3 citations · 3 across the 10 of their papers we have counts for
7 papers · 1 filter
WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition
Maximilian Burzer, Tobias King, Till Riedel +2
Deep learning has become the dominant paradigm in Wearable Human Activity Recognition (WHAR), yet progress is obscured by a comparability crisis. Results are often reported using i…
BayaHAR: Lightweight Bayesian Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition
Maximilian Burzer, Till Riedel, Michael Beigl +1
Sensor-based Human Activity Recognition (HAR) models often degrade on unseen users due to domain shifts caused by individual movement patterns and sensor placement. Practical weara…
Standardizing Your Training Process for Human Activity Recognition Models: A Comprehensive Review in the Tunable Factors
Yiran Huang, Haibin Zhao, Yexu Zhou +2
In recent years, deep learning has emerged as a potent tool across a multitude of domains, leading to a surge in research pertaining to its application in the wearable human activi…
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning
Yiran Huang, Yexu Zhou, Till Riedel +2
Deep learning has proven to be an effective approach in the field of Human activity recognition (HAR), outperforming other architectures that require manual feature engineering. De…
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning
Yiran Huang, Yexu Zhou, Michael Hefenbrock +3
The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficia…
McXai: Local model-agnostic explanation as two games
Yiran Huang, Nicole Schaal, Michael Hefenbrock +4
To this day, a variety of approaches for providing local interpretability of black-box machine learning models have been introduced. Unfortunately, all of these methods suffer from…
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone
Yexu Zhou, Yuting Gao, Yiran Huang +3
An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method,…