A Three Phase Computer Assisted Biofeedback Training System Using Case-Based Reasoning

Biofeedback is a method gaining increased interest and showing good results for a number of physical and psychological problems. Biofeedback training is mostly guided by an experienced clinician and the results largely rely on the clinician's competence. In this paper we propose a three phase computer assisted sensor-based biofeedback decision support system assisting less experienced clinicians, acting as second opinion for experienced clinicians. The three phase CBR framework is deployed to classify a patient, estimate initial parameters and to make recommendations for biofeedback training by retrieving and comparing with previous similar cases in terms of features extracted. The three phases work independently from each other. Moreover, fuzzy techniques are incorporated into our CBR system to better accommodate uncertainty in clinicians reasoning as well as decision analysis. All parts in the proposed framework have been implemented and primarily validated in a prototypical system. The initial result shows how the three phases functioned with CBR technique to assist biofeedback training. Eventually the system enables the clinicians to allow a patient to train himself/herself unsupervised.

[1]  Enric Plaza,et al.  Case-Based Learning of Strategic Knowledge , 1991, EWSL.

[2]  Mobyen Uddin Ahmed,et al.  Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress , 2006 .

[3]  Moti Schneider,et al.  Matching attributes in a fuzzy case based reasoning , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[4]  Mobyen Uddin Ahmed,et al.  Individualized Stress Diagnosis Using Calibration and Case-Based Reasoning , 2007 .

[5]  Petra Perner An architecture for a CBR image segmentation system , 1999 .

[6]  Mobyen Uddin Ahmed,et al.  Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning , 2007, ICCBR.

[7]  Peter Funk,et al.  Building similarity metrics reflecting utility in case-based reasoning , 2006, J. Intell. Fuzzy Syst..

[8]  Mobyen Uddin Ahmed,et al.  Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity , 2008, Trans. Case Based Reason..

[9]  Brijesh Verma,et al.  A neural network based technique for muscle coordination and vertical jump height prediction , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[10]  Cynthia R. Marling,et al.  Case-Based Reasoning in the Care of Alzheimer's Disease Patients , 2001, ICCBR.

[11]  Abdul V. Roudsari,et al.  Integrating Different Methodologies for Insulin Therapy Support in Type 1 Diabetic Patients , 2001, AIME.

[12]  Isabelle Bichindaritz,et al.  Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice , 1998, EWCBR.

[13]  Peter Funk,et al.  Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system , 2006, Artif. Intell. Medicine.

[14]  Petra Perner,et al.  Recognition of airborne fungi spores in digital microscopic images , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[15]  Wolfgang Rosenstiel,et al.  ONLINE CLASSIFICATION OF EEG SIGNALS USING ARTIFICIAL NEURAL NETWORKS FOR BIOFEEDBACK TRAINING OF PATIENTS WITH EPILEPSY , 2002 .

[16]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.