3
monoxide [22]. These helmets also incorporate brain activ-
ity sensors to gauge worker fatigue and issue alerts when
hazardous gas concentrations exceed safe levels. However,
some users expressed skepticism regarding the cost-effi-
ciency and efficacy of technology that purports to measure
brain activity with significant motion artifacts present. In
situations of high risk, a sensor module integrating multi-
ple sensors for real-time gas monitoring, coupled with con-
tinuous tracking of individual biometric health data, can
offer precise insights to control stations regarding chemical
exposure levels and their long-term implications. Therefore,
emerging wearable technologies capable of collecting data
on human performance and monitoring physiological
parameters show potential for acquiring directly measured
data that can provide valuable health and safety insights
for mine workers [21]. Still, the current practice of health
analysis remains periodic and relies heavily on voluntary
participation [1]. Therefore, developing a data analytics
framework that includes medical features and patterns of
physiological health parameters for each individual miner is
imperative to understanding the mining-related factors that
may lead to long-term health complications [1].
Nevertheless, the scale of health monitoring solutions,
considering the number of workers and the daily data col-
lection required to prevent future health risks, generates
a significantly large volume of data. To manage this data
effectively, technology, such as artificial intelligence (AI),
could be instrumental in analyzing vast datasets and identi-
fying trends that help comprehend when and how these dis-
eases occur. Consequently, wearable devices, continuously
monitoring both environmental conditions and individu-
als’ vital signs, hold the potential to contribute significantly
to the early detection of diseases and the understanding of
disease formation. An evaluation of the suitability of wear-
able health monitors for the mining industry in eight dis-
tinct areas related to heat stress, physical strain, respiratory
health, fatigue, environmental conditions, ergonomics,
incident monitoring, and long-term trends and patterns is
summarized in Table 1. Wearable sensors could offer valu-
able support in the assessment of miners’ health and safety
by maintaining continuous monitoring of individual physi-
ological indicators, thereby, reducing or eliminating the
reliance on generic environmental assessments.
In this paper, preliminary trial results of the application
of wearable sensors to monitor heat stress and other vital
biometrics for mines rescue personnel are presented, con-
firming the feasibility of using wearable sensors and associ-
ated analytics for real-time monitoring of biometric data in
extreme environmental conditions.
TESTING METHODOLOGY
Field trials of wearable sensors and associated applications
were conducted by Simtars’ Mine Safety at Queensland
Mine Rescue Station (QMRS) in Dysart, Queensland,
Australia, over 3 consecutive days. Both quantitative data,
through sensor measurements and analytics, and qualitative
data, via questionnaires, were collected during and after the
trial, respectively.
Variables and Participants
The variables measured or estimated during the trial
included HR, TC, motion, work intensity, humidity, wind
speed, and air temperature. The ambient environmental
conditions were measured using a combination of portable
chemical sensors and internet-based weather sources.
A total of eight participants, ranging in age from 24 to
44 years old (average of 34) were included in the testing.
Their typical workdays (outside of training as mine rescu-
ers) ranged from office workers with only occasional forays
into a hot mine to stope miners who spent 12-hour shifts
in the heat.
Devices and Applications
The participants were equipped with the Polar Verity Sense
(PVS) sensor measuring the participants’ HR and motion
intensity and enabling real-time data collection and analy-
sis using the SafeGuard Live mobile application (Figure 2a,
2b, 2c, 2d), which served as a central platform for data
aggregation, management, and remote viewing. To moni-
tor the environmental conditions during the study, the
RKI GX-3R Pro portable monitor was used by the trainer
for real-time detection and measurement of various gases
including oxygen, flammable gases, carbon monoxide, and
hydrogen sulfide, which can exist in underground spaces
such as mines (Figure 2e).
The PVS streamed HR values at a rate of about 1hz
to SafeGuard installed on a mobile device. SafeGuard’s
algorithm library then estimated TC and metabolic rate
(Figure 3a). The PVS uses Bluetooth and ANT+ telemetry
protocols and it has the capacity to stream to two Bluetooth
devices without any limitations on the number of ANT+
devices [26]. All data was timestamped and stored on both
the participants’ mobile phones and within the SafeGuard
cloud when internet connectivity was available. The GX-3R
Pro streamed second-by-second data using Bluetooth proto-
cols to SafeGuard for visualization and storage by both the
participant and remote safety attendants. GoPro Cameras
were utilized to visually document the participants’ activi-
ties throughout the testing phases.
