Assessment of Physiological Responses During Field Science Task Performance: Feasibility and Future Needs

Abstract

ObjectiveBy understanding the physiological demands of different types of tasks that will be performed during extravehicular activity (EVA) on Mars, human performance safety risks can be mitigated. In addition, such understanding can assist in planning EVAs with an appropriate balance of human health and safety with scientific mission return.BackgroundThis paper describes the results of a study of technical feasibility performed within a Mars human research analog, with participants conducting scientifically relevant planetary science sample analysis and return tasks in two distinct field locations.MethodsThe authors collected heart rate, respiration rate, and heart rate variability (HRV) data, using commercial off-the-shelf hardware and software from study participants as they performed field science tasks within a concept of operations for a Mars science return human expedition mission. These data were remotely monitored, shared in real time, and later analyzed to identify different responses to different tasks in order to determine if there were any predictable or consistent patterns among participants.ResultsIt was ultimately determined that, while differences exist between responses to tasks, they are highly subject to multiple sources of individual variability, dynamics of evolving field science tasks, and demands of a demanding physical environment. Further, distributional analyses of participants do not support parametric statistical analysis techniques.ConclusionThe authors conclude that the physiology of individual astronauts should be extensively studied and modeled to support individualized automated monitoring tools for each crew member that is sent to Mars. Application: Physiological monitoring for specialized populations will require significant individual-level analysis, baselining, and bootstrap statistical methods to enable appropriate human performance determinations.

Assessment of Stress Level with Help of “Smart Clothing” Sensors, Heart Rate Variability-Based Markers and Machine Learning Algorithms

“…Typically, HRV, temperature, respiratory rate, skin sympathetic response and motility are measured with textile sensors. The search for stress markers can be carried out with the help of machine learning (ML) algorithms, which are especially well suited for nonlinear metrics [14]. In this study, we aimed to assess stress levels with HRV markers in healthy individuals in their daily activities using HRV parameters obtained from smart clothing sensors and processed with ML algorithm technologies.…”