Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

Project Coolbit: Can your watch predict heat stress and thermal comfort sensation?

10.1088/1748-9326/abd130ENVIRONMENTAL RESEARCH LETTERS16

Design of a multi-channel high precision wearable temperature collection system based on negative temperature coefficient thermistor

Body temperature is often used to screen infectious diseases and monitor treatment. Through the method of measuring the resistance of constant voltage temperature measuring circuit, a wearable multi-point body temperature monitoring system is researched and designed to determine skin surface temperature. The STM32F103C8T6 chip is used as the core processor, and the negative temperature coefficient thermistor (NTC) as the temperature sensing component. ADS1256 chip is a temperature signal conditioner, Bluetooth module is a wireless transmission unit, and LABVIEW is used to design the host computer interface. The constant voltage bridge circuit composed of thermistor and resistor voltage divider to carry out the acquisition of 8 channels of temperature data, and the 24bits ultra-high-precision analog-to-digital conversion module is configured with differential inputs to amplify, filter and convert analog signals; the converted data is processed and calculated in the single-chip microcomputer; finally, the data is transmitted to the host computer via Bluetooth. The thermistor is linearly compensated using the fourth-order formulation of the Stein-hart formula. Reduce the impact of environmental interference and uneven body temperature distribution from software and hardware. The error during the temperature measurement of temperature sensor is analyzed. The experimental results showed that the resolution of measurement system reached 0. 01 , and the temperature measurement accuracy was up to ± 0. 02 . This design scheme has high stability and accuracy; and the circuit is simple in structure, small in size, and low power consumption which can be used in occasions requiring precise body temperature measurement

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system

AutoTherm: A Dataset and Ablation Study for Thermal Comfort Prediction in Vehicles

State recognition in well-known and customizable environments such as vehicles enables novel insights into users and potentially their intentions. Besides safety-relevant insights into, for example, fatigue, user experience-related assessments become increasingly relevant. As thermal comfort is vital for overall comfort, we introduce a dataset for its prediction in vehicles incorporating 31 input signals and self-labeled user ratings based on a 7-point Likert scale (-3 to +3) by 21 subjects. An importance ranking of such signals indicates higher impact on prediction for signals like ambient temperature, ambient humidity, radiation temperature, and skin temperature. Leveraging modern machine learning architectures enables us to not only automatically recognize human thermal comfort state but also predict future states. We provide details on how we train a recurrent network-based classifier and, thus, perform an initial performance benchmark of our proposed thermal comfort dataset. Ultimately, we compare our collected dataset to publicly available datasets

Data Driven Approach to Thermal Comfort Model Design

Apart from the dominant environmental factors such as relative humidity, radiant, and ambient temperatures, studies have confirmed that thermal comfort significantly depends on internal personal parameters such as metabolic rate, age, and health status. This study reviews the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters of a group of people in a space. PMV model equations adapted in ASHRAE Standard 55–Thermal Environmental Conditions for Human Occupancy, are used in this investigation to conduct a parametric study by generating and analyzing multi-dimensional comfort zone plots. It has been found that personal parameters such as metabolic rate and clothing have the highest impact. Current and newly emerging advancements in state of the art wearable technology have made it possible to continuously acquired biometric information. This work proposes to access and exploit this data to build a new innovative thermal comfort model. Relying on various supervised machine-learning methods, a thermal comfort model has been produced and compared to a general model to show its superior performance. Finally, the study represents an architecture to employ new thermal comfort model in inexpensive, responsive and extensible smart home service. Advisor: Fadi Alsalee

Effects of heat and personal protective equipment on thermal strain in healthcare workers: part B—application of wearable sensors to observe heat strain among healthcare workers under controlled conditions

Purpose As climate change accelerates, healthcare workers (HCW) are expected to be more frequently exposed to heat at work. Heat stress can be exacerbated by physical activity and unfavorable working requirements, such as wearing personal protective equipment (PPE). Thus, understanding its potential negative effects on HCW´s health and working performance is becoming crucial. Using wearable sensors, this study investigated the physiological effects of heat stress due to HCW-related activities. Methods Eighteen participants performed four experimental sessions in a controlled climatic environment following a standardized protocol. The conditions were (a) 22 °C, (b) 22 °C and PPE, (c) 27 °C and (d) 27 °C and PPE. An ear sensor (body temperature, heart rate) and a skin sensor (skin temperature) were used to record the participants´ physiological parameters. Results Heat and PPE had a significant effect on the measured physiological parameters. When wearing PPE, the median participants’ body temperature was 0.1 °C higher compared to not wearing PPE. At 27 °C, the median body temperature was 0.5 °C higher than at 22 °C. For median skin temperature, wearing PPE resulted in a 0.4 °C increase and higher temperatures in a 1.0 °C increase. An increase in median heart rate was also observed for PPE (+ 2/min) and heat (+ 3/min). Conclusion Long-term health and productivity risks can be further aggravated by the predicted temperature rise due to climate change. Further physiological studies with a well-designed intervention are needed to strengthen the evidence for developing comprehensive policies to protect workers in the healthcare sector

Wearable sensor technology to predict core body temperature : a systematic review

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers. © 2022 by the authors

Validity of a noninvasive estimation of deep body temperature when wearing personal protective equipment during exercise and recovery

©2019 The Authors. Published by BMC. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1186/s40779-019-0208-7© 2019 The Author(s). Background: Deep body temperature is a critical indicator of heat strain. However, direct measures are often invasive, costly, and difficult to implement in the field. This study assessed the agreement between deep body temperature estimated from heart rate and that measured directly during repeated work bouts while wearing explosive ordnance disposal (EOD) protective clothing and during recovery. Methods: Eight males completed three work and recovery periods across two separate days. Work consisted of treadmill walking on a 1% incline at 2.5, 4.0, or 5.5 km/h, in a random order, wearing EOD protective clothing. Ambient temperature and relative humidity were maintained at 24 °C and 50% [Wet bulb globe temperature (WBGT) (20.9 ± 1.2) °C] or 32 °C and 60% [WBGT (29.0 ± 0.2) °C] on the separate days, respectively. Heart rate and gastrointestinal temperature (TGI) were monitored continuously, and deep body temperature was also estimated from heart rate (ECTemp). Results: The overall systematic bias between TGI and ECTemp was 0.01 °C with 95% limits of agreement (LoA) of ±0.64 °C and a root mean square error of 0.32 °C. The average error statistics among participants showed no significant differences in error between the exercise and recovery periods or the environmental conditions. At TGI levels of (37.0-37.5) °C, (37.5-38.0) °C, (38.0-38.5) °C, and > 38.5 °C, the systematic bias and ± 95% LoA were (0.08 ± 0.58) °C, (-0.02 ± 0.69) °C, (-0.07 ± 0.63) °C, and (-0.32 ± 0.56) °C, respectively. Conclusions: The findings demonstrate acceptable validity of the ECTemp up to 38.5 °C. Conducting work within an ECTemp limit of 38.4 °C, in conditions similar to the present study, would protect the majority of personnel from an excessive elevation in deep body temperature (> 39.0 °C).This project was financially supported by the Australian Government, managed by the National Security Science & Technology Centre within the Defence Science & Technology Organisation, and the US Government through the Technical Support Working Group within the Combating Terrorism Technical Support Office.Published versio