The role of human experience when making sense of brain monitoring: an interdisciplinary case study to assess wearable, non-invasive, brain-monitoring devices for rehabilitation-Reference-Cited by-同舟云学术

The role of human experience when making sense of brain monitoring: an interdisciplinary case study to assess wearable, non-invasive, brain-monitoring devices for rehabilitation

Published:2023-01-02 Issue:1 Volume:10 Page:
ISSN:2329-9460
Container-title:Journal of Responsible Innovation
language:en
Short-container-title:Journal of Responsible Innovation

Author:

Risnes Martha¹²^ORCID,Korostynska Olga²^ORCID,Mirtaheri Peyman²^ORCID,Berg Arild³^ORCID

Affiliation:

1. Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway

2. Department of Mechanical, Electronic and Chemical Engineering, Oslo Metropolitan University, Oslo, Norway

3. Department of Product Design, Oslo Metropolitan University, Oslo, Norway

Funder

Research Council of Norway

Publisher

Informa UK Limited

Subject

Management of Technology and Innovation,Information Systems and Management,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/23299460.2023.2175476

Reference72 articles.

1. Aagaard Jesper and Don Ihde. 2018. Postphenomenological Methodologies: New Ways in Mediating Techno-Human Relationships . https://rowman.com/ISBN/9781498545235/Postphenomenological-Methodologies-New-Ways-in-Mediating-Techno-Human-Relationships.

2. A comprehensive review of EEG-based brain–computer interface paradigms

3. Adams, Catherine, and Joni Turville. 2018. “Doing Postphenomenology in Education.” In Postphenomenological Methodologies: New Ways in Mediating Techno-Human Relationships, edited by Jesper Aagaard, Jan Kyrre Berg Friis, Jessica Sorenson, Oliver Tafdrup, and Cathrine Hasse, 296. Rowman & Littlefield. https://books.google.com.au/books/about/Postphenomenological_Methodologies.html?id=a99jDwAAQBAJ&source=kp_book_description&redir_esc=y.

4. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

5. Experience-based design: from redesigning the system around the patient to co-designing services with the patient

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