Georgia N. Athanasiou

A profile-based Trust Management scheme for Ubiquitous Healthcare environment

Ubiquitous Healthcare environment materializes the patient-centric paradigm providing healthcare services without spatial and temporal limitations. However, the nature of Ubiquitous Healthcare services requiring exchange of sensitive personal data raises trust issues. In this paper, we propose a profile-based Trust Management scheme that enables the patient to select the most trustworthy Healthcare Provider in a Ubiquitous Healthcare environment. Furthermore, we propose an extended User Profile structure integrating trust-related information in order to enhance the functionality of the proposed Trust Management scheme.

Towards personalization of Trust Management service for ubiquitous healthcare environment

In healthcare, trust is considered to be the key factor for the provision of effective healthcare services. Thus, ubiquitous healthcare environment incorporate Trust Management systems or services for enabling the creation of confident and secure background required for the provision of healthcare services. In this paper the concept of personalized Trust Management service is introduced. However, since this approach makes the service vulnerable to users subjectivity in this paper is proposed a mechanism that determines the proper in case personalization factor. Especially, it quantifies the Quality of trust Information that user has acquired from past interactions and determines if he/she is capable to discover and select healthcare providers. The introduced mechanism is deployed on a Fuzzy Interference System and its performance was evaluated through simulations in MATLAB/SIMULINK environment.

A novel trust evaluation method for Ubiquitous Healthcare based on cloud computational theory

The notion of trust is considered to be the cornerstone on patient-psychiatrist relationship. Thus, a trustfully background is fundamental requirement for provision of effective Ubiquitous Healthcare (UH) service. In this paper, the issue of Trust Evaluation of UH Providers when register UH environment is addressed. For that purpose a novel trust evaluation method is proposed, based on cloud theory, exploiting User Profile attributes. This theory mimics human thinking, regarding trust evaluation and captures fuzziness and randomness of this uncertain reasoning. Two case studies are investigated through simulation in MATLAB software, in order to verify the effectiveness of this novel method.

A Trust Assessment mechanism for Ubiquitous Healthcare environment employing cloud theory.

Mental healthcare domain highlights the significance of trustworthiness between patient and psychiatrist for treatment process. In this paper, the issue of assessing psychiatrist trustworthiness from patient perspective, within a Ubiquitous Healthcare (UH) environment, is addressed. To meet that challenge, a Trust Assessment mechanism mimicking human cognitive judgment, is proposed. The exploitation of innovative fuzzy-probabilistic transformation model, denoted as cloud, for mechanism deployment enables fuzziness as well as adhered randomness of cognitive perception and assessment to be captured. A set of simulations within MATLAB software environment verify the introduced mechanism efficiency.

Stress-oriented driver assistance system for electric vehicles

Stress is physiological and physical reaction that appears in highly demanding situations and affects humans perception and reaction capability. Occurrence of stress events within highly dynamic road environment could lead to life-threatening situation. With the perspective of safety and comfort driving provision to anxious drivers, in this paper a stress-oriented Driver Assistance System (DAS) is proposed. The DAS deployed on Electric Vehicle. This novel DAS customizes driving command signal in respect to road context, when stress is detected. The effectiveness of this novel DAS is verified by simulation in MATLAB/SIMULINK environment.

Towards Personalized Services in the Healthcare Domain

Healthcare services are designed for enabling the provision of medical care to the patient. The traditional healthcare services are based on the doctor-centric paradigm. Essentially, they enable healthcare providers to assess patients’ health status based on information derived from medical examination and information stored in patient’s electronic Medical Health Records (eMHRs) [1]. Hence, it is crucial for patient’s health data to be digitalized and organized in such a way allowing their exploitation by the healthcare provider at a later point of time [2]. The doctor-centric healthcare services enhance healthcare providers’ diagnosing skills and enable them to give patients accurate treatment directions aiming to their earlier and safer de-hospitalization.

Fault identification on hall-effect sensors positioning in brushless DC motor drives via a fuzzy inference system

In this study, a diagnostic method to identify the misplacement angle of a Hall-effect position sensor in brushless DC motor drives is presented. The proposed method is based on a single signal measurement, deployed on a Fuzzy Inference System (FIS), and can be implemented on a micro-processor for near real-time diagnosis of the unbalanced positioning of a sensor. The designed FIS was evaluated in a wide range of misplacement angles and its ability to determine both low and high error values was verified. The calculated local and overall accuracy values prove the high performance of the proposed diagnostic method.

Hall-effect sensor fault identification in brushless DC motor drives using wavelets

In this study, a diagnostic method to identify potential faults of Hall-effect sensors in brushless DC (BLDC) motor drives is proposed. The wavelet signal theory is evaluated as an alternative diagnostic method, able to identify either a breakdown or a misplacement of a position sensor. The proposed method combines the required noise insensitivity with the measurement of a single signal. Since the DC-link current of the inverter input is highly affected by erroneous position sensing and usually measured in BLDC drives, it was chosen as the only necessary informational signal. Consequently, the wavelet signal theory was assessed as the first stage of a fault tolerant control system (FTCS) and its effectiveness to identify both types of position sensor faults was experimentally verified.

Deployment of pHealth Services upon Always Best Connected Next Generation Network

This paper deals with the deployment of personalized healthcare (pHealth) services upon new networks supporting pervasive connectivity. A profiling scheme of two levels is introduced, where health status related attributes and preferences are included in the upper-level, named Service User Profile and communication attributes compose the lower-level, named Transport User Profiles. We manage pervasive connective by means of Always Best Connected paradigm for enhancing personalization within Next Generation Network architecture.