Issue 10 Volume 41 May 16, 2003

artery pulse measurements. Such neural networks can be trained in specific subgroups (e.g. diabetics) to improve the
estimation of central aortic pressure from the peripheral pulse.
DTIC
Neural Nets; Pressure Measurement; Blood Pressure
20030034593 Seluk Univ., Konya, Turkey
A Recognition of ECG Arrhythmias Using Artificial Neural Networks
Oezbay, Yueksel; Karlik, Bekir; October 25, 2001; 5 pp.; In English; Original contains color illustrations
Report No.(s): AD-A410236; No Copyright; Avail: CASI; A01, Hardcopy
In this study, Artificial Neural Networks (ANN) has been used to classify the ECG arrhythmias. Types of arrhythmias
chosen from MIT-BIH ECG database to train ANN include normal sinus rhythm, sinus bradycardia, ventricular tachycardia,
sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial
fibrillation, and atrial flutter have been as. The different structures of ANN have been trained by arrhythmia separately and also
by mixing these 10 different arrhythmias. The most appropriate ANN structure is used for each class to test patients’ records.
The ECG records of 17 patients whose average age is 38.6 were made in the Cardiology Department, Faculty of Medicine
at Selcuk University. Forty-two different test patterns were extracted from these records. These patterns were tested with the
most appropriate ANN structures of single classification case and mixed classification cases. The average error of single
classifications was found to be 4.3\% and the average error of mixed classification 2.2\%.
DTIC
Arrhythmia; Electrocardiography; Neural Nets; Artificial Intelligence; Computer Networks
20030034657 University Hospital, Berlin, German
Development Aspects of a Robotised Gait Trainer for Neurological Rehabilitation
Schmidt, H.; Sorowka, D.; Hesse, S.; Bernhardt, R.; Oct 2001; 5 pp.; In English; Original contains color illustrations
Report No.(s): AD-A410368; No Copyright; Avail: CASI; A01, Hardcopy
The restoration of gait is a key goal after stroke, traumatic brain injury and spinal cord injury. Conventional training
methods, e.g. treadmill training, require great physical effort from the therapists to assist the patient After the successful
development and application of a mechanised gait trainer, a new research project of constructing a sensorised robot gait trainer
is under way. The aim of this project is to build a robotic device which enables the therapist to let the machine move the
patients feet, fixed on two footplates, on programmable foot trajectories (e.g. walking on the ground, stepping stairs up and
down, disturbances during walking). Furthermore impedance control algorithms will be incorporated for online adaptation of
the foot trajectories to the patients walking capabilities. Another important feature is the compliance control to simulate virtual
ground conditions, i.e. the machine acts as a haptic foot device. Due to the partially high dynamic foot movements during
normal walking, conventional industrial robots are not suitable for this task. This paper describes development aspects and
problems that have to be dealt with during the design process of the robotised gait training machine.
DTIC
Robotics; Training Devices; Walking
20030034704 Newcastle-upon-Tyne Univ., Newcastle
Taxonomy and Evaluation for Systems Analysis Methodologies in a Workflow Context: Structured Systems Analysis
Design Method (SSADM), Unified Modeling Language (UML), Unified Process, Soft Systems Methodology (SSM) and
Organization Process Modeling
Al-Humaidan, F.; Rossiter, B. N.; 2003; 36 pp.
Report No.(s): PB2003-102609; Copyright; Avail: National Technical Information Service (NTIS)
Complex information systems require a methodology for their development in a structured manner. Many different
methodologies exist, each suitable for a particular type of application. In this report we develop a taxonomy covering 14
different classification features for methodologies targeted at the workflow area. Features identified include concerns, method
structure, data gathering means, people involved, notations, decomposition, policies, reuse, adaptability, flexibility, exception
handling, method output, CASE tool and quality assurance. The capabilities of a number of methodologies are expressed in
tabular form relative to this taxonomy for workflow systems and to a more general taxonomy dealing with both hard- and
soft-system aspects. The results show that there is no methodology that covers all of the taxonomic aspects identified.
Organizational Process Modeling (OPM) and Soft Systems Methodology (SSM) are relatively strong on soft aspects and weak
on hard aspects. Unified Modeling Language (UML) and Unified Process are relatively strong on hard aspects and weak on
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