Mahmoudi, Asghar ; Khosrotabar, Morteza ; Kuhmann, Jannis ; Grimmer, Martin ; Rinderknecht, Stephan ; Sharbafi, Maziar A. (2024)
Feasibility of utilizing passive BCI for assistance evaluation: a case study on a knee exoskeleton.
2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). Heidelberg, Germany (01.09.2024-04.09.2024)
doi: 10.1109/BioRob60516.2024.10719932
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
This study explored the feasibility of incorpo-rating a passive Brain-Computer Interface (BCI) into the individualization process of wearable assistive devices, with a specific focus on lower-limb exoskeletons. These devices can greatly improve mobility and quality of life for those with lower limb impairments. However, their optimal performance relies on personalized adjustments. This study offered a unique approach to user feedback without intentional control. A one-degree-of-freedom knee exoskeleton was utilized, introducing different levels of impedance to create distinct assistance levels. The analysis identified specific brain clusters, notably the right premotor and supplementary motor cortex, exhibiting significant differences in activity between the easiest and the most challenging conditions. This critical proof-of-concept step demonstrated that through decoding the user's brain activity, passive BCI could support the individualization of assistive technology. This approach indicated the potential to enhance the performance and user experience of wearable assistive devices.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Mahmoudi, Asghar ; Khosrotabar, Morteza ; Kuhmann, Jannis ; Grimmer, Martin ; Rinderknecht, Stephan ; Sharbafi, Maziar A. |
| Art des Eintrags: | Bibliographie |
| Titel: | Feasibility of utilizing passive BCI for assistance evaluation: a case study on a knee exoskeleton |
| Sprache: | Englisch |
| Publikationsjahr: | 2 September 2024 |
| Ort: | Piscataway, NJ |
| Verlag: | IEEE |
| Buchtitel: | 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) |
| Veranstaltungstitel: | 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) |
| Veranstaltungsort: | Heidelberg, Germany |
| Veranstaltungsdatum: | 01.09.2024-04.09.2024 |
| DOI: | 10.1109/BioRob60516.2024.10719932 |
| Kurzbeschreibung (Abstract): | This study explored the feasibility of incorpo-rating a passive Brain-Computer Interface (BCI) into the individualization process of wearable assistive devices, with a specific focus on lower-limb exoskeletons. These devices can greatly improve mobility and quality of life for those with lower limb impairments. However, their optimal performance relies on personalized adjustments. This study offered a unique approach to user feedback without intentional control. A one-degree-of-freedom knee exoskeleton was utilized, introducing different levels of impedance to create distinct assistance levels. The analysis identified specific brain clusters, notably the right premotor and supplementary motor cortex, exhibiting significant differences in activity between the easiest and the most challenging conditions. This critical proof-of-concept step demonstrated that through decoding the user's brain activity, passive BCI could support the individualization of assistive technology. This approach indicated the potential to enhance the performance and user experience of wearable assistive devices. |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Mechatronische Systeme im Maschinenbau (IMS) 16 Fachbereich Maschinenbau > Institut für Mechatronische Systeme im Maschinenbau (IMS) > Mensch-Mechatronik Synergie DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 2761 LokoAssist – Nahtlose Integration von Assistenzsystemen für die natürliche Lokomotion des Menschen 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Sportwissenschaft |
| Hinterlegungsdatum: | 22 Jan 2025 06:30 |
| Letzte Änderung: | 22 Jan 2025 08:03 |
| PPN: | 525464042 |
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