activity
20182022
most citedSkeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural Networks

74 citations · 141 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2022★ 25 cited

Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network with Attention

Chunzhuo Wang, T. Sunil Kumar, Walter De Raedt +3

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potent…

cs.CV2022★ 74 cited

Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural Networks

Benjamin Filtjens, Bart Vanrumste, Peter Slaets

The ability to identify and temporally segment fine-grained actions in motion capture sequences is crucial for applications in human movement analysis. Motion capture is typically…

cs.CV2021★ 42 cited

Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks

Benjamin Filtjens, Pieter Ginis, Alice Nieuwboer +2

Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess…

eess.AS2018

A multi-layered energy consumption model for smart wireless acoustic sensor networks

Gert Dekkers, Fernando Rosas, Steven Lauwereins +6

Smart sensing is expected to become a pervasive technology in smart cities and environments of the near future. These services are improving their capabilities due to integrated de…

eess.AS2018

DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics

Gert Dekkers, Lode Vuegen, Toon van Waterschoot +2

The DCASE 2018 Challenge consists of five tasks related to automatic classification and detection of sound events and scenes. This paper presents the setup of Task 5 which includes…