An analysis of speech as a modality for activity recognition during complex medical teamwork

Abstract

We analyzed the nature of verbal communication among team members in a dynamic medical setting of trauma resuscitation to inform the design of a speech-based automatic activity recognition system. Using speech transcripts from 20 resuscitations, we identified common keywords and speech patterns for different resuscitation activities. Based on these patterns, we developed narrative schemas (speech “workflow” models) for five most frequently performed activities and applied linguistic models to represent relationships between sentences. We evaluated the narrative schemas with 17 new cases, finding that all five schemas adequately represented speech during activities and could serve as a basis for speech-based activity recognition. We also identified similarities between narrative schemas of different activities. We conclude with design implications and challenges associated with speech-based activity recognition in complex medical processes.