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pro vyhledávání: '"Canellas, Manuel Lage"'
Autor:
Cañellas, Manuel Lage, Nguyen, Le, Mukherjee, Anirban, Casado, Constantino Álvarez, Wu, Xiaoting, Susarla, Praneeth, Sharifipour, Sasan, Jayagopi, Dinesh B., López, Miguel Bordallo
In the domain of non-contact biometrics and human activity recognition, the lack of a versatile, multimodal dataset poses a significant bottleneck. To address this, we introduce the Oulu Multi Sensing (OMuSense-23) dataset that includes biosignals ob
Externí odkaz:
http://arxiv.org/abs/2407.06137
Autor:
Nguyen, Le Ngu, Casado, Constantino Álvarez, Cañellas, Manuel Lage, Mukherjee, Anirban, Nguyen, Nhi, Jayagopi, Dinesh Babu, López, Miguel Bordallo
Radio frequency (RF) signals have facilitated the development of non-contact human monitoring tasks, such as vital signs measurement, activity recognition, and user identification. In some specific scenarios, an RF signal analysis framework may prior
Externí odkaz:
http://arxiv.org/abs/2401.05538
Autor:
Nguyen, Le Ngu, Susarla, Praneeth, Mukherjee, Anirban, Cañellas, Manuel Lage, Casado, Constantino Álvarez, Wu, Xiaoting, Olli~Silvén, Jayagopi, Dinesh Babu, López, Miguel Bordallo
Indoor human monitoring systems leverage a wide range of sensors, including cameras, radio devices, and inertial measurement units, to collect extensive data from users and the environment. These sensors contribute diverse data modalities, such as vi
Externí odkaz:
http://arxiv.org/abs/2312.07601
Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes
Autor:
Casado, Constantino Álvarez, Cañellas, Manuel Lage, Pedone, Matteo, Wu, Xiaoting, Nguyen, Le, López, Miguel Bordallo
Respiratory diseases remain a leading cause of mortality worldwide, highlighting the need for faster and more accurate diagnostic tools. This work presents a novel approach leveraging digital stethoscope technology for automatic respiratory disease c
Externí odkaz:
http://arxiv.org/abs/2309.07183
Exercise-induced fatigue resulting from physical activity can be an early indicator of overtraining, illness, or other health issues. In this article, we present an automated method for estimating exercise-induced fatigue levels through the use of th
Externí odkaz:
http://arxiv.org/abs/2309.06095
Deep learning models have shown promising results in recognizing depressive states using video-based facial expressions. While successful models typically leverage using 3D-CNNs or video distillation techniques, the different use of pretraining, data
Externí odkaz:
http://arxiv.org/abs/2212.06400
Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work a
Externí odkaz:
http://arxiv.org/abs/2206.04399
Autor:
Nguyen, Le Ngu, Susarla, Praneeth, Mukherjee, Anirban, Cañellas, Manuel Lage, Casado, Constantino Álvarez, Wu, Xiaoting, Silvén, Olli, Jayagopi, Dinesh Babu, López, Miguel Bordallo
Publikováno v:
In Information Fusion October 2024 110
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Akademický článek
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