monoxide [22]. These helmets also incorporate brain activ-
ity sensors to gauge worker fatigue and issue alerts when
hazardous gas concentrations exceed safe levels. However,
some users expressed skepticism regarding the cost-effi-
ciency and efficacy of technology that purports to measure
brain activity with significant motion artifacts present. In
situations of high risk, a sensor module integrating multi-
ple sensors for real-time gas monitoring, coupled with con-
tinuous tracking of individual biometric health data, can
offer precise insights to control stations regarding chemical
exposure levels and their long-term implications. Therefore,
emerging wearable technologies capable of collecting data
on human performance and monitoring physiological
parameters show potential for acquiring directly measured
data that can provide valuable health and safety insights
for mine workers [21]. Still, the current practice of health
analysis remains periodic and relies heavily on voluntary
participation [1]. Therefore, developing a data analytics
framework that includes medical features and patterns of
physiological health parameters for each individual miner is
imperative to understanding the mining-related factors that
may lead to long-term health complications [1].
Nevertheless, the scale of health monitoring solutions,
considering the number of workers and the daily data col-
lection required to prevent future health risks, generates
a significantly large volume of data. To manage this data
effectively, technology, such as artificial intelligence (AI),
could be instrumental in analyzing vast datasets and identi-
fying trends that help comprehend when and how these dis-
eases occur. Consequently, wearable devices, continuously
monitoring both environmental conditions and individu-
als’ vital signs, hold the potential to contribute significantly
to the early detection of diseases and the understanding of
disease formation. An evaluation of the suitability of wear-
able health monitors for the mining industry in eight dis-
tinct areas related to heat stress, physical strain, respiratory
health, fatigue, environmental conditions, ergonomics,
incident monitoring, and long-term trends and patterns is
summarized in Table 1. Wearable sensors could offer valu-
able support in the assessment of miners’ health and safety
by maintaining continuous monitoring of individual physi-
ological indicators, thereby, reducing or eliminating the
reliance on generic environmental assessments.
In this paper, preliminary trial results of the application
of wearable sensors to monitor heat stress and other vital
biometrics for mines rescue personnel are presented, con-
firming the feasibility of using wearable sensors and associ-
ated analytics for real-time monitoring of biometric data in
extreme environmental conditions.
TESTING METHODOLOGY
Field trials of wearable sensors and associated applications
were conducted by Simtars’ Mine Safety at Queensland
Mine Rescue Station (QMRS) in Dysart, Queensland,
Australia, over 3 consecutive days. Both quantitative data,
through sensor measurements and analytics, and qualitative
data, via questionnaires, were collected during and after the
trial, respectively.
Variables and Participants
The variables measured or estimated during the trial
included HR, TC, motion, work intensity, humidity, wind
speed, and air temperature. The ambient environmental
conditions were measured using a combination of portable
chemical sensors and internet-based weather sources.
A total of eight participants, ranging in age from 24 to
44 years old (average of 34) were included in the testing.
Their typical workdays (outside of training as mine rescu-
ers) ranged from office workers with only occasional forays
into a hot mine to stope miners who spent 12-hour shifts
in the heat.
Devices and Applications
The participants were equipped with the Polar Verity Sense
(PVS) sensor measuring the participants’ HR and motion
intensity and enabling real-time data collection and analy-
sis using the SafeGuard Live mobile application (Figure 2a,
2b, 2c, 2d), which served as a central platform for data
aggregation, management, and remote viewing. To moni-
tor the environmental conditions during the study, the
RKI GX-3R Pro portable monitor was used by the trainer
for real-time detection and measurement of various gases
including oxygen, flammable gases, carbon monoxide, and
hydrogen sulfide, which can exist in underground spaces
such as mines (Figure 2e).
The PVS streamed HR values at a rate of about 1hz
to SafeGuard installed on a mobile device. SafeGuard’s
algorithm library then estimated TC and metabolic rate
(Figure 3a). The PVS uses Bluetooth and ANT+ telemetry
protocols and it has the capacity to stream to two Bluetooth
devices without any limitations on the number of ANT+
devices [26]. All data was timestamped and stored on both
the participants’ mobile phones and within the SafeGuard
cloud when internet connectivity was available. The GX-3R
Pro streamed second-by-second data using Bluetooth proto-
cols to SafeGuard for visualization and storage by both the
participant and remote safety attendants. GoPro Cameras
were utilized to visually document the participants’ activi-
ties throughout the testing phases.