Vasileios Syrimpeis

A Web and Smart Phone System for Tibia Open Fractures

This paper presents an integrated System for Tibia Open Fractures (STOF), based on web and mobile phone applications. A rapid prototyping method was followed based on Adobe Fireworks CS3. The database is developed in SQL Server 2008. The application is created in Microsoft Visual Studio 2010 Ultimate, using ASP.NET programming language. A system that includes an online database and a smart phone application for recording, monitoring and studying patients with tibia open fractures is developed. STOF is designed to be doctors-friendly using classifications and knowledge grouping specialized for tibia open fractures.

A closed-loop drop-foot correction system with gait event detection from the contralateral lower limb using fuzzy logic

In the present paper a closed loop fuzzy control scheme for the correction of the drop-foot syndrome is presented. The control scheme is based on EMG signals from the contralateral, unimpaired lower limb and its efficiency is tested by the application of the controller to a simulated bipedal locomotion with one lower limb suffering from the Drop-Foot Syndrome. Two are the major characteristics of the controller: identification of the phase where the stimulation must be provided using EMG signals from the contralateral, unimpaired lower limb and correction of the excitation signal according to the phase using EMG signals from the collateral impaired lower limb.

Exploiting Fuzzy Expert Systems in Cardiology

This paper presents a Sugeno-type Fuzzy Expert System (FES) designed to help mostly Cardiologists and General Practitioners in taking decisions on the most common cardiological clinical dilemmas. FES is separated in five sub-systems; Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure and Diabetes, covering a wide range of Cardiology. The Fuzzy Rules of the sub-systems start counting from 30 till 300. FES is verified and validated from three different Medical Doctors User Groups (A, B & C) for three basic criteria, which are Medical Reliability, Assistance in Work and Usability. In addition, FES proved to be a valuable educative tool for Cardiology Medical Residents and Medical Students.

A Knowledge Based System for the Selection of Muscles for Gait Phase Detection using EMGs

Purpose: This paper presents the development of a knowledge based system for the detection of gait phases based on EMGs from muscles of the lower limb. Methods: An empirical analysis of the EMG characteristics for the most representative muscle of every muscle group concerning their suitability for the gait phase detection is presented. The same approach is applied to every lower limb muscle where an EMG could be received. The entities and the decision-making mechanism of the knowledge based system is presented in a formal way. Results: A knowledge based system is built upon the knowledge acquired from this analysis. Finally, an example is presented where the developed knowledge based system is used to support the conceptual design of a drop foot correction system. Conclusions: The knowledge based system can be used in the conceptual design of any rehabilitation system for lower limb disabilities using EMG signals from the lower limbs.

Exploiting expert systems in cardiology: a comparative study.

An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients.

An Expert System for supporting the conceptual design of controllers for lower limbs rehabilitation systems

In this paper, an Expert System (ES) for supporting the conceptual design of a closed loop control scheme for the rehabilitation of lower limb disabilities is presented. The design of the controllers is based on the exploitation of experts knowledge concerning the gait cycle and uses pathological muscles electromyographical (EMG) data for feedback signals. EMGs from normal muscles are also used to detect the gait phases and an expert system for supporting the selection of the suitable set of muscles is developed. The acquired knowledge concerning the gait phases and the muscles EMGs is presented. Finally, a case study of designing a controller for the correction of the drop foot syndrome is presented.