Computational Topology to Monitor Human Occupancy

The recent advances in sensing technologies, embedded systems, and wireless communication technologies, make it possible to develop smart systems to monitor human activities continuously. The occupancy of specific areas or rooms in a smart building is an important piece of information, to infer the behavior of people, or to trigger an advanced surveillance module. We propose a method based on computational topology to infer the occupancy of a room monitored for a week by a system of low-cost sensors.

[1]  R. Ghrist.  Barcodes: The persistent topology of data , 2007 .

[2]  Farah Hani Nordin,et al.  Identification of electrical appliances using non-intrusive magnetic field and probabilistic neural network (PNN) , 2014, 2014 IEEE International Conference on Power and Energy (PECon).

[3]  Silvia Santini,et al.  Occupancy Detection from Electricity Consumption Data , 2013, BuildSys@SenSys.

[4]  Afra Zomorodian,et al.  Computing Persistent Homology , 2004, SCG '04.

[5]  Rodrigo Fernandes de Mello,et al.  Persistent homology for time series and spatial data clustering , 2015, Expert Syst. Appl..

[6]  Jose A. Perea,et al.  Sliding Windows and Persistence: An Application of Topological Methods to Signal Analysis , 2013, Found. Comput. Math..

[7]  Vin de Silva,et al.  On the Local Behavior of Spaces of Natural Images , 2007, International Journal of Computer Vision.

[8]  Paolo Barsocchi,et al.  Are you in or out? Monitoring the human behavior through an occupancy strategy , 2016, 2016 IEEE Symposium on Computers and Communication (ISCC).

[9]  Mason A. Porter,et al.  A roadmap for the computation of persistent homology , 2015, EPJ Data Science.

[10]  Nataša Jonoska,et al.  Discrete and Topological Models in Molecular Biology , 2014, Natural Computing Series.

[11]  Vin de Silva,et al.  Coverage in sensor networks via persistent homology , 2007 .

[12]  A. Schoofs,et al.  Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor , 2010, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[13]  Prashant J. Shenoy,et al.  Non-Intrusive Occupancy Monitoring using Smart Meters , 2013, BuildSys@SenSys.

[14]  Daniela Giorgi,et al.  Describing shapes by geometrical-topological properties of real functions , 2008, CSUR.

[15]  Paolo Barsocchi,et al.  EMS@CNR: An Energy monitoring sensor network infrastructure for in-building location-based services , 2014, 2014 International Conference on High Performance Computing & Simulation (HPCS).

[16]  Paolo Barsocchi,et al.  Monitoring elderly behavior via indoor position-based stigmergy , 2015, Pervasive Mob. Comput..

[17]  Silvia Biasotti,et al.  Mathematical Tools for Shape Analysis and Description , 2014, Synthesis Lectures on Computer Graphics and Animation.

[18]  Gennady L. Andrienko,et al.  Extracting Events from Spatial Time Series , 2010, 2010 14th International Conference Information Visualisation.

[19]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.

[20]  Mikael Vejdemo-Johansson,et al.  javaPlex: A Research Software Package for Persistent (Co)Homology , 2014, ICMS.

[21]  H. Edelsbrunner,et al.  Persistent Homology — a Survey , 2022 .