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 (UTI). 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 paper highlights practical hurdles in establishing an Internet of Medical Things network, assisting informed deployment of similar future systems.
An evaluation of a newly CE approved bedside monitoring device used in a general hospital ward is presented. This evaluation has shown that it is feasible to use the system within this environment to provide medical staff with supplementary information on patient health, at more frequent intervals than traditional monitoring methods. The physiological data recorded by the body worn device is wirelessly transmitted to a patient management system for storage and display. Good correlation between heart rate values recorded by hospital staff and those recorded by the automated Vitalsens VS100 system was observed. The system has highlighted clinical information that routine observations alone did not readily identify. This can provide clinicians with a better view of the overall health status of the patient. Such medical issues include those witnessed in this study, namely paroxysmal AF, ectopic beats, increasing heart rates recorded prior to a hypoglycaemic event, general high and low heart rate trends and various instances where clinically relevant ECG data has been captured.
This paper presents the results of a campaign to investigate the empirical characterisation and mathematic modelling of the radio channel for a body-centric LoRaWAN (Long Range Wide Area Network) transceiver for various operating distances across various environments including urban, suburban, and rural. The radio channel for a wearable LoRa transceiver device was explored, as well as anechoic measurements to understand body-shadowing effects. Results indicate that the best fit model for all recorded received signal strength measurements (using the Akaike information criterion to fit) is the Nakagami distribution with mu = 0.52 and Ω = 662.13. Anechoic measurements indicated typical additional effects regarding the orientation of the user with respect to the gateway location. This work highlights LoRaWAN as a credible wearable wireless technology.
Received signal strength measurements and delay statistics are presented for both a stationary and mobile user equipped with a wearable UWB radio transmitter within a hospital environment. The measurements were made for both waist and chest mounted antennas using RF-over-fibre technology to eliminate any spurious electromagnetic scattering effects associated with metallic co-axial cables. The results show that received signal strength values were dependent on whether transmit and receive antennas had line of sight and were also affected by body-shadowing and antenna-body position. For mobile conditions, received signal strength tended to be lognormally distributed with non line of sight links having significantly lower mean values. Excess time delay results for mobile user tests were best described by the Weibull distribution. Overall, the results favoured the chest mounted antenna position, with higher mean signal levels, reduced mean excess delay and less difference between line of sight and non line of sight channels.
This paper presents a critical analysis of ultrawideband (UWB) and considers the turbulent journey it has had from the Federal Communications Commission's bandwidth allocation in 2002 to today. It analyzes the standards, the standoffs, and the stalemate in standardization activities and investigates the past and present research and commercial activities in realizing the UWB dream. In this paper, statistical evidence is presented to depict UWB's changing fortunes and is utilized as an indicator of future prominence. This paper reviews some of the opinions and remarks from commentators and analyzes predictions that were made. Finally, it presents possible ways forward to reignite the high-data-rate UWB standardization pursuit.
This study presents the findings of an empirical channel characterisation for an ultra-wideband off-body optic fibre-fed multiple-antenna array within an office and corridor environment. The results show that for received power experiments, the office and corridor were best modelled by lognormal and Rician distributions, respectively [for both line of sight (LOS) and non-LOS (NLOS) scenarios]. In the office, LOS measurements for t mean and t RMS were both described by the Normal distribution for all channels, whereas NLOS measurements for t mean and t RMS were Nakagami and Weibull distributed, respectively. For the corridor measurements, LOS for t mean and t RMS were either Nakagami or normally distributed for all channels, with NLOS measurements for t mean and t RMS being Nakagami and normally distributed, respectively. This work also shows that achievable diversity gain was influenced by both mutual coupling and cross-correlation co-efficients. Although the best diversity gains were 1.8 dB for three-channel selective diversity combining, the authors present recommendations for improving these results.
This paper presents a study which evaluated the potential for using ultra-low altitude, unmanned aerial vehicles to deliver fifth-generation (5G) cellular connectivity, particularly into areas requiring short-term enhancement in coverage. Such short-term enhancement requirements may include large gatherings of people or during disaster scenarios where there may be service outages or a need for increased bandwidth. An evaluation of this approach was conducted with empirically generated results regarding signal quality and cellular coverage-illustrating the potential of using unmanned ultra-low altitude aerial vehicles to deliver 5G cellular mobile services. Specifically, channel gain, mean time delay of the received signals (τ mean), and the root-mean-square spread of the delay (τ rms) were investigated for two distinct user modes at three different drone heights for three selected environments-an open area (field), a tree-lined environment, and an enclosed area. Maximum likelihood estimates for the various drone heights, user modes, and operational environments were found to be Rician distributed for the received signal strength measurements, whereas τ mean and τ rms for the open and tree-lined environments were Weibull distributed with the enclosed area tests being lognormally distributed. The paper also investigates how the channel gain may be affected when operating in each of the various global bands allocated for mid-5G communications, namely, Europe, China, Japan, South Korea, and North America. These regional mid-5G band allocations were found to yield minimal variance for all the environments considered.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.