Collaboration, cooperation, and coordination are essential underpinnings of effective teamwork. This is especially the case for “high reliability” action teams, such as long duration space flight crews, that have to perform in isolated, confined, and extreme environments. For such teams, effective teamwork is critical for minimizing errors and supporting team performance, and it is reflective of good psycho-social adaptation by the team to the rigors and stresses of long duration space missions.
This project was an extension of work previously funded by NASA. That research had two primary research foci: (1) benchmarking the variability, cycles, and fluctuation of team cohesion in a range of isolated, confined, and extreme (ICE) mission analogs and (2) developing a team interaction sensor (TIS) technology platform – a badge - designed to unobtrusively measure team interactions and physiological reactions in real time as a means to assess potential stressors to team cohesiveness.
The first research focus, which is concluded, used Experience Sampling Methods (ESM; daily assessments) to benchmark team functioning in ICE environments. The research was designed to quantify the expected range of variation in key teamwork processes (e.g., cohesion, conflict), identify shocks that influence variation, and assess effects on team functioning. Findings, across a range of ICE environments and mission durations (i.e., 6 weeks to 1 year in Antarctica; 1 month to 12 months in dedicated space mission simulations), indicated that team psycho-social functioning during long duration missions was challenged. These challenges were particularly apparent for simulation teams on missions longer that 6 months. Specifically, such teams evidenced destabilization and decline of social cohesion – the psychological glue that bonds members to the team as an entity. This evidence makes clear the need for a means to help team members assess their teamwork interactions and to take necessary steps to maintain it in the face of persistent mission stressors. This is relevant to the goal of the second research focus.
The second research focus is the development of an unobtrusive technology platform (i.e., a wearable wireless sensor package) – the TIS – to assess the dynamics of team cohesion. The technology is designed to assess the frequency, duration, and quality of collaborative interactions between team members as they work together to accomplish team tasks, as well as physiological metrics (i.e., heart rate [HR]; heart rate variability [HRV]). The high frequency interaction data streamed by the badges are highly reliable and valid. In addition, experimental evidence indicated that positive and negative affective reactions to specific team member interactions could be predicted from badge data. Specifically, interactions between team members that involved a cognitive stressor were associated with negative affect and performance failures for the stressed team member. HR predicted both affective states (activation). However, negative affect (valence) was predicted by a significant interaction between HR and HRV. This was considered a very promising finding, because it indicated that the psychosocial status of team members could be inferred analytically from interaction metrics (who was interacting) combined with HR and HRV (who had a negative affective reaction) data streams that are monitored by the badges.
Although this was considered a promising finding, the current project was predicated on adding an additional sensor – galvanic skin response (GSR) – to the TIS array. The purpose was to improve reliable detection of crew anomalies using TIS data streams. Detecting affective states using HR and HRV is complicated, because they reflect the activities of the autonomic nervous system (ANS). The ANS consists of sympathetic and parasympathetic branches, which are associated with activation and relaxation, respectively. Importantly, different indicators of ANS activity reflect different branches. GSR predominantly reflects sympathetic activity, HR reflects a combination of sympathetic and parasympathetic activities, and HRV is linked to parasympathetic activity. Research suggests that combining multiple indicators of ANS activity increases sensitivity to distinguish affective valence and activation, which is essential for discriminating positive and negative affective states. Distinguishing and classifying these states is critical to the effectiveness of the TIS system to detect anomalies in team cohesion. Thus, the purpose of this project was to extend technology development of the TIS system and to evaluate the utility of the additional GSR sensor to improve discrimination of positive and negative affective states.
The sensor platform had evolved from an early prototype in which the sensors and processing components were housed in a cardboard box (Version 1.0) to a subsequent package utilizing a 3D printed case (Version 2.0) to a more robust hardened 3D printed case (Version 2.5). Technology development was predicated on maintaining the form factor, robust casework, and good battery life (approximately 12-16 hours of continuous usage) of the TIS badges.
Technology development devised the hardware, embedded software, and storage software components for adding GSR as an additional sensing modality to the existing badge platform. The design was system-wide backward compatible in that the usability and prior functions of the TIS platform were maintained. The GSR sensor was designed to be pluggable to the badge using a wired interface. Upon detection, the badge and base station software self-configures to sense, collect, and store GSR data following the standard protocols employed for the other sensing modalities. The design was implemented such that the wired interface could be replaced by a Bluetooth (BT) interface.
The following components were designed and developed:
• GSR Hardware Integration: An off-the-shelf GSR sensor was used. Interface hardware was developed so the sensor connects to the badge processor using a wired interface. Functions of this hardware include signal conditioning, filtering, and Analog to Digital conversion.
• Driver Software: TinyOS driver software for GSR signal acquisition and integration with the existing badge to base station networking software was developed.
