7 papers
HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound
Robin Burchard, Pascal-André Brückner, Marius Bock +2
With each sensing modality exhibiting inherent strengths and limitations, multi-modal approaches for wearable Human Activity Recognition (HAR) are becoming increasingly relevant --…
-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…
FedFitTech: A Baseline in Federated Learning for Fitness Tracking
Zeyneddin Oz, Shreyas Korde, Marius Bock +1
The rapid evolution of sensors and resource-efficient machine learning models has spurred the widespread adoption of wearable fitness tracking devices. Equipped with inertial senso…
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, Michael Moeller, Juergen Gall +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…
WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
Marius Bock, Hilde Kuehne, Kristof Van Laerhoven +1
Research has shown the complementarity of camera- and inertial-based data for modeling human activities, yet datasets with both egocentric video and inertial-based sensor data rema…