Monitoring and Detection Platform to Prevent Anomalous Situations in Home Care

Monitoring and tracking people at home usually requires high cost hardware installations, which implies they are not affordable in many situations. This study/paper proposes a monitoring and tracking system for people with medical problems. A virtual organization of agents based on the PANGEA platform, which allows the easy integration of different devices, was created for this study. In this case, a virtual organization was implemented to track and monitor patients carrying a Holter monitor. The system includes the hardware and software required to perform: ECG measurements, monitoring through accelerometers and WiFi networks. Furthermore, the use of interactive television can moderate interactivity with the user. The system makes it possible to merge the information and facilitates patient tracking efficiently with low cost.

[1]  Szi-Wen Chen,et al.  A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising , 2006, Comput. Methods Programs Biomed..

[2]  Wen-June Wang,et al.  QRS complexes detection for ECG signal: The Difference Operation Method , 2008, Comput. Methods Programs Biomed..

[3]  Javier Bajo,et al.  Mitigation of the ground reflection effect in real-time locating systems based on wireless sensor networks by using artificial neural networks , 2012, Knowledge and Information Systems.

[4]  Chia-Ping Shen,et al.  Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines , 2012, Expert Syst. Appl..

[5]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[6]  Shane A Lowe,et al.  Monitoring human health behaviour in one's living environment: a technological review. , 2014, Medical engineering & physics.

[7]  Yuefeng Ji,et al.  R2NA: Received Signal Strength (RSS) Ratio-Based Node Authentication for Body Area Network , 2013, Sensors.

[8]  Miguel A. Patricio,et al.  Context-based scene recognition from visual data in smart homes: an Information Fusion approach , 2012, Personal and Ubiquitous Computing.

[9]  Sarabjeet Singh Mehta,et al.  Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram , 2009, Comput. Methods Programs Biomed..

[10]  J. Takács,et al.  Validation of the Fitbit One activity monitor device during treadmill walking. , 2014, Journal of science and medicine in sport.

[11]  Enzo Pasquale Scilingo,et al.  Robust multiple cardiac arrhythmia detection through bispectrum analysis , 2011, Expert Syst. Appl..

[12]  José A. Gallud,et al.  Improving location awareness in indoor spaces using RFID technology , 2010, Expert Syst. Appl..

[13]  Paulo Novais,et al.  A Caregiver Support Platform within the Scope of an Ambient Assisted Living Ecosystem , 2014, Sensors.

[14]  Kenton R Kaufman,et al.  Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure. , 2005, Preventive medicine.

[15]  Nayat Sanchez-Pi,et al.  A knowledge-based system approach for a context-aware system , 2012 .

[16]  Sandra Prescher,et al.  Tele-accelerometry as a novel technique for assessing functional status in patients with heart failure: feasibility, reliability and patient safety. , 2013, International Journal of Cardiology.

[17]  Xiaopeng Zhao,et al.  Cloud-ECG for real time ECG monitoring and analysis , 2013, Comput. Methods Programs Biomed..

[18]  Patrick Gaydecki,et al.  The use of the Hilbert transform in ECG signal analysis , 2001, Comput. Biol. Medicine.

[19]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[20]  Steffen Leonhardt,et al.  Automatic Step Detection in the Accelerometer Signal , 2007, BSN.

[21]  Bachir Boucheham,et al.  Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation , 2005 .

[22]  José Neves,et al.  Inter-organization cooperation for ambient assisted living , 2010, J. Ambient Intell. Smart Environ..

[23]  Peter Martini,et al.  Indoor tracking for mission critical scenarios: A survey , 2011, Pervasive Mob. Comput..

[24]  Javier Bajo,et al.  idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining , 2013, Inf. Sci..

[25]  Javier Bajo,et al.  Implementing a hardware-embedded reactive agents platform based on a service-oriented architecture over heterogeneous wireless sensor networks , 2013, Ad Hoc Networks.

[26]  Marcela D. Rodríguez,et al.  Supporting Context-Aware Collaboration in a Hospital: An Ethnographic Informed Design , 2003, CRIWG.

[27]  Yo-Ping Huang,et al.  Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis , 2014, Appl. Soft Comput..

[28]  Ming-Feng Yeh,et al.  Real-time ECG telemonitoring system design with mobile phone platform , 2008 .

[29]  Carlo Marchesi,et al.  Discovering dangerous patterns in long-term ambulatory ECG recordings using a fast QRS detection algorithm and explorative data analysis , 2006, Comput. Methods Programs Biomed..

[30]  V. Vapnik Pattern recognition using generalized portrait method , 1963 .

[31]  John M. Darrington Towards real time QRS detection: A fast method using minimal pre-processing , 2006, Biomed. Signal Process. Control..

[32]  Nimsiri Abhayasinghe,et al.  A gyroscopic data based pedometer algorithm , 2013, 2013 8th International Conference on Computer Science & Education.

[33]  Matjaz Gams,et al.  Context-aware MAS to support elderly people (demonstration) , 2012, AAMAS.

[34]  A W Hahn,et al.  Wavelet transforms for electrocardiogram processing. , 1997, Biomedical sciences instrumentation.

[35]  Beth Logan,et al.  Single access point localisation for wearable wireless sensors , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[36]  Javier Bajo,et al.  Context-aware multiagent system: Planning home care tasks , 2013, Knowledge and Information Systems.

[37]  Kevin Curran,et al.  A survey of active and passive indoor localisation systems , 2012, Comput. Commun..

[38]  Javier Bajo,et al.  Applying Classifiers in Indoor Location System , 2013, PAAMS.

[39]  Javier Bajo,et al.  PANGEA - Platform for Automatic coNstruction of orGanizations of intElligent Agents , 2012, DCAI.

[40]  José M. Alonso,et al.  Enhanced WiFi localization system based on Soft Computing techniques to deal with small-scale variations in wireless sensors , 2011, Appl. Soft Comput..

[41]  Patricia Anthony,et al.  APPLYING MULTI-AGENT SYSTEM IN A CONTEXT AWARE SMART HOME , 2009 .

[42]  W. Hoffmann,et al.  Telemedicine and telecare for older patients--a systematic review. , 2012, Maturitas.

[43]  K. Kaufman,et al.  Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities. , 2014, Medical engineering & physics.

[44]  Jesús García,et al.  Context-Aided Sensor Fusion for Enhanced Urban Navigation , 2012, Sensors.

[45]  Javier Bajo,et al.  Model for assigning roles automatically in egovernment virtual organizations , 2012, Expert Syst. Appl..

[46]  Florentino Fernández Riverola,et al.  Unobstructive Body Area Networks (BAN) for Efficient Movement Monitoring , 2012, Sensors.

[47]  Javier Bajo,et al.  GerAmi: Improving Healthcare Delivery in Geriatric Residences , 2008, IEEE Intelligent Systems.

[48]  Chia-Tai Chan,et al.  ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI , 2011, ANT/MobiWIS.

[49]  José M. Molina López,et al.  A knowledge-based system approach for a context-aware system , 2012, Knowl. Based Syst..

[50]  Jaime Lloret,et al.  Mobile Sensing Systems , 2013, Sensors.