The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 70833 Ostrava Poruba Czech Republic

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Vanus J., Belesova J., Martinek R., Nedoma J., Fajkus M., Bilik P., Zidek J. Monitoring of the daily living activities in smart home care. Hum. Cent. Comput. Inf. Sci. 2017;7:30. doi: 10.1186/s13673-017-0113-6. DOI

Panagopoulos C., Menychtas A., Tsanakas P., Maglogiannis I. Increasing Usability of Homecare Applications for Older Adults: A Case Study. Designs. 2019;3:23. doi: 10.3390/designs3020023. DOI

Loukatos D., Arvanitis K.G., Armonis N. International Conference on Human Interaction and Emerging Technologies. Springer; Cham, Switzerland: 2019. Investigating Educationally Fruitful Speech-Based Methods to Assist People with Special Needs to Care Potted Plants; pp. 157–162.

Wiljer D., Hakim Z. Developing an artificial intelligence–enabled health care practice: Rewiring health care professions for better care. J. Med. Imaging Radiat. Sci. 2019;50:S8–S14. doi: 10.1016/j.jmir.2019.09.010. PubMed DOI

Sadreazami H., Bolic M., Rajan S. Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network. IEEE Trans. Circuits Syst. II Express Briefs. 2020;67:197–201. doi: 10.1109/TCSII.2019.2904498. DOI

Ahamed F., Shahrestani S., Cheung H. Intelligent Fall Detection with Wearable IoT. Adv. Intell. Syst. Comput. 2020;993:391–401. doi: 10.1007/978-3-030-22354-0_35. DOI

Dhiraj, Manekar R., Saurav S., Maiti S., Singh S., Chaudhury S., Neeraj, Kumar R., Chaudhary K. Activity Recognition for Indoor Fall Detection in 360-Degree Videos Using Deep Learning Techniques. Adv. Intell. Syst. Comput. 2020;1024:417–429. doi: 10.1007/978-981-32-9291-8_33. DOI

Hsueh Y.-L., Lie W.-N., Guo G.-Y. Human Behavior Recognition from Multiview Videos. Inf. Sci. 2020;517:275–296. doi: 10.1016/j.ins.2020.01.002. DOI

Szczurek A., Maciejewska M., Pietrucha T. Occupancy determination based on time series of CO2 concentration, temperature and relative humidity. Energy Build. 2017;147:142–154. doi: 10.1016/j.enbuild.2017.04.080. DOI

Vanus J., Machac J., Martinek R., Bilik P., Zidek J., Nedoma J., Fajkus M. The design of an indirect method for the human presence monitoring in the intelligent building. Hum. Cent. Comput. Inf. Sci. 2018;8:28. doi: 10.1186/s13673-018-0151-8. DOI

Vanus J., Kubicek J., Gorjani O.M., Koziorek J. Using the IBMSPSS SWTool withWavelet Transformation for CO2 Prediction within IoT in Smart Home Care. Sensors. 2019;19:1407. doi: 10.3390/s19061407. PubMed DOI PMC

Vanus J.M., Gorjani O., Bilik P. Novel Proposal for Prediction of CO2 Course and Occupancy Recognition in Intelligent Buildings within IoT. Energies. 2019;12:4541. doi: 10.3390/en12234541. DOI

Albert M.V., Toledo S., Shapiro M., Koerding K. Using mobile phones for activity recognition in Parkinson’s patients. Front. Neurol. 2012;3:158. doi: 10.3389/fneur.2012.00158. PubMed DOI PMC

Nweke H.F., Teh Y.W., Al-Garadi M.A., Alo U.R. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Syst. Appl. 2018;105:233–261. doi: 10.1016/j.eswa.2018.03.056. DOI

Lara O.D., Labrador M.A. A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 2012;15:1192–1209. doi: 10.1109/SURV.2012.110112.00192. DOI

Yousefi S., Narui H., Dayal S., Ermon S., Valaee S. A survey on behavior recognition using wifi channel state information. IEEE Commun. Mag. 2017;55:98–104. doi: 10.1109/MCOM.2017.1700082. DOI

Minarno A.E., Kusuma W.A., Wibowo H. Performance Comparisson Activity Recognition using Logistic Regression and Support Vector Machine; Proceedings of the 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS); Singapore. 26–29 February 2020; pp. 19–24.

