Collective allostatic load measures for teams
Abstract
Teams in action, such as emergency responders and medical personnel, are challenged with environments that are characterized by time pressure, rapidly unfolding events, high information processing demand, and severe consequences of wrong decisions. Such environments in general have adverse effects on team performance. To mitigate this problem and increase the performance and resilience of teams, we developed the Collective Allostatic Load Measures system. Collective Allostatic Load Measures system collects, aggregates, and analyzes multimodal data, and provides recommendation and intervention mechanisms under acute and chronic stressors. The key innovation in Collective Allostatic Load Measures is the integration of multimodal sensing capabilities with accurate algorithms that can process sequential multimodal data from heterogeneous sensors. We built a prototype of Collective Allostatic Load Measures that incorporates the core functionalities that can assess allostatic load at the team level, namely collective allostatic load. Collective Allostatic Load Measures includes a set of commercial off-the-shelf sensors that record an individual’s physiological responses, a speech processing module that can extract the communication patterns of a team, a machine-learning based computational analysis module, a mobile phone app, and a web-based dashboard for visualization. Collective Allostatic Load Measures provides near real-time quantitative measurement of collective allostatic load that is leveraged to improve team performance and resilience by recommending interventions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 11, 2019
- Source ID
- 10.1177/1475921719884864
Entities
People
- Bob Pokorny
- Clint Bowers
- Henrik Molintas
- Kemal Davaslıoğlu
- Rebecca Grossman
- Sohraab Soltani
- Yalin E. Sagduyu
Organizations
- Defense Advanced Research Projects Agency
- Hofstra University
- University of Central Florida