ArticleInternational journal of computer assisted radiology and surgery2026
Action recognition in medical environments for robotic assistance.
Article in International journal of computer assisted radiology and surgery, 2026. 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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5 authors.
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Abstract
purposeTeamwork is fundamental to medical practice and relies on seamless collaboration among professionals with different tasks. Integrating robotic systems into this environment demands smooth interactions. Human action recognition, which infers a person's state without explicit input, can support this. We focus on handovers between medical staff, using the actions as implicit cues for robotic assistance to replace the giving party in such scenarios.
methodsSkeletal information processed with differing machine learning algorithms makes it possible to derive actions out of sequential image data. Transferred to the medical context, we aim to infer actions defined for each situation in two datasets, a surgery in the operating room and a care intervention in the patient ward, depicting a handover between staff. We aim to abstract movement patterns across individuals through skeletal representation, leveraging the spatiotemporal information of medical handovers to enable future robotic systems to interact based on implicit cues.
resultsWe report an F1 score of
conclusionThe handover phases in two medical contexts, a minimally invasive surgery and a wound dressing on the patient station, are recognized with the proposed framework. This lays a first step for the integration of robotic assistance in the handover of medical material or instruments.
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