Kwapisz J.R., Weiss G.M., Moore S.A. Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newsl. 2011;12:74–82. doi: 10.1145/1964897.1964918. DOI

Bayat A., Pomplun M., Tran D.A. A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sci. 2014;34:450–457. doi: 10.1016/j.procs.2014.07.009. DOI

Trost S.G., Zheng Y., Wong W.K. Machine learning for activity recognition: Hip versus wrist data. Physiol. Meas. 2014;35:2183. doi: 10.1088/0967-3334/35/11/2183. PubMed DOI

European Committee for Standards . Home and Building Electronic System (HBES) European Committee for Standards; Brussels, Belgium: 2005. Cenelec EN50090.

International Organization for Standardization . K.N.X. Standard ISO/IEC14543-3. International Organization for Standardization; Geneva, Switzerland: 2006.

NGIMU X-Io Technologies. [(accessed on 13 August 2020)]; Available online: https://x-io.co.uk/ngimu/

Rantalainen T., Karavirta L., Pirkola H., Rantanen T., Linnamo V. Gait Variability UsingWaist- and Ankle-Worn Inertial Measurement Units in Healthy Older Adults. Sensors. 2020;20:2858. doi: 10.3390/s20102858. PubMed DOI PMC

Fida B., Bernabucci I., Bibbo D., Conforto S., Schmid M. Pre-processing effect on the accuracy of event-based activity segmentation and classification through inertial sensors. Sensors. 2015;15:23095–23109. doi: 10.3390/s150923095. PubMed DOI PMC

Alankar B., Yousf N., Ahsaan S.U. Advances in Intelligent Systems and Computing. Volume 1030. Springer; Berlin/Heidelberg, Germany: 2020. Predictive Analytics for Weather Forecasting using Back Propagation and Resilient Back Propagation Neural Networks; pp. 99–115. DOI

Poornima S., Pushpalatha M. Prediction of rainfall using intensified LSTM based recurrent Neural Network with Weighted Linear Units. Atmosphere. 2019;10:668. doi: 10.3390/atmos10110668. DOI

Pooja S.B., Siva Balan R.V. Linear program boosting classification with remote sensed big data for weather forecasting. Int. J. Adv. Trends Comput. Sci. Eng. 2019;8:1405–1415. doi: 10.30534/ijatcse/2019/58842019. DOI

Yang Y., Hu R., Sun G., Qiu C. Chinese Spam Data Filter Model in Mobile Internet; Proceedings of the International Conference on Advanced Communication Technology ICACT; Xi’an, China. 17–20 February 2019; pp. 339–343. DOI

Maguluri L.P., Ragupathy R., Buddi S.R.K., Ponugoti V., Kalimil T.S. Adaptive Prediction of Spam Emails Using Bayesian Inference; Proceedings of the 3rd International Conference on Computing Methodologies and Communication ICCMC; Erode, India. 27–29 March 2019; pp. 628–632. DOI

Mansourbeigi S.M.-H. Stochastic Methods to Find Maximum Likelihood for Spam E-mail Classification. Adv. Intell. Syst. Comput. 2019;927:623–632. doi: 10.1007/978-3-030-15035-8_60. DOI

Mallik R., Sahoo A.K. A novel approach to spam filtering using semantic based naive bayesian classifier in text analytics. Adv. Intell. Syst. Comput. 2019;813:301–309. doi: 10.1007/978-981-13-1498-8_27. DOI

Van Nguyen T., Zhou L., Chong A.Y.L., Li B., Pu X. Predicting customer demand for remanufactured products: A data-mining approach. Eur. J. Oper. Res. 2019;281:543–558. doi: 10.1016/j.ejor.2019.08.015. DOI

Liu C., Lei Z., Morley D., Abourizk S.M. Dynamic, Data-Driven Decision-Support Approach for Construction Equipment Acquisition and Disposal. J. Comput. Civ. Eng. 2020;34:34. doi: 10.1061/(ASCE)CP.1943-5487.0000871. DOI

