This paper presents an advanced Internet of Things point-of-care bio-fluid analyzer; a LoRa/Bluetooth-enabled electronic reader for biomedical strip-based diagnostics system for personalized monitoring. We undertake test simulations (technology trial without patient subjects) to demonstrate potential of long-range analysis, using a disposable test ‘key’ and companion Android app to form a diagnostic platform suitable for remote point-of-care screening for urinary tract infection. The 868 MHz LoRaWAN-enabled personalized monitor demonstrated sound potential with UTI test results being correctly diagnosed and transmitted to a remote secure cloud server in every case. Tests ranged over distances of 1.1-6.0 Km with radio path losses from 119-141 dB. All tests conducted were correctly and robustly received at the base station and relayed to the secure server for inspection. The UTI test strips were visually inspected for correct diagnosis based on color change and verified as 100% accurate. Results from testing across a number of regions indicate that such an Internet of Things medical solution is a robust and simple way to deliver next generation community-based smart diagnostics and disease management to best benefit patients and clinical staff alike. This significant step can be applied to any type of home or region, particularly those lacking suitable mobile signals, broadband connections, or even landlines. It brings subscription-free long-range bio-telemetry to healthcare providers and offers savings on regular clinician home visits or frequent clinic visits by the chronically ill. This work highlights practical hurdles in establishing an Internet of Medical Things network, assisting informed deployment of similar future systems.
|Journal||IEEE Journal of Translational Engineering in Health and Medicine|
|Publication status||Accepted/In press - 24 Mar 2018|
- Clinical Diagnostics
- Internet of Things
- Remote Healthcare
- Sensor networks
- Urinary Tract Infection.
Catherwood, P., Steele, D., McComb, S., & McLaughlin, J. (Accepted/In press). A Community-based IoT Personalized Wireless Healthcare Solution Trial. IEEE Journal of Translational Engineering in Health and Medicine, 1, 1-1. http://uir.ulster.ac.uk/39918/2/JTEHM-00087-2017%20Decision%20Letter.docx