scholarly journals Wearable Sensor Application for Integrated Early Warning and Health Surveillance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lauren E Charles ◽  
Devin P Wright ◽  
Zhuanyi Huang ◽  
Cree White ◽  
Fnu Anubhav ◽  
...  
Keyword(s):  
Health Status ◽  
Real Time ◽  
Early Warning ◽  
Wearable Sensors ◽  
Environmental Parameters ◽  
Sensor Data ◽  
Wearable Sensor ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index

Objective: The Wearable Sensor Application developed by Pacific Northwest National Laboratory (PNNL) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. The information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agency's Biosurveillance Ecosystem (BSVE).Introduction: Wearable devices are a low cost, minimally invasive way to monitor health. Sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. Information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. In addition, this data can be used to monitor individuals that are in high risk of health-related problems.However, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. Often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. In addition, the environment surrounding the individual needs to be considered when interpreting the data. To address these issues, PNNL has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals.Methods: The prototype application described here was a product of PNNL’s BSVE Application Development Competition. The final product that will be deployed in the BSVE is currently under development by PNNL and will vary slightly in the exact design and architecture described.Data. Wearable sensor data was collected from the Rim2Rim (R2R) Watch Study of individuals hiking the Grand Canyon in Arizona [1]. Weather information was obtained from nearby weather stations and mapping features were derived from Google Maps.Calculations. A physiological Strain Index (PSI) was calculated using core temperature estimates derived through a Kalman Filter approach and heart rate [2,3].Application. The prototype backend application development was based in Python with a MongoDB. The front-end development was built using a scalable architecture and modular approach with components in React and D3.Results: A prototype application was developed this past summer through the PNNL BSVE App Competition (Fig 1). The application was aimed at visualizing wearable sensor data from the Grand Canyon R2R hike dataset. Simulated real-time analysis was used to calculate health status of individuals hiking based on measured physiological parameters and to alert to individuals with signs of physiologic health stress. Visualization tools were incorporated to enable sensor data for individuals and the group to be viewed simultaneously along with pertinent weather, geographic, and elevation data.Many features described in the prototype application will be incorporated into the final BSVE application. The key changes will be 1) the ability to select given time periods for viewing historical data as well as the real-time data collection, 2) environmental data and map view will come from BSVE internal data sources, and 3) the alerts will provide more information and have their own page for reviewing.Conclusions: The Wearable Sensor Application developed by PNNL for integration into the BSVE provides an early warning system for individual and group health using physiologic and environmental parameters. The application highlights health status from wearable sensors and relevant environmental parameters while monitoring a calculated physiological strain index. With this tool, an analyst can easily monitor the health of individuals and groups with the aid of real-time alerting tool for early detection of abnormal health parameters.

scholarly journals Design of Early Warning System Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 66
Author(s):  
Gan Bo ◽  
Jin Shan
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sensor Data ◽  
Landslide Monitoring ◽  
Remote Transmission ◽  
Sensor Data Acquisition

In order to solve the shortcomings of the landslide monitoring technology method, a set of landslides monitoring and early warning system is designed. It can achieve real-time sensor data acquisition, remote transmission and query display. In addition, aiming at the harsh environment of landslide monitoring and the performance requirements of the monitoring system, an improved minimum hop routing protocol is proposed. It can reduce network energy consumption, enhance network robustness, and improve node layout and networking flexibility. In order to realize the remote transmission of data, GPRS wireless communication is used to transmit monitoring data. Combined with remote monitoring center, real-time data display, query, preservation and landslide warning and prediction are realized. The results show that the sensor data acquisition system is accurate, the system is stable, and the node network is flexible. Therefore, the monitoring system has a good use value.

Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

2020 ◽  
Vol 11 (4) ◽  
pp. 57-71
Author(s):  
Qiuxia Liu
Keyword(s):  
Data Fusion ◽  
Environmental Monitoring ◽  
Real Time ◽  
Monitoring System ◽  
Intelligent System ◽  
Evidence Synthesis ◽  
Environmental Parameters ◽  
Monitoring Data ◽  
Sensor Data ◽  
Environmental Monitoring System

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

scholarly journals Automated P-Wave Quality Assessment for Wearable Sensors

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 13
Author(s):  
Diogo Tecelão ◽  
Peter Charlton
Keyword(s):  
At Risk ◽  
Quality Assessment ◽  
Wearable Sensors ◽  
P Wave ◽  
Sensor Data ◽  
Wearable Sensor ◽  
High Quality ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Morphology

