Human performance in harsh environments

Human performance in harsh environment Monitoring workers under thermal stress and cognitive loading ICARUS ARMOR is an ESA SPARK funded project developing a product for an active monitoring of stress and fatigue of individuals operating in harsh environments, such as firefighters, field police, and heavy machinery operators.

Challenge

The imposition of thermal stress on the human body can lead to a significant decrement in cognitive function, a critical factor when assessing the potential for accidents or serious incidents in occupational or extreme environments. Cognitive functions, including attention, memory, and decision-making, are particularly vulnerable to the effects of temperature extremes. These cognitive impairments can compromise safety, increasing the risk of errors and accidents in situations requiring high levels of attention and decision-making capabilities.

Fig 1. ICARUS ARMOR logo.

The relationship between thermal stress and cognitive dysfunction underscores the importance of mitigating these stressors to maintain cognitive integrity and prevent serious incidents. Understanding the mechanisms by which thermal stress affects cognitive function is crucial for developing effective interventions and policies to enhance human performance and safety in extreme environments. The active monitoring of the current stress level imposed on a person is thus a key aspect of assessment for the current stress level of an individual so these policies can be applied in reality.

Solution

The person under monitoring, who is under extreme stress, shall not have the comfort to self-determine if they are under or over some stress limit. That is the purpose of beforehand determining their custom human digital twin model. A general model was created first during a test campaign that took place in the climate chamber at the Faculty of Electrical Engineering and Communication of the Brno University of Technology (BUT). The experiments were designed to evaluate the cognitive performance and physiological responses of human subjects under two distinct climatic conditions, during which each subject undertakes a battery of cognitive tests. Throughout the duration of the test, physiological responses of the human participants are captured utilizing lightweight wearable non-invasive measuring devices.

Fig. 2. Aleš Svoboda (left) - a member of the ESA astronaut reserve, VIP participant of the testing campaign (photo BUT)

Volunteers participating in the test included Aleš Svoboda, an army pilot and member of the ESA astronaut reserve. All the data obtained from wearables were stored into the database. In principle the exact model of a wearable does not matter to be used in the system, which makes it open for a variety of users and use cases. Each type of measured biosignals were first processed and normalized by Uptimai for later use. Again, the process allows adding a type of measured biosignal if necessary. Based on the database, Uptimai prepared a set of mathematical models describing relations between physical responses of subject, environmental loading and cognitive loading. Building of these mathematical (or so-called surrogate) models takes only a few minutes of computational time so it is very easy to update them for either a group (e.g. workers of a certain profession) or an individual.

Fig. 3. Analysis of surrogate model in the Uptimai software

Also, models created within the ICARUS project were analyzed to identify the main physical responses related to environmental and cognitive loading. It was found that biosignals obtained using a breathing mask had only a minor response to cognitive loading in the test. Thus, it could be omitted from the set of wearables from the online monitoring because it is the most uncomfortable device for the user to wear and requires additional maintenance.

Fig. 4. Active monitoring of a person under thermal stress

Fig. 4 shows how Uptimai software and its models are used during the active monitoring of a user under environmental and cognitive stress. The person under stress is sending data from their wearables to the database. Data is then processed by Uptimai scripts and fed to the previously built Uptimai surrogate model. The model producess the computed level of the current equivalent cognitive loading of the test subject. Based on the previous testing campaign it is possible to state if the person under monitoring is closing to their limit (threshold) of the overall cognitive load that was found during the testing phase. In such cases the system sends an alert message to the operator responsible for withdrawing the person from the current task in a hostile environment.

Benefits

• Versatile sets of hardware. Monitoring can be enhanced with any type of wearable to respond to users and their custom needs.
• Models can be easily updated. Either for individuals or for professionals, building of mathematical models from biosignals is a matter of just minutes.
• Safe and efficient deployment of workers. System is warning people under active monitoring when their cognitive performance is compromised.