Indirect Recognition of Predefined Human Activities

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.

[1]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[2]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[3]  Duc A. Tran,et al.  The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC-2014) A Study on Human Activity Recognition Using Accelerometer Data from Smartphones , 2014 .

[4]  Wahyu Andhyka Kusuma,et al.  Performance Comparisson Activity Recognition using Logistic Regression and Support Vector Machine , 2020, 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS).

[5]  David L. Cassell,et al.  Stopping stepwise: Why stepwise and similar selection methods are bad, and what you should use , 2007 .

[6]  Vladimir Ulyantsev,et al.  Applying Reinforcement Learning and Supervised Learning Techniques to Play Hearthstone , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[7]  Venkat Reddy Konasani,et al.  Multiple Regression Analysis , 2015 .

[8]  Wen-Nung Lie,et al.  Human Behavior Recognition from Multiview Videos , 2020, Inf. Sci..

[9]  J. Orbach.  Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. , 1962 .

[10]  Lakshmana Phaneendra Maguluri,et al.  Adaptive Prediction of Spam Emails : Using Bayesian Inference , 2019, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC).

[11]  Rahul Nijhawan,et al.  Land cover classification using super-vised and unsupervised learning techniques , 2017, 2017 International Conference on Computational Intelligence in Data Science(ICCIDS).

[12]  Bhavya Alankar,et al.  Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks , 2020 .

[13]  Taleb Zarei,et al.  Predicting the water production of a solar seawater greenhouse desalination unit using multi-layer perceptron model , 2019, Solar Energy.

[14]  Sreeraman Rajan,et al.  Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[15]  Jan Nedoma,et al.  Monitoring of the daily living activities in smart home care , 2017, Human-centric Computing and Information Sciences.

[16]  A. Szczurek,et al.  Occupancy determination based on time series of CO2 concentration, temperature and relative humidity , 2017 .

[17]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[18]  Jan Vanus,et al.  Novel Proposal for Prediction of CO2 Course and Occupancy Recognition in Intelligent Buildings within IoT , 2019, Energies.

[19]  William R. Knecht.  Pilot Willingness to Take Off into Marginal Weather, Part II: Antecedent Overfitting with Forward Stepwise Logistic Regressions , 2005 .

[20]  Oguz Kaynar,et al.  Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils , 2010, Expert Syst. Appl..

[21]  R. R. Hocking.  The analysis and selection of variables in linear regression , 1976 .

[22]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[23]  W. Copes,et al.  Evaluating trauma care: the TRISS method. Trauma Score and the Injury Severity Score. , 1987, The Journal of trauma.

[24]  R. H. Myers.  Classical and modern regression with applications , 1986 .

[25]  Jirí Koziorek,et al.  Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care , 2019, Sensors.

[26]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[27]  Pooja S.B.  Linear Program Boosting Classification with Remote Sensed Big Data for Weather Forecasting , 2019 .

[28]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .

[29]  Xin Yan,et al.  Linear Regression Analysis: Theory and Computing , 2009 .

[30]  Andreas Menychtas,et al.  Increasing Usability of Homecare Applications for Older Adults: A Case Study , 2019, Designs.

[31]  Kandarpa Kumar Sarma,et al.  Image texture classification using Artificial Neural Network (ANN) , 2011, 2011 2nd National Conference on Emerging Trends and Applications in Computer Science.

[32]  W. Meurer,et al.  Logistic Regression: Relating Patient Characteristics to Outcomes. , 2016, JAMA.

[33]  Weng-Keen Wong,et al.  Machine learning for activity recognition: hip versus wrist data , 2014, Physiological measurement.

[34]  José Maria Monteiro,et al.  Leveraging feature selection to detect potential tax fraudsters , 2020, Expert Syst. Appl..

[35]  Jan Nedoma,et al.  The design of an indirect method for the human presence monitoring in the intelligent building , 2018, Human-centric Computing and Information Sciences.

[36]  Magdalena Szumilas.  Explaining odds ratios. , 2010, Journal of the Canadian Academy of Child and Adolescent Psychiatry = Journal de l'Academie canadienne de psychiatrie de l'enfant et de l'adolescent.

