An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly

The goal of this paper is to describe the way of taking advantage of the non-intrusive indoor air quality monitoring system by using data oriented modeling technologies to determine specific human behaviors. The specific goal is to determine when a human presence occurs in a specific room, while the objective is to extend the use of the existing indoor air quality monitoring system to provide a higher level aspect of the house usage. Different models have been trained by means of machine learning algorithms using the available temperature, relative humidity and CO 2 levels to determine binary occupation. The paper will discuss the overall acceptable quality provided by those classifiers when operating over new data not previously seen. Therefore, a recommendation on how to proceed is provided, as well as the confidence level regarding the new created knowledge. Such knowledge could bring additional opportunities in the care of the elderly for specific diseases that are usually accompanied by changes in patterns of behavior.

[1]  Chen Mao,et al.  Occupancy Estimation in Smart Building using Hybrid CO2/Light Wireless Sensor Network , 2016 .

[2]  Gregor P. Henze,et al.  The performance of occupancy-based lighting control systems: A review , 2010 .

[3]  Xinrong Li,et al.  A Cost-effective Wireless Sensor Network System for Indoor Air Quality Monitoring Applications , 2014, FNC/MobiSPC.

[4]  Hannu Rintamäki,et al.  High indoor CO2 concentrations in an office environment increases the transcutaneous CO2 level and sleepiness during cognitive work , 2016, Journal of occupational and environmental hygiene.

[5]  Pietro Siciliano,et al.  People occupancy detection and profiling with 3D depth sensors for building energy management , 2015 .

[6]  Jaime Lloret Mauri,et al.  A smart communication architecture for ambient assisted living , 2015, IEEE Communications Magazine.

[7]  George Mois,et al.  A Low-Power Wireless Sensor for Online Ambient Monitoring , 2015, IEEE Sensors Journal.

[8]  A. A. Azid,et al.  WSN based indoor air quality monitoring in classrooms , 2017 .

[9]  Stéphane Ploix,et al.  Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality , 2016, ArXiv.

[10]  Ming Jin,et al.  Sensing by Proxy : Occupancy Detection Based on Indoor CO 2 Concentration , 2015 .

[11]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[12]  Felix Wortmann,et al.  Look twice: Uncover hidden information in room climate sensor data , 2014, 2014 International Conference on the Internet of Things (IOT).

[13]  Yeng Chai Soh,et al.  Environmental Sensors-Based Occupancy Estimation in Buildings via IHMM-MLR , 2017, IEEE Transactions on Industrial Informatics.

[14]  Gerhard P. Hancke,et al.  An Energy-Efficient Smart Comfort Sensing System Based on the IEEE 1451 Standard for Green Buildings , 2014, IEEE Sensors Journal.

[15]  Melanie L. Sattler,et al.  [Indoor air quality in schools]. , 2011, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[16]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[17]  Ismail Güvenç,et al.  IoT-based occupancy monitoring techniques for energy-efficient smart buildings , 2015, 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[18]  Alfred Heller,et al.  Introduction of flexible monitoring equipment into the Greenlandic building sector , 2014 .