Abstract
Understanding what patients and therapists perceive may help to enhance social robots in rehabilitative therapies. In this paper we present the results of expert interviews about possible user model attributes for patients and therapists in stroke rehabilitation therapies. From these we derive implications for the use of social robots as therapist in these scenarios. As a method we asked therapists, about past patients and their impressions with other therapists in the post-stroke arm rehabilitation therapies of the E-BRAiN project. The interview technique was the repertory grid method, whereby we compare on a “between-subject” basis. The results show certain character traits and therapy-dependent characteristics, which can be used for the user modeling of both persons. We have found certain patient attributes to justify a system adaption like a “willingness for sabotage”.
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Acknowledgements
This joint research project “E-BRAiN—Evidence-based Robot Assistance in Neurorehabilitation” is supported by the European Social Fund (ESF), reference: ESF/14-BM-A55-0001/19-A01, and the Ministry of Education, Science and Culture of Mecklenburg-Vorpommern, Germany. The sponsors had no role in the decision to publish or any content of the publication.
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Bundea, A., Forbrig, P. (2022). Patient and Therapist Model Attributes for Social Robot Stroke Therapies Based on Implicit Knowledge from Expert Interviews. In: Zimmermann, A., Howlett, R.J., Jain, L.C. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 310. Springer, Singapore. https://doi.org/10.1007/978-981-19-3455-1_4
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DOI: https://doi.org/10.1007/978-981-19-3455-1_4
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