James Heaney
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
James Heaney has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Jamie Buick
School of Engineering, Ulster University, Newtownabbey BT37 0QB, Northern Ireland, UK
1 shared publication - Muhammad Usman Hadi
School of Engineering, Ulster University, Belfast BT15 1AP, UK
1 shared publication - Navneet Soin
Institute for Materials Research and Innovation (IMRI), University of Bolton, Bolton, UK
1 shared publication
Latest Publications
- Open Access
- 6 December 2022
- Published by MDPI AG in Micromachines
- Vol. 13 (12) , 2153
Internet of Things-Based ECG and Vitals Healthcare Monitoring System
Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient’s heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient’s physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient’s status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.
- 6 December 2022
- Published by MDPI AG in Micromachines
- Vol. 13 (12) , 2153
Open AccessInternet of Things-Based ECG and Vitals Healthcare Monitoring System
Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient’s heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient’s physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient’s status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.