Probabilistic-Neurosymbolic Methods for Activity Recognition The digital transformation of ambient environments relies on autonomous systems capable of interpreting complex human behavior. However, activity recognition from multi-modal, noisy sensor data faces significant hurdles: real-world domains are intrinsically hybrid, blending discrete elements, such as specific activity types, with continuous variables like spatial position. Over time, the interaction of these variables produces an exponentially growing set of possible states, often exceeding the memory constraints of standard hardware.
https://www.haiml.informatik.uni-rostock.de/research/projects/probnesy4activities/