A multi: modal decision making system for an ambient assisted living environment

Modern ubiquitous services demand the fusion of data from multiple modalities, but so far few approaches have achieved to present such solutions. This challenge becomes even more demanding when the AAL environment comes to serve the requirements of elderly people who need continuous monitoring and care. In light of this, this paper presents a multi -- modal decision making system that consists of mixed knowledge and non-knowledge based subsystems that deliver the appropriate intelligence among three modalities (i.e. ambient, health, fall detection). The main effort has been given on the health status assessment module, where a SVM classifier has been trained using the Physionet's MIT-BIH Arrhythmia database in order to detect abnormal heart beats based on time-domain and statistical features. An initial study on the classification scheme showed satisfactory results for the purposes of a system that is responsible of early screening and dangerous event detection.

[1]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[2]  Álvaro Marco,et al.  A Smart Kitchen for Ambient Assisted Living , 2014, Sensors.

[3]  C. Todd,et al.  World Health Organisation Global Report on Falls Prevention in Older Age , 2007 .

[4]  Mukhtiar Memon,et al.  Ambient Assisted Living Healthcare Frameworks, Platforms, Standards, and Quality Attributes , 2014, Sensors.

[5]  Alex Mihailidis,et al.  A Survey on Ambient-Assisted Living Tools for Older Adults , 2013, IEEE Journal of Biomedical and Health Informatics.

[6]  Joan Cabestany,et al.  SVM-based posture identification with a single waist-located triaxial accelerometer , 2013, Expert Syst. Appl..

[7]  Edward H. Shortliffe,et al.  Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence) , 1984 .

[8]  Qeethara Al-Shayea.  Artificial Neural Networks in Medical Diagnosis , 2024, International Journal of Research Publication and Reviews.

[9]  Ignacio González Alonso,et al.  Assessing Smart-Home Platforms for Ambient Assisted Living (AAL) , 2013, Int. J. Ambient Comput. Intell..

[10]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[11]  David Redden,et al.  A decision tree for tuberculosis contact investigation. , 2002, American journal of respiratory and critical care medicine.

[12]  Y. Y. Chen,et al.  Rule based clinical decision support system for hematological disorder , 2013, 2013 IEEE 4th International Conference on Software Engineering and Service Science.

[13]  Inmaculada Plaza,et al.  Challenges, issues and trends in fall detection systems , 2013, Biomedical engineering online.

[14]  Liang Xiao,et al.  Developing a rule-driven clinical decision support system with an extensive and adaptative architecture , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[15]  Fatih Basçiftçi,et al.  Web based medical decision support system application of Coronary Heart Disease diagnosis with Boolean functions minimization method , 2011, Expert Syst. Appl..

[16]  Elif Derya Übeyli,et al.  ECG beat classifier designed by combined neural network model , 2005, Pattern Recognit..

[17]  Fariba Sadri,et al.  Ambient intelligence: A survey , 2011, CSUR.

[18]  Yu-Liang Hsu,et al.  ECG arrhythmia classification using a probabilistic neural network with a feature reduction method , 2013, Neurocomputing.

[19]  Howard Carter,et al.  Foundations of Decision Support Systems , 1982 .

[20]  Álvaro Marco,et al.  Location-based services for elderly and disabled people , 2008, Comput. Commun..

[21]  Shailja Shukla,et al.  ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier , 2013 .

[22]  D.G. Wakde,et al.  Design of portable ARM processor based ECG module for 12 lead ECG data acquisition and analysis , 2009, 2009 International Conference on Biomedical and Pharmaceutical Engineering.

[23]  Bruce G. Buchanan,et al.  The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .

[24]  Christos Panagiotou,et al.  On the Detection of Myocadial Scar Based on ECG/VCG Analysis , 2013, IEEE Transactions on Biomedical Engineering.

[25]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[26]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.