ArticleProceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting2024
Exploring Collaborative Patterns in Neurodiverse Teams: A Hidden Markov Model Approach Using Physiological Signals.
Article in Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Autistic 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 nonautistic (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.
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