activity
20232026
most citedInitial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition

2 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

LITEWAY: LIghtweight HAR via Temporal Efficient highWAY

Dominique Nshimyimana, Vitor Fortes Rey, Mengxi Liu +2

Wearable human activity recognition (HAR) remains challenging due to the computational and energy constraints of deep learning models on resource-limited devices. Existing lightwei…

cs.HC2025

Beyond the Pocket: A Large-Scale International Study on User Preferences on Bodily Placements of Commercial Wearables

Joanna Sorysz, Lars Krupp, Dominique Nshimyimana +4

As wearables become smaller, more powerful, and increasingly embedded in everyday life, their integration into diverse user contexts raises important design challenges. Despite thi…

cs.CV2025★ 1 cited

PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition

Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh +2

Human activity recognition (HAR) with deep learning models relies on large amounts of labeled data, often challenging to obtain due to associated cost, time, and labor. Self-superv…

cs.LG2024

Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets

Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey +3

The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research priorit…

cs.LG2024★ 2 cited

Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition

Mengxi Liu, Daniel Geißler, Dominique Nshimyimana +3

In this work, we explore the use of a novel neural network architecture, the Kolmogorov-Arnold Networks (KANs) as feature extractors for sensor-based (specifically IMU) Human Activ…

cs.LG2023★ 1 cited

Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR

Dominique Nshimyimana, Vitor Fortes Rey, Paul Lukowic

Machine learning algorithms are improving rapidly, but annotating training data remains a bottleneck for many applications. In this paper, we show how real data can be used for sel…