiv.org/abs/2206.04746