A Reconfigurable Connected Health Platform Using ZYNQ System on Chip
Author | Abunahia D.G. |
Author | Ismail T.A. |
Author | Abou Al Ola H.R. |
Author | Amira A. |
Author | Ait Si Ali A. |
Author | Bensaali F. |
Available date | 2020-02-05T08:53:39Z |
Publication Date | 2018 |
Publication Name | Lecture Notes in Networks and Systems |
Resource | Scopus |
ISSN | 23673370 |
Abstract | This paper presents a reconfigurable connected health platform for fall and cardiac disease detection to be used in smart home environments using Shimmer sensing device and ZYNQ System on Chip (SoC) platform. The system can also be deployed in ambulances to equip them with health monitoring technologies. The system is designed to be used by elderly, diabetics, muscular and neurological patients who are likely to fall, in addition to heart patients who are probable to get heart attacks causing falls. This project aims to provide users and their families with a sense of mental and physical security in their houses. It will also help them pass through the abstraction of money, since they will not need costly home care nursing services, and they will enjoy relief without having an observer, hence providing a significant socioeconomic impact. The system has three main features: (1) Sensing data gathered from the accelerometer and Electrocardiogram (ECG) electrodes embedded in the Shimmer sensing device; (2) Real time monitoring and alerting system; and (3) Medical logging consists of time, position, strength, and location of the fall. The real-time classification of the fall detection has been achieved with an accuracy of 90% using K-Nearest Neighbors (KNN) algorithm. Moreover, the KNN hardware implementation requires 48% of Look-Up Tables (LUTs) and 22% of Flip-Flops (FFs) available on the Zedboard while consuming 582 Mw. Springer International Publishing AG 2018. |
Sponsor | Acknowledgment. This paper was made possible by National Priorities Research Program (NPRP) grant No. 5 - 080 - 2 - 028 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The work is also supported by Qatar University student grant QUST-CENG-FALL-15/16-1. |
Language | en |
Publisher | Springer |
Subject | Connected health ECG Fall detection KNN System on chip ZYNQ ZYNQ prototyping board |
Type | Book chapter |
Pagination | 857-867 |
Volume Number | 16 |
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