Monitoring of the daily living activities in smart home care

One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO2, humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg–Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO2 concentrations from obtained measurements of temperature (indoor, outdoor) Ti, To and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO2, T and rH with the aim to indirectly predict CO2 leading to the elimination of CO2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015–30.6.2015). For learned ANN (1.2.2014–27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO2 for ADL monitoring in SHC.

[1]  Youtian Du,et al.  Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors , 2013 .

[2]  Chunfeng Yang,et al.  Utilizing artificial neural network to predict energy consumption and thermal comfort level: an indoor swimming pool case study , 2014 .

[3]  Milos Hammer.  Metody umělé inteligence v diagnostice elektrických strojů. , 2009 .

[4]  Stan Healy,et al.  Toward a “Smart” Nightstand Prototype: An Examination of Nightstand Table Contents and Preferences , 2011, HERD.

[5]  Junjing Yang,et al.  Predicting the CO2 levels in buildings using deterministic and identified models , 2016 .

[6]  Miguel Pinzolas,et al.  Neighborhood based Levenberg-Marquardt algorithm for neural network training , 2002, IEEE Trans. Neural Networks.

[7]  Michel Vacher,et al.  On-line human activity recognition from audio and home automation sensors: Comparison of sequential and non-sequential models in realistic Smart Homes , 2016, J. Ambient Intell. Smart Environ..

[8]  Radek Martinek,et al.  New method for accurate prediction of CO2 in the Smart Home , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[9]  Tae-Seong Kim,et al.  Human Activity Recognition via Recognized Body Parts of Human Depth Silhouettes for Residents Monitoring Services at Smart Home , 2013 .

[10]  Nacim Ramdani,et al.  Indoor human/robot localization using robust multi-modal data fusion , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[11]  N. Noury,et al.  Monitoring behavior in home using a smart fall sensor and position sensors , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[12]  Burcin Becerik-Gerber,et al.  A systematic approach to occupancy modeling in ambient sensor-rich buildings , 2014, Simul..

[13]  Il Seok Ko,et al.  A User Interface for Controlling Information Appliances in Smart Homes , 2007, KES-AMSTA.

[14]  Sonal Sharma,et al.  A voice-controlled multi-functional Smart Home Automation System , 2015, 2015 Annual IEEE India Conference (INDICON).

[15]  Amedeo Cesta,et al.  GiraffPlus: Combining social interaction and long term monitoring for promoting independent living , 2013, 2013 6th International Conference on Human System Interactions (HSI).

[16]  M.N.S. Swamy,et al.  Neural networks in a softcomputing framework , 2006 .

[17]  M Abbod,et al.  WSN and RFID Integration to Support Intelligent Monitoring in Smart Buildings Using Hybrid Intelligent Decision Support Systems , 2015 .

[18]  Muttukrishnan Rajarajan,et al.  Activity recognition in smart homes with self verification of assignments , 2015, Neurocomputing.

[19]  Bernadette Dorizzi,et al.  Human activities of daily living recognition using fuzzy logic for elderly home monitoring , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[20]  Radek Martinek,et al.  Testing of the voice communication in smart home care , 2015, Human-centric Computing and Information Sciences.

[21]  Mohammad Reza Keyvanpour,et al.  SARF: Smart activity recognition framework in Ambient Assisted Living , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[22]  Leopold,et al.  Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region , 2016 .

[23]  Joo Ghee Lim,et al.  Augmented reality control home (ARCH) for disabled and elderlies , 2015, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[24]  Juan Carlos Augusto,et al.  Considerations on uncertain spatio-temporal reasoning in smart home systems , 2006 .

[25]  Bing Dong,et al.  Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network , 2011 .

[26]  Mohammad Reza Keyvanpour,et al.  SOnAr: Smart Ontology activity recognition framework to fulfill semantic web in smart homes , 2016, 2016 Second International Conference on Web Research (ICWR).

[27]  Weiming Shen,et al.  A home mobile healthcare system for wheelchair users , 2014, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[28]  William C. Mann,et al.  Consumer Feedback on Smart Home Applications , 2007 .

[29]  Kune-Yao Chen,et al.  Practical design of intelligent remote-controller with speech-recognition and self-learning function , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[30]  Yoshinori Yaginuma,et al.  Hassle-free Sensing Technologies for Monitoring Daily Health Changes , 2014 .

[31]  Russel Torah,et al.  Prototyping a voice-controlled smart home hub wirelessly integrated with a wearable device , 2015, 2015 9th International Conference on Sensing Technology (ICST).

[32]  Bhuvana Ramabhadran,et al.  Multimodal Classification of Activities of Daily Living Inside Smart Homes , 2009, IWANN.

[33]  Mark Hasegawa-Johnson,et al.  Acoustic fall detection using Gaussian mixture models and GMM supervectors , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[34]  James R. Green,et al.  Thermal Imaging for Assisted Living at Home: Improving Kitchen Safety , 2013 .

[35]  Hugo Fuks,et al.  AmbLEDs collaborative healthcare for AAL systems , 2015, 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[36]  Keiichi Yasumoto,et al.  A method for recognizing living activities in homes using positioning sensor and power meters , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[37]  Anand Dersingh.  Human movement detection using home network information and events on smartphones , 2014, 2014 International Conference on Electronics, Information and Communications (ICEIC).

[38]  Radek Martinek,et al.  A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS , 2015 .

[39]  Abdelhamid Bouchachia,et al.  Human activity recognition in pervasive single resident smart homes: State of art , 2015, 2015 12th International Symposium on Programming and Systems (ISPS).

