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Integrating robotic systems into this environment demands smooth interactions. Human action recognition, which infers a person\u2019s 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Skeletal 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      We report an\n                      <jats:italic>F<\/jats:italic>\n                      1 score of\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$0.736 \\pm 0.045$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mn>0.736<\/mml:mn>\n                              <mml:mo>\u00b1<\/mml:mo>\n                              <mml:mn>0.045<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      for the OR dataset with ST-GCN and an\n                      <jats:italic>F<\/jats:italic>\n                      1 score of\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$0.941 \\pm 0.009$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mn>0.941<\/mml:mn>\n                              <mml:mo>\u00b1<\/mml:mo>\n                              <mml:mn>0.009<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      for the Ward dataset with the SkateFormer human action recognition. The defined actions showed distinction in the confusion matrix with limitations on actions with a rapid transition like approach and reach as well as the handover actions in the OR.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-025-03551-6","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T10:04:48Z","timestamp":1763978688000},"page":"241-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Action recognition in medical environments for robotic assistance"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7023-5162","authenticated-orcid":false,"given":"Sonja","family":"Stabenow","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3021-4152","authenticated-orcid":false,"given":"Lars","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4840-076X","authenticated-orcid":false,"given":"Alois","family":"Knoll","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3906-6093","authenticated-orcid":false,"given":"Klaus","family":"Bengler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2972-9802","authenticated-orcid":false,"given":"Dirk","family":"Wilhelm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"issue":"9","key":"3551_CR1","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1111\/jan.13711","volume":"74","author":"J-Y Lee","year":"2018","unstructured":"Lee J-Y, Song YA, Jung JY, Kim HJ, Kim BR, Do H-K, Lim J-Y (2018) Nurses\u2019 needs for care robots in integrated nursing care services. 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Approval was granted by the Ethics Committee of the University Hospital rechts der Isar (No. 337\/21\u00a0S). This study was performed in line with the principles of the Declaration of Helsinki. The authors also affirm that they have received written informed consent from all individuals for the publication of the images contained in this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and informed consent"}}]}}