Real Time Anomaly Detection
Royal Institute of Technology (KTH), Sweden
Prof. Šarūnas Girdzijauskas/Prof. Christer Norström / Dr. Mohammed El-Beltagy
Feature Selection, Anomaly Detection Classification, Personalization, Uncertainity Quantification, Machine Learning
a)Bsc. Electronic Engineering at Polytechinic University of Tirana, Albania.
b)Msc. Smart System Integration at Heriot-Watt University, Edinburgh, UK.
Machine Learning, Image/Signal Processing
Machine learning is undoubtedly a revolutionary technology in closing the gap between personalized services and health care. At the moment, the research activity undertaken at the RAIS program is developing advanced (deep) machine learning systems capable of early prediction of potential deterioration of leg (knee) status. This continuous risk prediction of upcoming injuries, especially in athletes who have undergone in the past some knee surgery, could mitigate any re-occurrence of adverse events at the same time comforts the recovery period.
The current roadmap for this issue contains a combination of recurrent neural networks and model-based reinforcement learning. A follow-up within the scope of injury prevention is also a personalized guidance system able to provide continuous recommendations for potentially risky situations.