Deep Learning for Smart Living
Smart living refers to life in a digital and networked home and living environment. Smart living thus encompasses technical systems (Internet of Things, smart home applications, advanced digital services) that are related to the topic of living. Examples of possible sample applications are assistance systems with care or emergency services based on them, security functions, the optimization of energy consumption or the improvement of comfort for residents.
Advanced smart living services can only be developed on the basis of AI methods. Deep Learning is becoming increasingly important in this context. Here, it is particularly interesting if the Deep Learning models can be executed locally on site with almost no latency and without transmitting sensitive data to external cloud systems. In this presentation, an overview of the use of resource-efficient edge AI systems for Smart Living will be given
Hendrik Wöhrle received Bachelor's and Master's degrees in Bioinformatics from the Freie Universität Berlin, a Master's degree in Electrical Engineering from the Fernuniversität Hagen and a doctor of engineering (Dr.-Ing.) from the University of Bremen. He worked as a software developer of algorithms and signal processing methods from 2007 to 2009. Since 2009, he is with the Robotics Innovation Center (RIC) of the German Research Center for Artificial Intelligence (DFKI) in Bremen. Furthermore, he is a professor of information technology at the University of Applied Sciences and Arts Dortmund since 2019. His research interests are signal processing, machine learning, embedded systems, reconfigurable computing, the Internet of Things and Smart Living.
