Architecture and design of real-time system for elderly health monitoring

Despite the decreased human mortality rate, heart disorders are one of the main causes of death around the world. As a result, detection of irregularities in the rhythms of the heart is a growing concern in medical researches. The collection, processing, and visualisation of such biomedical data in real-time is a challenging task due to the large amounts of data that need to be processed, especially when the records are made for a long time. Recent technological advances in sensors and low-power microelectronics enabled the development of a single embedded biomedical chip capable of running computationally intensive biomedical applications, such as remote analysis and monitoring of human heart activity, which is still a challenging problem for biomedical engineers. In this work, we present a novel architecture and hardware/software prototyping of a real-time system, targeted for elderly health monitoring, named BANSMOM. The proposed system achieves its real-time performance via parallel processing techniques and a period-peak-detection algorithm (PPD) for processing multi-lead electrocardiography records in parallel. We tested the proposed system with real ECG fixed length records (10 s/sample) from the MIT database. From the evaluation results, we found that the system meets its real-time requirements and achieves about 69% accuracy.