315 citations · 531 across the 9 of their papers we have counts for
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cs.LG2019★ 139 cited
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
Mathias Kraus, Stefan Feuerriegel
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely…
cs.LG2019★ 23 cited
Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning
Martin Maritsch, Caterina Bérubé, Mathias Kraus +5
The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HR…