Hospital patients recovering from major cardiac surgery are at risk of paroxysmal atrial fibrillation (AF), an arrhythmia which can be life-threatening. Wearable sensors are routinely used for electrocardiogram (ECG) monitoring in patients at risk of AF, providing real-time AF detection. However, wearable sensors could have greater impact if used to identify the subtle changes in P-wave morphology which precede AF. This would allow prophylactic treatment to be administered, potentially preventing AF. However, ECG signals acquired by wearable sensors are susceptible to artefact, making it difficult to distinguish between physiological changes in P-wave morphology, and changes due to noise. The aim of this study was to design and assess the performance of a novel automated P-wave quality assessment tool to identify high-quality P-waves, for AF prediction. We designed a two-stage algorithm which uses P-wave template-matching to assess quality. Its performance was assessed using the AFPDB, a database of wearable sensor ECG signals acquired from both healthy subjects and patients susceptible to AF. The algorithm’s quality assessments of 97,989 P-waves were compared to manual annotations. The algorithm identified high-quality P-waves with high sensitivity (93%) and good specificity (82%), indicating that it may have utility for identifying high-quality P-waves in wearable sensor data. Measurements of P-wave morphology derived from high-quality P-waves could be used to predict AF, improving patient outcomes, and reducing healthcare costs. Further studies assessing the clinical utility of the presented tool are warranted for validation.

Establishing a Physiological Strain Index Criterion During the Final Sixty Minutes of Heat Tolerance Testing

2019 ◽  
Vol 51 (Supplement) ◽  
pp. 566
Author(s):  
Douglas M. Jones ◽  
Andrew J. Ordille ◽  
Katherine M. Wilson ◽  
Jay H. Heaney
Keyword(s):  
Heat Tolerance ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index ◽  
Tolerance Testing

scholarly journals What Does Big Data Mean for Wearable Sensor Systems?

2014 ◽  
Vol 23 (01) ◽  
pp. 135-142 ◽  
Author(s):  
N. H. Lovell ◽  
G. Z. Yang ◽  
A. Horsch ◽  
P. Lukowicz ◽  
L. Murrugarra ◽  
...  
Keyword(s):  
Big Data ◽  
Health Management ◽  
Wearable Sensors ◽  
Scientific Progress ◽  
Sensor Data ◽  
Sensor Systems ◽  
Wearable Sensor ◽  
Negative Consequences ◽  
Inference Problems ◽  
Care Working Group

Summary Objectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods: The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Health-care Working Group. Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.

scholarly journals Predicting Compensable versus Uncompensable Heat Stress from Physiological Strain Index

2004 ◽  
Vol 36 (Supplement) ◽  
pp. S316
Author(s):  
Thomas E. Bernard ◽  
Candi D. Ashley ◽  
Victor Caravello
Keyword(s):  
Heat Stress ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index ◽  
Uncompensable Heat Stress

Predicting Compensable versus Uncompensable Heat Stress from Physiological Strain Index

2004 ◽  
Vol 36 (Supplement) ◽  
pp. S316
Author(s):  
Thomas E. Bernard ◽  
Candi D. Ashley ◽  
Victor Caravello
Keyword(s):  
Heat Stress ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index ◽  
Uncompensable Heat Stress

scholarly journals Effects of human head hair on performance and thermoregulatory responses during 10-km outdoor running in healthy men

2016 ◽  
Vol 18 (2) ◽  
pp. 155
Author(s):  
Angelo Ruediger Pisani Martini ◽  
João Batista Ferreira-Júnior ◽  
Daniel Barbosa Coelho ◽  
Diego Alcântara Borba ◽  
Leonardo Gomes Martins Coelho ◽  
...  
Keyword(s):  
Heart Rate ◽  
Rectal Temperature ◽  
Heat Storage ◽  
Human Head ◽  
Physiological Strain ◽  
Strain Index ◽  
Healthy Men ◽  
Thermoregulatory Responses ◽  
Head Hair ◽  
Physiological Strain Index

DOI: http://dx.doi.org/10.5007/1980-0037.2016v18n2p155 The aim of the present study was to evaluate the effects of human head hair on performance and thermoregulatory responses during 10-km outdoor running in healthy men. Twelve healthy males (29.5 ± 3.7 years, 174.9 ± 4.3 cm, 72.7 ± 3.2 kg and VO2max 44.6 ± 3.4 ml.kg-1.min-1) participated in two self-paced outdoor 10-km running trials separated by 7 days: 1) HAIR, subjects ran with their natural head hair; 2) NOHAIR, subjects ran after their hair had been totally shaved. Average running velocity was calculated from each 2-km running time. Rectal temperature, heart rate and physiological strain index were measured before and after the 10-km runs and at the end of each 2 km. The rate of heat storage was measured every 2 km. The environmental stress (WBGT) was measured every 10 min. The running velocity (10.9 ± 1 and 10.9 ± 1.1 km.h-1), heart rate (183 ± 10 and 180 ± 12 bpm), rectal temperature (38.82 ± 0.29 and 38.81 ± 0.49oC), physiological strain index (9 ± 1 and 9 ± 1), or heat storage rate (71.9 ± 64.1 and 80.7 ± 56.7 W.m-1) did not differ between the HAIR and NOHAIR conditions, respectively (p>0.05). There was no difference in WBGT between the HAIR and NOHAIR conditions (24.0 ± 1.4 and 23.2 ± 1.5ºC, respectively; p=0.10). The results suggest that shaved head hair does not alter running velocity or thermoregulatory responses during 10-km running under the sun.

