Zobrazeno 1 - 10 of 1 297 pro vyhledávání: '"Brage Soren"'
1
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UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction
Autor: Wu, Yu, Spathis, Dimitris, Jia, Hong, Perez-Pozuelo, Ignacio, Gonzales, Tomas, Brage, Soren, Wareham, Nicholas, Mascolo, Cecilia
Deep learning models have shown great promise in various healthcare monitoring applications. However, most healthcare datasets with high-quality (gold-standard) labels are small-scale, as directly collecting ground truth is often costly and time-cons
Externí odkaz: http://arxiv.org/abs/2307.16651
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2
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Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction
Autor: Wu, Yu, Spathis, Dimitris, Jia, Hong, Perez-Pozuelo, Ignacio, Gonzales, Tomas I., Brage, Soren, Wareham, Nicholas, Mascolo, Cecilia
Deep learning models have shown great promise in various healthcare applications. However, most models are developed and validated on small-scale datasets, as collecting high-quality (gold-standard) labels for health applications is often costly and
Externí odkaz: http://arxiv.org/abs/2211.10475
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3
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Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments
Autor: Spathis, Dimitris, Perez-Pozuelo, Ignacio, Gonzales, Tomas I., Wu, Yu, Brage, Soren, Wareham, Nicholas, Mascolo, Cecilia
Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO$_{2}max$), or indirectly assessed using heart rate responses to standard exercise tests. However,
Externí odkaz: http://arxiv.org/abs/2205.03116
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8
Report
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data
Autor: Tang, Chi Ian, Perez-Pozuelo, Ignacio, Spathis, Dimitris, Brage, Soren, Wareham, Nick, Mascolo, Cecilia
Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition. However, the performance of such models in real-world settings largely depends on the availability of large datasets tha
Externí odkaz: http://arxiv.org/abs/2102.06073
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9
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Self-supervised transfer learning of physiological representations from free-living wearable data
Autor: Spathis, Dimitris, Perez-Pozuelo, Ignacio, Brage, Soren, Wareham, Nicholas J., Mascolo, Cecilia
Wearable devices such as smartwatches are becoming increasingly popular tools for objectively monitoring physical activity in free-living conditions. To date, research has primarily focused on the purely supervised task of human activity recognition,
Externí odkaz: http://arxiv.org/abs/2011.12121
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10
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Learning Generalizable Physiological Representations from Large-scale Wearable Data
Autor: Spathis, Dimitris, Perez-Pozuelo, Ignacio, Brage, Soren, Wareham, Nicholas J., Mascolo, Cecilia
To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited success in inferring high-level health outcomes from low-level signal
Externí odkaz: http://arxiv.org/abs/2011.04601
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Plný text Recenzováno Digitální knihovna AV ČR
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