doi:10.1109/CVPRW53098.2021.00312engSabater A.Santos L.Santos-Victor J.Bernardino A.Montesano L.Murillo A.C.One-shot action recognition in challenging therapy scenariosART-2021-127884One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy. © 2021 IEEE.2021http://zaguan.unizar.es/record/11826510.1109/CVPRW53098.2021.00312http://zaguan.unizar.es/record/118265oai:zaguan.unizar.es:118265IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2021), 2771-2779All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess