Vital Signs Monitoring System Based on EMFi Sensors and Wavelet Analysis

Author(s):  
O. Postolache ◽  
P. Silva Girao ◽  
G. Postolache ◽  
M. Pereira
2014 ◽  
Vol 80 (3) ◽  
pp. 218
Author(s):  
N. Lo ◽  
A. Navlekar ◽  
E. Palmgren ◽  
R. Rekhi ◽  
F. Ussher ◽  
...  

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e031150 ◽  
Author(s):  
Candice Downey ◽  
Shu Ng ◽  
David Jayne ◽  
David Wong

ObjectiveTo validate whether a wearable remote vital signs monitor could accurately measure heart rate (HR), respiratory rate (RR) and temperature in a postsurgical patient population at high risk of complications.DesignManually recorded vital signs data were paired with vital signs data derived from the remote monitor set in patients participating in the Trial of Remote versus Continuous INtermittent monitoring (TRaCINg) study: a trial of continuous remote vital signs monitoring.SettingSt James’s University Hospital, UK.Participants51 patients who had undergone major elective general surgery.InterventionsThe intervention was the SensiumVitals monitoring system. This is a wireless patch worn on the patient’s chest that measures HR, RR and temperature continuously. The reference standard was nurse-measured manually recorded vital signs.Primary and secondary outcome measuresThe primary outcomes were the 95% limits of agreement between manually recorded and wearable patch vital sign recordings of HR, RR and temperature. The secondary outcomes were the percentage completeness of vital sign patch data for each vital sign.Results1135 nurse observations were available for analysis. There was no clinically meaningful bias in HR (1.85 bpm), but precision was poor (95% limits of agreement −23.92 to 20.22 bpm). Agreement was poor for RR (bias 2.93 breaths per minute, 95% limits of agreement −8.19 to 14.05 breaths per minute) and temperature (bias 0.82°C, 95% limits of agreement −1.13°C to 2.78°C). Vital sign patch data completeness was 72.8% for temperature, 59.2% for HR and 34.1% for RR. Distributions of RR in manually recorded measurements were clinically implausible.ConclusionsThe continuous monitoring system did not reliably provide HR consistent with nurse measurements. The accuracy of RR and temperature was outside of acceptable limits. Limitations of the system could potentially be overcome through better signal processing. While acknowledging the time pressures placed on nursing staff, inaccuracies in the manually recorded data present an opportunity to increase awareness about the importance of manual observations, particularly with regard to methods of manual HR and RR measurements.


Author(s):  
Osman Yakubu ◽  
Emmanuel Wireko

The advent of the internet of things (IoT) has resulted in an upsurge in the deployment of digital health care systems enabling patients’ health conditions to be remotely monitored. This article presents an intelligent and automated IoT-based vital signs monitoring system to aid in patient care. A the oretical framework was established to guide the development of a prototype. It encompasses the patient, IoT sensors, input and storage unit and data processing, analysis and data transmission. The prototype is equipped with the capability of sensing a patient’s body temperature, heart rate, and respiration rate in real time and transmits the data to a cloud data repository for storage and analysis. Alerts are sent to caregivers using SMS, email and voice calls where urgent attention is required for the patient. The voice call isto ensure a caregiver does not miss the alert since SMS and email may not be checked on time. To ensure privacy of patients, a caregiver has to be biometrically verified by either fingerprint or facial pattern. The experimental results confirmed the accuracy of the data gathered by the prototype, privacy of patients is also guaranteed compared to other benchmark systems.


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