[37]  A. Afifi,et al.  Comparison of Stopping Rules in Forward “Stepwise” Regression , 1977 .

[38]  Alain Yee-Loong Chong,et al.  Predicting customer demand for remanufactured products: A data-mining approach , 2020, Eur. J. Oper. Res..

[39]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[40]  Gerald J. Hahn,et al.  Applied Regression Analysis (2nd Ed.) , 2012 .

[41]  Ying Wah Teh,et al.  Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges , 2018, Expert Syst. Appl..

[42]  Amin Bemani,et al.  Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN , 2018 .

[43]  Jiawen Wang,et al.  Predicting customer absence for automobile 4S shops: A lifecycle perspective , 2020, Eng. Appl. Artif. Intell..

[44]  Silvia Conforto,et al.  Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors , 2015, Sensors.

[45]  J. Cornfield,et al.  A multivariate analysis of the risk of coronary heart disease in Framingham. , 1967, Journal of chronic diseases.

[46]  Hugo Kubinyi,et al.  Evolutionary variable selection in regression and PLS analyses , 1996 .

[47]  David Wiljer,et al.  Developing an Artificial Intelligence-Enabled Health Care Practice: Rewiring Health Care Professions for Better Care. , 2019, Journal of medical imaging and radiation sciences.

[48]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. , 1993, JAMA.

[49]  Riyanarto Sarno,et al.  Anomaly detection in business processes using process mining and fuzzy association rule learning , 2020, Journal of Big Data.

[50]  Konrad Paul Kording,et al.  Using Mobile Phones for Activity Recognition in Parkinson’s Patients , 2012, Front. Neur..

[51]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[52]  Seyed M.-H. Mansourbeigi.  Stochastic Methods to Find Maximum Likelihood for Spam E-mail Classification , 2019, AINA Workshops.

[53]  Zhen Lei,et al.  Dynamic, Data-Driven Decision-Support Approach for Construction Equipment Acquisition and Disposal , 2020, J. Comput. Civ. Eng..

[54]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[55]  Hon Cheung,et al.  Intelligent Fall Detection with Wearable IoT , 2019, CISIS.

[56]  Dimitrios Loukatos,et al.  Investigating Educationally Fruitful Speech-Based Methods to Assist People with Special Needs to Care Potted Plants , 2019 .

[57]  A. Ghanbarzadeh,et al.  The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data , 2010 .

[58]  Timo Rantalainen,et al.  Gait Variability Using Waist- and Ankle-Worn Inertial Measurement Units in Healthy Older Adults , 2020, Sensors.

[59]  Elham Heidari,et al.  Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN) , 2016 .

[60]  Alireza Baghban,et al.  Phase behavior modelling of asphaltene precipitation utilizing MLP-ANN approach , 2017 .

[61]  S. Poornima,et al.  Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units , 2019, Atmosphere.

[62]  Mohammad Ali Ahmadi,et al.  ANN-Based Prediction of Laboratory-Scale Performance of CO2-Foam Flooding for Improving Oil Recovery , 2019, Natural Resources Research.

[63]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[64]  Santanu Chaudhury,et al.  Activity Recognition for Indoor Fall Detection in 360-Degree Videos Using Deep Learning Techniques , 2018, CVIP.

[65]  Yitao Yang,et al.  Chinese Spam Data Filter Model in Mobile Internet , 2019, 2019 21st International Conference on Advanced Communication Technology (ICACT).

[66]  Xuejun Liao,et al.  Semi-Supervised Life-Long Learning with Application to Sensing , 2007, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing.

[67]  Abhaya Kumar Sahoo,et al.  A Novel Approach to Spam Filtering Using Semantic Based Naive Bayesian Classifier in Text Analytics , 2019 .

[68]  Imran Mumtaz,et al.  Robust geodesic based outlier detection for class imbalance problem , 2020, Pattern Recognit. Lett..

[69]  Minseok Song,et al.  Predicting performances in business processes using deep neural networks , 2020, Decis. Support Syst..

[70]  Shahrokh Valaee,et al.  A Survey on Behavior Recognition Using WiFi Channel State Information , 2017, IEEE Communications Magazine.