Real-Time Vital Signs Monitoring System Towards Livestock Health Furtherance

Author(s):  
Ana Rita Reigones ◽  
Pedro D. Gaspar
2021 ◽  
Author(s):  
Yu Gu ◽  
Xiang Zhang ◽  
Huan Yan ◽  
Zhi Liu ◽  
Fuji Ren

High-quality sleep is essential to our daily lives, and real-time monitoring of vital signs during sleep is beneficial. Current sleep monitoring solutions are mostly based on wearable sensors or cameras, the former is worse for sleep quality, the latter is worse for privacy, dissimilar to such methods, we implement our sleep monitoring system based on COTS WiFi devices. There are two challenges need to be overcome in the system implementation process: First, the torso deformation caused by breathing/heartbeat is weak, how to effectively capture this deformation? Second, movements such as turning over will affect the accuracy of vital signs monitoring, how to quickly distinguish such movements? For the former, we propose a motion detection capability enhancement method based on Rice-K theory and Fresnel theory. For the latter, we propose a sleep motion positioning algorithm based on regularity detection. The experimental results indicated the performance of our method.


2014 ◽  
Vol 80 (3) ◽  
pp. 218
Author(s):  
N. Lo ◽  
A. Navlekar ◽  
E. Palmgren ◽  
R. Rekhi ◽  
F. Ussher ◽  
...  

2021 ◽  
Author(s):  
Yu Gu ◽  
Xiang Zhang ◽  
Huan Yan ◽  
Zhi Liu ◽  
Fuji Ren

High-quality sleep is essential to our daily lives, and real-time monitoring of vital signs during sleep is beneficial. Current sleep monitoring solutions are mostly based on wearable sensors or cameras, the former is worse for sleep quality, the latter is worse for privacy, dissimilar to such methods, we implement our sleep monitoring system based on COTS WiFi devices. There are two challenges need to be overcome in the system implementation process: First, the torso deformation caused by breathing/heartbeat is weak, how to effectively capture this deformation? Second, movements such as turning over will affect the accuracy of vital signs monitoring, how to quickly distinguish such movements? For the former, we propose a motion detection capability enhancement method based on Rice-K theory and Fresnel theory. For the latter, we propose a sleep motion positioning algorithm based on regularity detection. The experimental results indicated the performance of our method.


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.


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