[40]  Jing Luo,et al.  Short-term Prediction of Building Energy Consumption Based on GALM Neural Network , 2015 .

[41]  Tin-Tai Chow,et al.  The use of occupancy space electrical power demand in building cooling load prediction , 2012 .

[42]  S. Karsoliya,et al.  Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture , 2012 .

[43]  Futoshi Asano,et al.  Fusion of audio and video information for detecting speech events , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[44]  Rui Zhang,et al.  An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network , 2010 .

[45]  Francesco G. B. De Natale,et al.  Smart Camera Reconfiguration in Assisted Home Environments for Elderly Care , 2014, ECCV Workshops.

[46]  Zhibin Zhao,et al.  EM-LDA model of user behavior detection for energy efficiency , 2014, 2014 IEEE International Conference on System Science and Engineering (ICSSE).

[47]  Cina Motamed,et al.  PhD forum: A data mining approach for human activity learning in a multi-modal sensor system , 2011, 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras.

[48]  Donald E. Brown,et al.  Health-status monitoring through analysis of behavioral patterns , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[49]  Sooyoung Kim,et al.  Prediction models and control algorithms for predictive applications of setback temperature in cooling systems , 2017 .

[50]  Antonio López,et al.  Ambient intelligence as paradigm of a full automation process at home in a real application , 2005, 2005 International Symposium on Computational Intelligence in Robotics and Automation.

[51]  Eric Campo,et al.  Design and evaluation of a device worn for fall detection and localization: Application for the continuous monitoring of risks incurred by dependents in an Alzheimer's care unit , 2013, Expert Syst. Appl..

[52]  Balqies Sadoun,et al.  The BAU GIS system using open source mapwindow , 2015, Human-centric Computing and Information Sciences.

[53]  Brigitte Meillon,et al.  Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects , 2011, Personal and Ubiquitous Computing.

[54]  Sebastian Möller,et al.  Evaluating the speech output component of a smart-home system , 2006, Speech Commun..

[55]  Xu Ming.  Control methods of active power filter , 2006 .

[56]  Javier Reina-Tosina,et al.  Wearable human movement monitoring device aimed at providing personalized support in healthcare , 2005, SPIE Microtechnologies.

[57]  Sadik Kara,et al.  Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease , 2006, Expert Syst. Appl..

[58]  Adina Magda Florea,et al.  A Multi-agent System for Human Activity Recognition in Smart Environments , 2011, IDC.

[59]  Jirí Koziorek,et al.  The design of the voice communication in Smart Home Care , 2013, 2013 36th International Conference on Telecommunications and Signal Processing (TSP).

[60]  Andrzej Czyzewski,et al.  Neuro-rough control of masking thresholds for audio signal enhancement , 2001, Neurocomputing.

[61]  Jing Luo,et al.  Application of GLBP Algorithm in the Prediction of Building Energy Consumption , 2015 .

[62]  Iván Pau,et al.  The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development , 2015, Sensors.

[63]  Henri P. Gavin,et al.  The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems c © , 2013 .

[64]  Kandarpa Kumar Sarma,et al.  Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech , 2016, Neural Networks.

[65]  Enrico Vicario,et al.  Using Temporal Logic and Model Checking in Automated Recognition of Human Activities for Ambient-Assisted Living , 2013, IEEE Transactions on Human-Machine Systems.

[66]  Maria E. Niessen,et al.  Monitoring Activities of Daily Living in Smart Homes: Understanding human behavior , 2016, IEEE Signal Processing Magazine.

[67]  Jan Vanus.  Using of the Adaptive Noise Cancellation for Voice Communication with the Control System , 2011, Int. J. Comput. Sci. Appl..

[68]  Roman Neruda,et al.  Teoretické otázky neuronových sítí , 1996 .

[69]  Jean Hennebert,et al.  Duration models for activity recognition and prediction in buildings using Hidden Markov Models , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[70]  Michel Giordani,et al.  Building an Index of Activity of Inhabitants From Their Activity on the Residential Electrical Power Line , 2011, IEEE Transactions on Information Technology in Biomedicine.

[71]  José Manuel Cuadra Troncoso,et al.  A simulation tool for monitoring elderly who suffer from disorientation in a smart home , 2015, Expert Syst. J. Knowl. Eng..

[72]  M. Shamim Hossain,et al.  Patient status monitoring for smart home healthcare , 2016, 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[73]  Jan Vanus,et al.  Application of optimal settings of the LMS adaptive filter for speech signal processing , 2010, Proceedings of the International Multiconference on Computer Science and Information Technology.

[74]  Alex Mihailidis,et al.  Development of an automated speech recognition interface for personal emergency response systems , 2009, Journal of NeuroEngineering and Rehabilitation.

[75]  Shaharyar Kamal,et al.  Real-time life logging via a depth silhouette-based human activity recognition system for smart home services , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[76]  Quang Vinh Nguyen,et al.  Passive RFID Localisation Framework in Smart Homes Healthcare Settings. , 2016, Studies in health technology and informatics.

[77]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.

[78]  Sungyoung Lee,et al.  Clinical Decision Support Service for elderly people in smart home environment , 2012, 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV).

[79]  Daijin Kim,et al.  A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments , 2014, Sensors.

[80]  Melvin Robinson,et al.  Prediction of residential building energy consumption: A neural network approach , 2016 .

[81]  Belkacem Fergani,et al.  Comparing HMM, LDA, SVM and Smote-SVM Algorithms in Classifying Human Activities , 2016 .

[82]  Radek Martinek,et al.  Adaptive noise suppression in voice communication using a neuro-fuzzy inference system , 2015, 2015 38th International Conference on Telecommunications and Signal Processing (TSP).