Introduces and proves stability for a persistent homology-based bi-conditional periodicity score for pairwise time series similarity.
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Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.
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A Stable Measure of Similarity for Time Series using Persistent Homology
Introduces and proves stability for a persistent homology-based bi-conditional periodicity score for pairwise time series similarity.
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Human Centered Non Intrusive Driver State Modeling Using Personalized Physiological Signals in Real World Automated Driving
Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.