Determination of formulas for processing of measured points representing road surface deformations

The paper describes an initial approach adopted by the authors to create a system usable to monitor the road surface deformations in time and to model them in the 3D environment. To obtain measured data a 2D laser scanner is used to measure a distance of the real environment points. The main part of the paper deals with the derivation of formulas used by a measurement unit to calculate the point coordinates. The formulas result from and reflex a method of 3D measurement using an accelerometer and a gyroscope. The practical experiments have confirmed the ability of the measurement unit to scan the road deformation and the surrounding environment

Never waste a good crisis : Examining team coordination breakdowns during crisis situations

During crisis situations, teams are more prone to coordination breakdowns that are characterized by a temporary, diminished ability to function effectively as a team. However, team research currently lacks robust approaches for identifying transitions from effective team functioning to coordination breakdowns. With the current study, we aimed to develop such robust approaches, and to deepen our understanding of how team coordination dynamics across various signals reflect coordination breakdowns. Consequently, we used audiovisual data from four-person teams involved in a stressful collaborative game task to manually identify coordination breakdowns. Next, we set out to computationally identify coordination breakdowns by applying continuous measures of team coordination (windowed synchronization coefficient and multidimensional recurrence quantification analysis) to photoplethysmogram and electrodermal activity data obtained during the task, and identifying transitions therein with change point and nonlinear prediction algorithms. We find that our computational coordination breakdown identification approaches can identify up to 96% of the manually identified coordination breakdowns, although our results also show that the precision of our approaches falls far behind. Our findings contribute theoretically and methodologically to the systematic investigation of coordination breakdowns, which may ultimately facilitate the support of teams in responding to and mitigating negative consequences of crisis situations

A Methodological Framework to Study Change in Team Cognition Under the Dynamical Hypothesis

The dynamical hypothesis claims that cognitive systems, such as teams, are dynamical systems (i.e., an interdependent collection of individuals and their technology that change together over time). Following this hypothesis, team researchers have adopted dynamical approaches to better understand the team cognitive processes and states that form team cognition, as well as how they emerge over time. One approach focuses on team coordination dynamics, which examines the coupling of signals between interacting individuals in various modalities, and has been shown to reflect aspects of team functioning including team cognition. However, how changes in team coordination relate to high-level team cognitive processes and states, as well as important events, are not yet fully understood. To this end, we advance a methodological framework for researching team cognition under the dynamical hypothesis. Subsequently, we provided an empirical case-study application of this framework. Thereby, this work contributes methodologically and empirically to a deeper understanding of team cognition, the dynamical hypothesis, and the synergy between them.</p

Examining Underlying Features of Team Coordination Breakdowns

Team coordination dynamics describe the self-organized behavior of team members’ interactivity to achieve a larger, continuously changing common goal. The common goal changes based on dynamically shifting demands and environments (Demir et al., 2018; Gorman &amp; Amazeen, 2010; Kelso, 1994). A team coordination breakdown occurs when this self-organized behavior fails, resulting in a temporarily diminished ability to function effectively as a team affecting team performance (Bearman et al., 2010). Previous studies have shown that by examining team coordination dynamics, we can identify breakdowns in team coordination, which disrupt effective team functioning (Amazeen, 2018; Eijndhoven et al., 2022; Likens et al., 2014). By identifying team coordination breakdowns (TCBs), ineffective team functioning can be located in time. If TCBs can be identified computationally feedback on suboptimal team coordination can be delivered in real-time (Wiltshire et al., 2022). Especially during crisis situations, in which effective team functioning is challenged by rapidly changing demands and environments, such feedback can be of crucial importance. Eijndhoven et al. (2022) were able to computationally identify up to 96% of TCBs, albeit that the precision of the approaches was found to be lower (i.e., below 30%; . To improve this previous work, further research is needed to examine the underlying structure of team coordination dynamics data as it relates to TCBs. To this end, we aim to study what features (e.g., sample entropy, mean change, autocorrelation) are relevant for more precisely distinguishing between coordination dynamics that reflect TCBs and those that do not. In addition, we aim to examine the extent to which we can use these features to forecast TCBs, and to interpret the patterns in team coordination that precede or occur during TCBs. Besides the computational identification of TCBs in our previous work, we also conducted manual identification. We coded TCBs as time-localized instances of inefficient team functioning that affected team performance, based on audiovisual recordings of the experiment. For the current study, we further examine these instances, by looking beyond the team performance during these specific instances, to the effect of TCBs on the global team performance. More specifically, we aim to investigate the relationship between the total duration of TCBs and the amount of time it takes teams to achieve the main goal (with longer amounts of time indicating poorer performance). Such an investigation will help to better understand the effects of TCBs on team performance. Ultimately, our current study will contribute to deepening our understanding of how team coordination dynamics underly TCBs, and pave the way towards enabling the provision of feedback to support effective team functioning, and subsequently team performance

A Review of Using Wearable Technology to Assess Team Functioning and Performance

Wearable technology enables collecting continuous in situ data from multiple people in various modalities, which can enhance team research and support, as the dynamic coupling of signals between interacting individuals (i.e., team coordination dynamics) is believed to reflect underlying processes and states of team functioning and performance. We conducted a systematic review on existing literature to evaluate the prospective use of wearable technology in research and practice. Using the IMOI framework as an organizing tool, our review revealed considerable support linking team coordination dynamics in different modalities to team functioning and performance, but also explicated the field’s nascent status

A Review of Using Wearable Technology to Assess Team Functioning and Performance

Wearable technology enables collecting continuous in situ data from multiple people in various modalities, which can enhance team research and support, as the dynamic coupling of signals between interacting individuals (i.e., team coordination dynamics) is believed to reflect underlying processes and states of team functioning and performance. We conducted a systematic review on existing literature to evaluate the prospective use of wearable technology in research and practice. Using the IMOI framework as an organizing tool, our review revealed considerable support linking team coordination dynamics in different modalities to team functioning and performance, but also explicated the field's nascent status