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Bio-behavioral Team Dynamics Measurement System: Multimodal Sensing, Dynamical Systems Modeling, and Machine Learning

Garima Arya Yadav1, Bethany K Bracken2, Nancy J Cooke3

  • 1Arizona State University, Tempe AZ.

Nonlinear Dynamics, Psychology, and Life Sciences
|October 21, 2025
PubMed
Summary
This summary is machine-generated.

The Bio-behavioral Team Dynamics Measurement System (BioTDMS) offers automatic, objective team performance assessments during training. This system analyzes multimodal bio-behavioral data to predict team effectiveness with 90% accuracy.

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Area of Science:

  • Human-computer interaction
  • Team dynamics
  • Biometric analysis

Background:

  • Military training requires objective team performance assessment.
  • Current methods are often subjective and time-consuming.
  • DARPA's OP TEMPO program aims to enhance warfighter readiness through automated assessment.

Purpose of the Study:

  • To develop and validate the Bio-behavioral Team Dynamics Measurement System (BioTDMS).
  • To create an objective, automatic system for assessing team performance in simulation training.
  • To identify bio-behavioral signatures that predict team effectiveness.

Main Methods:

  • Utilized a multimodal sensor suite to collect neural, cardio-respiratory, eye tracking, and verbal data.
  • Employed a layered symbolic dynamics model and moving-window entropy/mutual information for real-time analysis.
  • Integrated a multitask, multi-kernel learning engine for performance prediction and explainability.

Main Results:

  • BioTDMS achieved 90% accuracy in predicting subjective team performance assessments.
  • Demonstrated real-time metrics for team adaptability and influence distribution.
  • Successfully implemented and field-tested during U.S. Marine Corps Fire Support Team (FiST) training.

Conclusions:

  • BioTDMS shows significant potential as an operational tool for objective team assessments.
  • The system can enhance training effectiveness by providing immediate, data-driven feedback.
  • Future work will explore generalizability in air combat teams and human-autonomy teaming.