Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Van Der Donckt, Jonas"'
This work presents the solution of the Signal Sleuths team for the 2024 HASCA WEAR challenge. The challenge focuses on detecting 18 workout activities (and the null class) using accelerometer data from 4 wearables - one worn on each limb. Data analys
Externí odkaz:
http://arxiv.org/abs/2408.03947
This work presents the solution of the Signal Sleuths team for the 2024 SHL recognition challenge. The challenge involves detecting transportation modes using shuffled, non-overlapping 5-second windows of phone movement data, with exactly one of the
Externí odkaz:
http://arxiv.org/abs/2407.11048
Autor:
Van Der Donckt, Jonas, Vandenbussche, Nicolas, Van Der Donckt, Jeroen, Chen, Stephanie, Stojchevska, Marija, De Brouwer, Mathias, Steenwinckel, Bram, Paemeleire, Koen, Ongenae, Femke, Van Hoecke, Sofie
Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable sensing technologies, particularly wrist-worn devices, offer promising solutions for longitudin
Externí odkaz:
http://arxiv.org/abs/2401.13518
Interactive line chart visualizations greatly enhance the effective exploration of large time series. Although downsampling has emerged as a well-established approach to enable efficient interactive visualization of large datasets, it is not an inher
Externí odkaz:
http://arxiv.org/abs/2307.05389
Visualization plays an important role in analyzing and exploring time series data. To facilitate efficient visualization of large datasets, downsampling has emerged as a well-established approach. This work concentrates on LTTB (Largest-Triangle-Thre
Externí odkaz:
http://arxiv.org/abs/2305.00332
Time series visualization plays a crucial role in identifying patterns and extracting insights across various domains. However, as datasets continue to grow in size, visualizing them effectively becomes challenging. Downsampling, which involves data
Externí odkaz:
http://arxiv.org/abs/2304.00900
Autor:
Van Der Donckt, Jeroen, Van Der Donckt, Jonas, Deprost, Emiel, Vandenbussche, Nicolas, Rademaker, Michael, Vandewiele, Gilles, Van Hoecke, Sofie
Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring
Externí odkaz:
http://arxiv.org/abs/2207.07753
Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To
Externí odkaz:
http://arxiv.org/abs/2206.08703
Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their applicability, as they cannot cope with irregularly-sampled or asynchronous data and
Externí odkaz:
http://arxiv.org/abs/2111.12429
Autor:
Vandenbussche, Nicolas1,2 (AUTHOR) nicolas.vandenbussche@ugent.be, Van Der Donckt, Jonas3 (AUTHOR), De Brouwer, Mathias3 (AUTHOR), Steenwinckel, Bram3 (AUTHOR), Stojchevska, Marija3 (AUTHOR), Ongenae, Femke3 (AUTHOR), Van Hoecke, Sofie3 (AUTHOR), Paemeleire, Koen1,2 (AUTHOR)
Publikováno v:
Brain & Behavior. Jan2024, Vol. 14 Issue 1, p1-10. 10p.