Can gender differences during exercise-heat stress be assessed by the physiological strain index?

1999 ◽  
Vol 276 (6) ◽  
pp. R1798-R1804 ◽  
Author(s):  
Daniel S. Moran ◽  
Yair Shapiro ◽  
Arie Laor ◽  
Sharona Izraeli ◽  
Kent B. Pandolf
Keyword(s):  
Gender Differences ◽  
Heat Stress ◽  
Sweat Rate ◽  
Physiological Strain ◽  
Strain Index ◽  
Wide Acceptance ◽  
Physiological Strain Index ◽  
Lower Strain ◽  
And Gender ◽  
C 50

A physiological strain index (PSI) based on rectal temperature (Tre) and heart rate (HR) was recently suggested to evaluate exercise-heat stress. The purpose of this study was to evaluate PSI for gender differences under various combinations of exercise intensity and climate. Two groups of eight men each were formed according to maximal rate of O2 consumption (V˙o 2 max). The first group of men (M) was matched to a group of nine women (W) with similar ( P > 0.001)V˙o 2 max (46.1 ± 2.0 and 43.6 ± 2.9 ml ⋅ kg−1 ⋅ min−1, respectively). The second group of men (MF) was significantly ( P < 0.001) more fit than M or W with V˙o 2 max of 59.1 ± 1.8 ml ⋅ kg−1 ⋅ min−1. Subjects completed a matrix of nine experimental combinations consisting of three different exercise intensities for 60 min [low, moderate, and high (300, 500, and 650 W, respectively)] each at three climates {comfortable, hot wet, and hot dry [20°C 50% relative humidity (RH), 35°C 70% RH, and 40°C 35% RH, respectively]}. No significant differences ( P > 0.05) were found between matched genders (M and W) at the same exposure for sweat rate, relativeV˙o 2 max(%V˙o 2 max), and PSI. However, MF had significantly ( P < 0.05) lower strain than M and W as reflected by %V˙o 2 max and PSI. In summary, PSI applicability was extended for exercise-heat stress and gender. This index continues to show potential for wide acceptance and application.

A Real-Time Case Study in Driver Science: Physiological Strain and Related Variables

2015 ◽  
Vol 10 (8) ◽  
pp. 1058-1060 ◽  
Author(s):  
Edward S. Potkanowicz
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Core Body Temperature ◽  
Time Data ◽  
Physiological Strain ◽  
Physiological Strain Index ◽  
Probe System ◽  
Future Work ◽  
Core Body

This case study was conducted as an attempt to quantify racecar-driver core body temperature and heart rate (HR) in real time on a minute-by-minute basis and to expand the volume of work in the area of driver science. Three drivers were observed during a 15-lap, 25-min maximal event. Each driver competed in the closed-wheel, closed-cockpit sports-car category. Data on core body temperature and HR were collected continuously using the HQ Inc. ingestible core probe system and HR monitoring. Driver 1 pre- and postrace core temperatures were 37.80°C and 38.79°C, respectively. Driver 2 pre- and postrace core temperatures were 37.41°C and 37.99°C. Driver 1 pre- and postrace HRs were 102 and 161 beats/min. Driver 2 pre- and postrace HRs were 94.3 and 142 beats/min. Driver 1’s physiological strain index (PSI) at the start was 3.51. Driver 2’s PSI at the start was 3.10. Driver 1 finished with a PSI of 7.04 and driver 2 with a PSI of 3.67. Results show that drivers are continuously challenged minute by minute. In addition, before getting into their cars, the drivers already experience physiological and thermal challenges. The data suggest that drivers are getting hot quickly. In longer events, this represents the potential for severe heat injury. Investigating whether the HRs observed are indicative of work or evidence of a thermoregulatory-associated challenge is a direction for future work. The findings support the value of real-time data collection and offer strong evidence for the expansion of research on driver-athletes.

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