Wang J., Lai X., Zhang S., Wang W.M., Chen J. Predicting customer absence for automobile 4S shops: A lifecycle perspective. Eng. Appl. Artif. Intell. 2020;89 doi: 10.1016/j.engappai.2019.103405. DOI

Park G., Song M. Predicting performances in business processes using deep neural networks. Decis. Support. Syst. 2020;129:113191. doi: 10.1016/j.dss.2019.113191. DOI

Sarno R., Sinaga F., Sungkono K.R. Anomaly detection in business processes using process mining and fuzzy association rule learning. J. Big Data. 2020;7:5. doi: 10.1186/s40537-019-0277-1. DOI

Matos T., Macedo J.A., Lettich F., Monteiro J.M., Renso C., Perego R., Nardini F.M. Leveraging feature selection to detect potential tax fraudsters. Expert Syst. Appl. 2020;145:113128. doi: 10.1016/j.eswa.2019.113128. DOI

Shi C., Li X., Lv J., Yin J., Mumtaz I. Robust geodesic based outlier detection for class imbalance problem. Pattern Recognit. Lett. 2020;131:428–434. doi: 10.1016/j.patrec.2020.01.028. DOI

Han J., Pei J., Kamber M. Data Mining: Concepts and Techniques. Elsevier; Waltham, MA, USA: 2011.

Kachalsky I., Zakirzyanov I., Ulyantsev V. Applying Reinforcement Learning and Supervised Learning Techniques to Play Hearthstone; Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA); Cancun, Mexico. 18–21 December 2017; pp. 1145–1148.

Nijhawan R., Srivastava I., Shukla P. Land cover classification using super-vised and unsupervised learning techniques; Proceedings of the 2017 International Conference on Computational Intelligence in Data Science (ICCIDS); Chennai, India. 2–3 June 2017; pp. 1–6.

Liu Q., Liao X., Carin L. Semi-Supervised Life-Long Learning with Application to Sensing; Proceedings of the 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing; St. Thomas, VI, USA. 12–14 December 2007; pp. 1–4.

Yan X. Linear Regression Analysis: Theory and Computing. World Scientific; Hackensack, NJ, USA: 2009. pp. 1–2.

Rencher A.C., Christensen W.F. Wiley Series in Probability and Statistics. 3rd ed. Volume 709. John Wiley & Sons; Hoboken, NJ, USA: 2012. Chapter 10, Multivariate regression—Section 10.1 Introduction Methods of Multivariate Analysis; p. 19.

Tolles J., Meurer W.J. Logistic regression: Relating patient characteristics to outcomes. JAMA. 2016;316:533–534. doi: 10.1001/jama.2016.7653. PubMed DOI

Boyd C.R., Tolson M.A., Copes W.S. Evaluating trauma care: The TRISS method Trauma Score and the Injury Severity Score. J. Trauma. 1987;27:370–378. doi: 10.1097/00005373-198704000-00005. PubMed DOI

Le Gall J.R., Lemeshow S., Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270:2957–2963. doi: 10.1001/jama.1993.03510240069035. PubMed DOI

Berry M.J., Linoff G.S. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. John Wiley & Sons; Hoboken, NJ, USA: 2004.

Truett J., Cornfield J., Kannel W. A multivariate analysis of the risk of coronary heart disease in Framingham. J. Chronic Dis. 1967;20:511–524. doi: 10.1016/0021-9681(67)90082-3. PubMed DOI

Cybenko G. Approximation by superpositions of a sigmoidal function. Math. Control. Signals Syst. 1989;2:303–314. doi: 10.1007/BF02551274. DOI

Freedman D.A. Statistical Models: Theory and Practice. Cambridge University Press; Cambridge, UK: 2009.

Efroymson M.A. In: Multiple Regression Analysis. Mathematical Methods for Digital Computers. Ralston A., Wilf H.S., editors. Wiley; New York, NY, USA: 1960.

