2 citations · 2 across the 4 of their papers we have counts for
6 papers
MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition
Marius Bock, Yuwei Zhang, Juergen Gall +3
Despite the ubiquity of sensors in wearable and mobile devices and the abundance of human movement data they generate, translating unlabeled recordings into foundational motion mod…
-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition
Mishal Fatima, Shashank Agnihotri, Marius Bock +4
Most pattern recognition models are developed on pre-proce\-ssed data. In computer vision, for instance, RGB images processed through image signal processing (ISP) pipelines design…
Label Leakage in Federated Inertial-based Human Activity Recognition
Marius Bock, Maximilian Hopp, Kristof Van Laerhoven +1
While prior work has shown that Federated Learning updates can leak sensitive information, label reconstruction attacks, which aim to recover input labels from shared gradients, ha…
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
Marius Bock, Juergen Gall, Michael Moeller +1
Despite recognized limitations in modeling long-range temporal dependencies, Human Activity Recognition (HAR) has traditionally relied on a sliding window approach to segment label…
Weak-Annotation of HAR Datasets using Vision Foundation Models
Marius Bock, Kristof Van Laerhoven, Michael Moeller
As wearable-based data annotation remains, to date, a tedious, time-consuming task requiring researchers to dedicate substantial time, benchmark datasets within the field of Human…
Temporal Action Localization for Inertial-based Human Activity Recognition
Marius Bock, Michael Moeller, Kristof Van Laerhoven
As of today, state-of-the-art activity recognition from wearable sensors relies on algorithms being trained to classify fixed windows of data. In contrast, video-based Human Activi…