• Base Station Software: Software for integrating GSR data with existing sensor data streams was developed, providing a seamless integration of this new modality with the existing dashboard.
• Sensor Post-processing Software: Data processing software was developed to properly scale GSR data for integration within the existing data framework.
• Case Design: The 3D-printed badge casework was redesigned so the additional GSR-hardware could be packaged without a significant increase in the badge form-factor.
Technology Transfer to NASA: During 2017-2018, the engineering team led by Dr. Subir Biswas worked closely with the NASA Wearable Electronics Application and Research (WEAR) Lab to transfer TIS hardware and software capabilities developed by the Michigan State University research team to NASA. The WEAR Lab has been engineering a new hardware and software platform that replicates the capabilities of the TIS that was developed by this research stream.
GSR Sensor Rationale: Prior phased validation evidence demonstrated that the badge technology (1) is a highly reliable and accurate instrument for capturing team member interaction characteristics. Moreover, (2) a time pressure manipulation designed to differentially induce stress on interactions between teams (i.e., experimental vs. control) was significantly detected by badge heart rate (HR) metrics and ratings of affect following stressed interactions. Furthermore, (3) a cognitive test (CT) manipulation designed to stress interactions was shown to yield increases in HR mean and HR variance (HRV; controlling for baseline HR and HRV) and CT yielded increased negative affectivity (NA) and decreased positive affectivity (PA). Importantly, additional findings indicated that HR and HRV were predictive of NA and PA. Specifically, HR (arousal) was associated with NA and PA, HRV was positively related to NA, and the interaction of HR and HRV predicted NA (i.e., high HR and high HRV predicted high NA).
In this phase 4 validation experiment, we adapted and extended the prior phase 3 validation design to evaluate the efficacy of the GSR sensing modality as part of the TIS sensor array. The effectiveness of the GSR sensor as a predictor of affective responses to stressed interactions was examined. In particular, the research was primarily focused on its ability to add to the prediction of affect beyond the HR and HRV metrics that were previously validated. The validation experiment was designed to create differential degrees of stress across conditions by using a strong CT (i.e., similar to phase 3 validation which yielded a 78% failure rate) versus a moderate CT (i.e., an easier CT with a target failure rate of approximately 50%). This allowed an evaluation of the sensitivity of the GSR sensor to different degrees of stress. In addition, interactions within conditions were differentially stressed (i.e., resource exchanges with CT versus without CT) which allowed an examination within individuals.
GSR Sensor Evaluation
Research Task and Experimental Design: The laboratory task was adapted from a task we had successfully used in past validation phases. It was an adaptation of the NASA Space Flight Resource Management task “Moon Base” to serve as the simulation for collaborative interaction among members. To provide an appropriate research platform, the simulation was redesigned to provide a task context that necessitated frequent structured interactions to facilitate the validation efforts.
The validation sessions consisted of 3-person teams in which members were assigned the role of Alpha, Bravo, or Charlie. We introduced a manipulation between team members to selectively stress interactions (moderate vs. difficult). We used a short cognitive test that was delivered prior to selected resource exchanges to induce stress. Approximately 73% of stressed interactions in the difficult test condition and 49% of stressed interactions in the moderate test condition failed the cognitive test. The final sample included 72 teams of three team members each (n = 216).
Findings and Conclusion: Heart rate had more consistent relationships with the key study variables (PA, NA, cognitive testing) than GSR. GSR only related to NA, and its relationship was redundant with information conveyed by HR alone. As previously discussed, HR is indicative of both sympathetic and parasympathetic nervous system activity, whereas GSR is solely an indicator of the sympathetic nervous system. It is interesting and perhaps consistent with this conceptualization that GSR only related to NA; however, given the literature, we had anticipated that GSR would aid in the prediction of NA for this reason. Although HR may simply be a more effective physiological measure, there are alternative reasons why this result may have occurred. The GSR sensors used may simply be less precise than the more established HR sensors that were utilized. It is also possible that GSR and HR may not be sufficiently “in phase.” That is, if galvanic skin responses occur more slowly than HR responses, the interaction-level analyses would not effectively capture the relevant information from the GSR metrics; recall the failure of GSR SD to relate to any of the key study variables. However, previous research has shown that GSR and HR responses tend to occur on the same timescale. It was also plausible that skewness within physiological data affected the analyses. To address that possibility, all the analyses were replicated using log-transformed data. The relationships between GSR / GSR SD and key study variables were unchanged.
On balance, we conclude that GSR using the sensors employed in this research does not offer incremental validity for the TIS badge. Although there is sufficient ambiguity to note that these findings are not definitive, the research described in this report does not support a GSR sensor as a strong prospect for improving the TIS technology.