Zobrazeno 1 - 10
of 544
pro vyhledávání: '"Liu Mengxi"'
Autor:
Bello, Hymalai, Geißler, Daniel, Ray, Lala, Müller-Divéky, Stefan, Müller, Peter, Kittrell, Shannon, Liu, Mengxi, Zhou, Bo, Lukowicz, Paul
Artificial Intelligence (AI) methods are powerful tools for various domains, including critical fields such as avionics, where certification is required to achieve and maintain an acceptable level of safety. General solutions for safety-critical syst
Externí odkaz:
http://arxiv.org/abs/2409.08666
In the human activity recognition research area, prior studies predominantly concentrate on leveraging advanced algorithms on public datasets to enhance recognition performance, little attention has been paid to executing real-time kitchen activity r
Externí odkaz:
http://arxiv.org/abs/2409.06341
Autor:
Bian, Sizhen, Kang, Pixi, Moosmann, Julian, Liu, Mengxi, Bonazzi, Pietro, Rosipal, Roman, Magno, Michele
Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have garnered significant interest across various domains, including rehabilitation and robotics. Despite advancements in neural network-based EEG decoding, maintaining performance acr
Externí odkaz:
http://arxiv.org/abs/2409.00083
Despite the widespread integration of ambient light sensors (ALS) in smart devices commonly used for screen brightness adaptation, their application in human activity recognition (HAR), primarily through body-worn ALS, is largely unexplored. In this
Externí odkaz:
http://arxiv.org/abs/2408.09527
In this work, we explore the use of a novel neural network architecture, the Kolmogorov-Arnold Networks (KANs) as feature extractors for sensor-based (specifically IMU) Human Activity Recognition (HAR). Where conventional networks perform a parameter
Externí odkaz:
http://arxiv.org/abs/2406.11914
This work proposes an incremental learning (IL) framework for wearable sensor human activity recognition (HAR) that tackles two challenges simultaneously: catastrophic forgetting and non-uniform inputs. The scalable framework, iKAN, pioneers IL with
Externí odkaz:
http://arxiv.org/abs/2406.01646
Hand-over-face gestures can provide important implicit interactions during conversations, such as frustration or excitement. However, in situations where interlocutors are not visible, such as phone calls or textual communication, the potential meani
Externí odkaz:
http://arxiv.org/abs/2403.18433
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
Due to the fact that roughly sixty percent of the human body is essentially composed of water, the human body is inherently a conductive object, being able to, firstly, form an inherent electric field from the body to the surroundings and secondly, d
Externí odkaz:
http://arxiv.org/abs/2401.06000
Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with high-frequency sampling rates usually improves the results, it often le
Externí odkaz:
http://arxiv.org/abs/2401.05426