Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Rey, Vítor Fortes"'
Autor:
Geissler, Daniel, Nshimyimana, Dominique, Rey, Vitor Fortes, Suh, Sungho, Zhou, Bo, Lukowicz, Paul
The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research prioritizes statistical metrics over examining negative sample details. While
Externí odkaz:
http://arxiv.org/abs/2412.09037
Autor:
Fritsch, Stefan, Tschoepe, Matthias, Rey, Vitor Fortes, Krupp, Lars, Gruenerbl, Agnes, Monger, Eloise, Travenna, Sarah
Medical procedures such as venipuncture and cannulation are essential for nurses and require precise skills. Learning this skill, in turn, is a challenge for educators due to the number of teachers per class and the complexity of the task. The study
Externí odkaz:
http://arxiv.org/abs/2410.16164
Autor:
Palaiodimopoulos, Nikolaos, Frkatovic, Jasmin, Rey, Vitor Fortes, Tschöpe, Matthias, Suh, Sungho, Lukowicz, Paul, Kiefer-Emmanouilidis, Maximilian
Disordered Quantum many-body Systems (DQS) and Quantum Neural Networks (QNN) have many structural features in common. However, a DQS is essentially an initialized QNN with random weights, often leading to non-random outcomes. In this work, we emphasi
Externí odkaz:
http://arxiv.org/abs/2409.16180
Autor:
Fritsch, Stefan Gerd, Oguz, Cennet, Rey, Vitor Fortes, Ray, Lala, Kiefer-Emmanouilidis, Maximilian, Lukowicz, Paul
Human Activity Recognition (HAR) is a longstanding problem in AI with applications in a broad range of areas, including healthcare, sports and fitness, security, and more. The performance of HAR in real-world settings is strongly dependent on the typ
Externí odkaz:
http://arxiv.org/abs/2406.03857
Due to the scarcity of labeled sensor data in HAR, prior research has turned to video data to synthesize Inertial Measurement Units (IMU) data, capitalizing on its rich activity annotations. However, generating IMU data from videos presents challenge
Externí odkaz:
http://arxiv.org/abs/2406.01316
The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced
Externí odkaz:
http://arxiv.org/abs/2406.16886
Autor:
Ray, Lala Shakti Swarup, Zhou, Bo, Suh, Sungho, Krupp, Lars, Rey, Vitor Fortes, Lukowicz, Paul
In human activity recognition (HAR), the availability of substantial ground truth is necessary for training efficient models. However, acquiring ground pressure data through physical sensors itself can be cost-prohibitive, time-consuming. To address
Externí odkaz:
http://arxiv.org/abs/2402.14427
Automatic and precise fitness activity recognition can be beneficial in aspects from promoting a healthy lifestyle to personalized preventative healthcare. While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-i
Externí odkaz:
http://arxiv.org/abs/2402.09445
Machine learning algorithms are improving rapidly, but annotating training data remains a bottleneck for many applications. In this paper, we show how real data can be used for self-supervised learning without any transformations by taking advantage
Externí odkaz:
http://arxiv.org/abs/2311.12674
Autor:
Suh, Sungho, Rey, Vitor Fortes, Bian, Sizhen, Huang, Yu-Chi, Rožanec, Jože M., Ghinani, Hooman Tavakoli, Zhou, Bo, Lukowicz, Paul
Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising solution for achieving this goal, as they can provide continuous and
Externí odkaz:
http://arxiv.org/abs/2308.03514