Presentation
Exploring Collaborative Patterns in Neurodiverse Teams: A Hidden Markov Model Approach Using Physiological Signals
SessionCEDM9: Distributed Work II
DescriptionAutistic individuals face challenges in successful employment, emphasizing the need for targeted workplace support. This study explored collaborative dynamics within neurodiverse teams during a simulated remote work task by applying Hidden Markov Models (HMMs) to heart rate data. Eighteen participants formed nine dyads: six non-autistic (NA-NA) pairs and three autistic-non-autistic (ASD-NA) pairs. Dyads completed two trials of a collaborative programming task over Zoom, alternating roles between trials. Heart rate data were collected, segmented, and transformed to extract features reflecting participants' interactions. The final HMM was fitted with seven hidden states, and transition probabilities were derived for each dyad type. Results showed that NA-NA dyads exhibited more frequent transitions among states compared to ASD-NA dyads, potentially suggesting more varied interaction patterns. These findings demonstrate the utility of HMMs in capturing collaborative behaviors through physiological signals and highlight their potential in helping develop effective support strategies for neurodiverse teams.
Contributors
Research Assistant Professor
Event Type
Lecture
TimeThursday, September 12th1:30pm - 1:50pm MST
LocationFLW Salon C
Cognitive Engineering & Decision Making
DEI