PRESENT AND FUTURE PERVASIVE HEALTHCARE METHODOLOGIES: INTELLIGENT BODY DEVICES, PROCESSING AND MODELING TO SEARCH FOR NEW CARDIOVASCULAR AND PHYSIOLOGICAL BIOMARKERS

The motivation behind this work comes from the area of pervasive computing technologies for healthcare and wearable healthcare IT systems, an emerging field of research that brings in revolutionary paradigms for computing models in the 21st century. The aim of this thesis is focused on emerging personal health technologies and pattern recognition strategies for early diagnosis and personalized treatment and rehabilitation for individuals with cardiovascular and neurophysiological diseases. Attention was paid to the development of an intelligent system for the automatic classification of cardiac valve disease for screening purposes. Promising results were reported with the possibility to implement a new screening strategy for the diagnosis of cardiac valve disease in developing countries. A novel assistive architecture for the elderly able to non-invasively assess muscle fatigue by surface electromyography using wireless platform during exercise with an ergonomic platform was presented. Finally a wearable chest belt for ECG monitoring to investigate the psycho-physiological effects of the autonomic system and a wearable technology for monitoring of knee kinematics and recognition of ambulatory activities were characterized to evaluate the reliability for clinical purposes of collected data. The potential impact in the clinical arena of this research would be extremely important, since promising data show how such emerging personal technologies and methodologies are effective in several scenarios to early screening and discovery of novel diagnostic and prognostic biomarkers

A MODEL OF PACKET LOSS CAUSED BY INTERFERENCE BETWEEN THE BLUETOOTH LOW ENERGY COMPONENT OF AN IOS WEARABLE BODY AREA NETWORK AND RESIDENTIAL MICROWAVE OVENS

Cardiovascular diseases are the leading cause of death in the United States. Advances in wireless technology have made possible the remote monitoring of a patient’s heart sensors as part of a body area network. Previous studies have suggested that stray wireless transmissions in the industrial, scientific, and medical (ISM) band cause interference resulting in packet loss in Bluetooth piconets. This study investigates the impact that wireless transmissions from residential microwave ovens have on the Bluetooth Low Energy (BLE) component of the body area network. Using a systematic data collection approach, two variables were manipulated. The distance between the microwave oven and the BLE piconet was varied from 0.5 meter to 5.0 meters at one-half meter increments. At each distance, the power level of the microwave oven was varied from the lowest power setting to the highest power setting. The two variables that were collected were the microwave interference generated by channel and the packet loss by channel. The results suggest more packet loss is due to the microwave oven’s power level than by the distance, the interference caused by the microwave oven affects all BLE channels equally, and the packet loss by channel is a good predictor of microwave oven interference. The significance of this study lies in providing beneficial information to the medical and digital communication industries concerning the causes and solutions to disruptions in the Bluetooth-enabled body area network devices in a very common situation. The results of this study may lend support for improvements and widespread use of body area network medical systems, which may have the benefit of better monitoring, more data, and reduced fatalities due to misdiagnosed heart conditions