@inproceedings{renoux2018online,
title = {Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning},
author = {Jennifer Renoux and Uwe Köckemann and Amy Loutfi},
url = {http://www.jenniferrenoux.com/wp-content/uploads/2018/11/jrx_ukn_ali.pdf
http://www.jenniferrenoux.com/wp-content/uploads/2018/10/jrx_ukn_ali.pdf},
year = {2018},
date = {2018-12-14},
booktitle = {Proceedings of the 2018 European Conference on Ambient Intelligence},
address = {Larnaca, Cyprus},
abstract = {Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system’s ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. This paper presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.},
keywords = {csp, hmm, smart home},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{renoux2018simulating,
title = {Simulating Daily Activities in a Smart Home for Data Generation},
author = {Jennifer Renoux and Franziska Klügl},
url = {http://www.jenniferrenoux.com/wp-content/uploads/2018/10/main.pdf},
year = {2018},
date = {2018-12-09},
booktitle = {Proceedings of the Winter Simulation Conference},
address = {Gothenburg, Sweden},
abstract = {Smart Homes are currently one of the hottest topics in the market of Internet of Things and Augmented Living. However, in order to provide high-level intelligent solutions, algorithms need to be developed that take into account which activities the inhabitants intend to perform. Sensor data plays an essential role in their development, ranging from testing the algorithms to learning underlying rules for classifying and connecting sensor patterns and to inhabitant activities. However, currently only few and limited data sets are available. In this contribution we present concepts and solutions for generating high-quality data using a flexible agent-based simulation tool. Hereby, we integrate the simulation of a sensorized apartment with human behaviour modeling. We selected a constraint-based planning approach for producing a sequence of daily activities of a human inhabitant. The overall set-up is shown to produce data that exhibits the same relevant properties than a comparable real-world scenario and thus can be used to replace expensive data collection campaigns.},
keywords = {constraint-based planning, simulation, smart home},
pubstate = {published},
tppubtype = {inproceedings}
}