Comprehensive monitoring of firefighters by a Wireless Body Area Sensor Network

Ghosh, Sudip; Chakraborty, Suryadip; Jamthe, Anagha; Agrawal, Dharma P.

doi:10.1109/wocn.2013.6616245

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…Temperature, humidity, and wind speed Anania et al 206 Sensor network Firefighters' fall detection Coca et al 207 Plethysmographic sensor vest Physiological monitoring Ghosh et al 208 Accelerometer sensor, body temperature sensor, pressure sensor (Sphygmomanometer), oxygen in blood sensor (SP02), and ECG Determine position, temperature, oxygen pressure, and beat rhythm SHT: humidity sensor; ECG: electrocardiography.…”

Section: Discussionmentioning

confidence: 99%

Challenges and limitation of wearable sensors used in firefighters’ protective clothing

Shakeriaski

Ghodrat

2022

Journal of Fire Sciences

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This review aims to present recent improvements and existing challenges in the design of wearable sensors used in the firefighters’ protective clothing. Wearable sensors are generally used directly on the body or placed on wearable items to monitor data for the safety of firefighters. Recently, wearable sensors have attracted much attention from researchers and experts. Most investigations have addressed novel designs for wearable sensors to enhance firefighters’ safety measures and reduce the risk of exposure to fires. This article is an attempt to review design limitations of wearable sensors for future developments and improve existing shortcomings. The growing body of knowledge focused on the application of wearable technology to monitor firefighters’ activity, health, and body temperature. In the following, we have discussed the trials of the design of the existing sensors. Finally, moisture and radiation as common exterior parameters in fire events are discussed which received less attention and have major impact on the performance of firefighters’ wearable sensors.

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Section: Discussionmentioning

confidence: 99%

Challenges and limitation of wearable sensors used in firefighters’ protective clothing

Shakeriaski

Ghodrat

2022

Journal of Fire Sciences

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“…Many techniques report on the use of sensors to monitor firefighters' health and warn them of any threats to their safety. Several safety areas are being considered including tracking indoor location of first responders [13]- [15], physiological monitoring [16]- [18], and communication [19], [20]. There have also been substantial advancements in the research and development of intelligent threat assessment systems for firefighters.…”

Section: Background and Related Workmentioning

confidence: 99%

An Autoregressive Exogenous Neural Network to Model Fire Behavior via a Naïve Bayes Filter

et al. 2020

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This work presents an artificial neural network-based linearly regressive technique for the prediction of a temperature rise event caused by a fire in enclosed building environments. The method predicts temperature range in a burning compartment based on the historic fire behavior data modelled via a neural network algorithm. The approach further extends the method by transforming the regression outcome as actionable information for firefighters via a self-organising feature map (SOM) fire-stageclustering algorithm which categorises the predicted temperature range to provide warnings of any imminent and catastrophic temperature variations. The methodology implements a linear regression artificial neural model to model and predicts catastrophic temperature variations that are a threat to fire-and-rescue personnel in enclosed burning compartments via a ceiling gas-layer temperature acting as an exogenous variable. Based on the predicted temperature window, the SOM classifier maps the output in distinct clusters while isolating spurious prediction spikes and categorising the output to various self-organising threat/warning levels. The model was trained on temperature data captured with pole-mounted sensors in controlled fire training exercises. Tests carried out in actual fire-settings showed the capability of the SOM classifier to generate evacuation warnings for forecasted temperatures exceeding 300 C in 5-30 seconds ahead of the occurrences. Tests on these short-term and long-term prediction ranged from 78.31% to 96.27% in accuracy whereas the SOM classifier generated an overall accuracy of 76.92% to 100%. The techniques also showed a high resilience against both false-positives and false-negatives. INDEX TERMS Artificial neural networks, fire behaviour modelling, linear regression, self-organising feature maps.

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“…In [44] authors used RSSI (Received Signal Strength indicator) values of nodes in order to find the location of firefighters working in the field. Their location tracking scheme will locate the firefighter and also monitor the different vital parameter such as CO (Carbon monoxide) and HCN (Hydrogen Cyanide) in the environment and send the information to fire chief which make enable him to take appropriate decision about the health of his valued firefighter.…”

Section: Human Activity Monitoringmentioning

confidence: 99%

Application specific study, analysis and classification of body area wireless sensor network applications

et al. 2015

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Comprehensive monitoring of firefighters by a Wireless Body Area Sensor Network

Cited by 10 publications

References 4 publications

Challenges and limitation of wearable sensors used in firefighters’ protective clothing

Challenges and limitation of wearable sensors used in firefighters’ protective clothing

An Autoregressive Exogenous Neural Network to Model Fire Behavior via a Naïve Bayes Filter

Application specific study, analysis and classification of body area wireless sensor network applications

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