Hocking R.R. The Analysis and Selection of Variables in Linear Regression. Biometrics. 1976;32:1–49. doi: 10.2307/2529336. DOI

Draper N., Smith H. Applied Regression Analysis. 2nd ed. John Wiley & Sons, Inc.; New York, NY, USA: 1981.

SAS Institute . SAS/STAT User’s Guide. 4th ed. Volume 2 SAS Institute Inc.; Cary, NC, USA: 1989. Version 6.

Knecht W.R. Technical Report DOT/FAA/AM-O5/15. Federal Aviation Administration; Oklahoma City, OK, USA: 2005. Pilot Willingness to Take off into Marginal Weather, Part II: Antecedent Overfitting with forward Stepwise Logistic Regression.

Flom P.L., Cassell D.L. Stopping Stepwise: Why Stepwise and Similar Selection Methods are Bad, and What You Should Use. NESUG; Corvallis, OR, USA: 2007.

Myers R.H., Myers R.H. Classical and Modern Regression with Applications. Volume 2 Duxbury Press; Belmont, CA, USA: 1990.

Bendel R.B., Afifi A.A. Comparison of stopping rules in forward “stepwise” regression. J. Am. Stat. Assoc. 1977;72:46–53.

Kubinyi H. Evolutionary variable selection in regression and PLS analyses. J. Chemom. 1996;10:119–133. doi: 10.1002/(SICI)1099-128X(199603)10:2<119::AID-CEM409>3.0.CO;2-4. DOI

Szumilas M. Explaining odds ratios. J. Can. Acad. Child. Adolesc. Psychiatry. 2010;19:227. PubMed PMC

Moosavi S.R., Wood D.A., Ahmadi M.A., Choubineh A. ANN-Based Prediction of Laboratory-Scale Performance of CO2-Foam Flooding for Improving Oil Recovery. Nat. Resour. Res. 2019;28:1619–1637. doi: 10.1007/s11053-019-09459-8. DOI

Zarei T., Behyad R. Predicting the water production of a solar seawater greenhouse desalination unit using multi-layer perceptron model. Sol. Energy. 2019;177:595–603. doi: 10.1016/j.solener.2018.11.059. DOI

Yilmaz I., Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst. Appl. 2011;38:5958–5966. doi: 10.1016/j.eswa.2010.11.027. DOI

Heidari E., Sobati M.A., Movahedirad S. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN) Chemom. Intell. Lab. Syst. 2016;155:73–85. doi: 10.1016/j.chemolab.2016.03.031. DOI

Abdi-Khanghah M., Bemani A., Naserzadeh Z., Zhang Z. Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN. J. CO2 Util. 2018;25:108–119. doi: 10.1016/j.jcou.2018.03.008. DOI

Ahmed S.A., Dey S., Sarma K.K. Image texture classification using Artificial Neural Network (ANN); Proceedings of the 2011 2nd National Conference on Emerging Trends and Applications in Computer Science; Shillong, India. 4–5 March 2011; pp. 1–4.

Zarei F., Baghban A. Phase behavior modelling of asphaltene precipitation utilizing MLP-ANN approach. Pet. Sci. Technol. 2017;35:2009–2015. doi: 10.1080/10916466.2017.1377233. DOI

Behrang M., Assareh E., Ghanbarzadeh A., Noghrehabadi A. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol. Energy. 2010;84:1468–1480. doi: 10.1016/j.solener.2010.05.009. DOI

Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed. Macmillan College Publishing; New York, NY, USA: 1998.

Ripley B.D. Pattern Recognition and Neural Networks. Cambridge University Press (CUP); Cambridge, UK: 1996.

Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer; New York, NY, USA: 2009.

Rosenblatt F. Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. Defense Technical Information Center (DTIC); Baer Fort, VA, USA: 1961.

Rumelhart D.E., Hinton G.E., Williams R.J. Learning Internal Representations by Error Propagation. The MIT Press; Cambridge, MA, USA: 1985.

IBM SPSS Modeler 18 Algorithms Guide. [(accessed on 1 November 2019)]; Available online: http://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/18.0/en/AlgorithmsGuide.pdf.

Human Activity Classification Using Multilayer Perceptron