Hello everyone, my name is Chee Jen Ngeh and I’m joined with my other teammates,
Jiali Zhang and He Feng. The topic for our paper is “Designing a Smart
Helmet for Wildland Firefighters to Avoid Dehydration by Monitoring Bio-signals”.
As we all know, firefighters face incredibly harsh conditions, while performing highly demanding
physical work. What we often don’t see is what happens when they go over their limits, when
they break due to overexertion – dizziness, hard time talking, throwing up, or even collapsing.
In fact, if we look back on the past 20 years, over 300 firefighters have passed away while in
combat. The most shocking stats might even be the fact that over 60% of those fatalities were
caused by overexertion, and that every other firefighter out there has, at some point,
seen someone collapsing in the field Today, firefighters rely exclusively on
observation to both report and try to anticipate these situations. And often, that’s just too late.
We believe the status quo has to change and that technology can help prevent these incidents.
That’s the main premise for our work. Next, I will talk about our solution
Since we want to use technology to prevent incidents from happening to wildland firefighters,
our solution is to use heart rate, temperature,
and movement data to alert them and their supervisors before incidents happen.
There are two components to our solution, the smart helmet, and the mobile app. The helmet works
independently to collect data and will alert the firefighter when abnormal health data is detected.
The app enables the user to view their health data and also to transmit it to their supervisors.
If there is a potential risk to a firefighter, the supervisor would be alerted with a notification.
Next, Jiali will talk about the prototype of the helmet
In order to get heart rate, temperature, and movement data, we used a heart rate sensor,
infrared temperature sensor, and an accelerometer The raw data from the sensors are then processed
on the Feather Board using our algorithm. If abnormal health data is detected we would
nudge and alert the user using our array of actuators which includes the neopixel light,
vibration motor, and a buzzer And all these are powered by
three AAA batteries as they are also currently being used for headlamps
To complement our helmet-mounted device, we developed a mobile app using React
Native – to be used by both firefighters working on the line and their supervisors.
The Firefighter side focuses on collecting profile information, some enhancing the onboard
algorithm like age, and establishing the connection with the helmet. Also,
firefighters can provide feedback on how they are feeling during breaks, as seen on the right.
On the other hand, a supervisor has an extra tab available. There, they can create a new crew, and
manage it – being able to visualize crew members' health data and eventual alerts in real-time.
In order to get the stable data for analysis in our algorithm, we collect the heart rate
on the temple, the temperature on the forehead, and Accelerometer data on the top of the head.
Based on these biosignal and corresponding ranges for different physiological status,
we can clearly understand the underlying problem when our firefighter is working on the field.
These problems, or, events can be divided into 4 categories based on their severity,
which are emergency situations, drinking water, falling down, and helmets off.
At the end of the project, we ran the full-term function evaluation to prove that our product
works as we planned. We initially tested the FireWorks App and the alert system,
which are the core section of our project. Then we did further testing to see whether
our helmet has a reliable data pipeline and whether it can run over a long time.
The testing result is convincing. All of the implemented functions in the Apps work properly,
and the supervisor notification and location information at the end of this slide
are two functions that we plan to implement in the next generation of the helmet in the future.
All of the nine events in the algorithm can be triggered successfully,
and we can see a clear notification for each event as we discussed in the previous section.
From the charts on the top, we can see that our data pipeline is reliable to
transmit data during works. We can also see from the bottom chart that the temperature
data is very stable except for a half-second data loss during the long-duration testing,
and our heart rate data is stable all the time. Finally, to talk about the possible future work.
We are trying to figure out a peer-to-peer communication channel which can be used in
the absence of cellular networks. We also prepare to implement the location
features such that crews can keep track of their members. Most important point is,
we are trying to upgrade our product into an attachable standalone device
such that our product can realize mass production earlier and also fit on more types of helmets.
That’s all we have in this presentation. Thanks for your time. We are open for questions.