66 citations · 122 across the 10 of their papers we have counts for
12 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…
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, 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…
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors
Alexander Hoelzemann, Julia Lee Romero, Marius Bock +2
We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and game…
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…
Tutorial on Deep Learning for Human Activity Recognition
Marius Bock, Alexander Hoelzemann, Michael Moeller +1
Activity recognition systems that are capable of estimating human activities from wearable inertial sensors have come a long way in the past decades. Not only have state-of-the-art…
Improving Deep Learning for HAR with shallow LSTMs
Marius Bock, Alexander Hoelzemann, Michael Moeller +1
Recent studies in Human Activity Recognition (HAR) have shown that Deep Learning methods are able to outperform classical Machine Learning algorithms. One popular Deep Learning arc…