Abstract
Repetitive, individual exercises can improve the functional ability of stroke survivors over the long term. With the aim of providing extra motivation to adhere to repetitive, individual rehabilitation, this paper presents a robotic coach for stroke rehabilitation. Our system uses the Pepper robot and performs one of twelve data-driven coaching policies. The policies were learned from human-human observations of professional stroke physiotherapists and provide high-level personalisation based on user information and training context. A within subjects evaluation of the system was conducted in-person involving short interactions with 3 stroke survivors. The system was able to engage the target end users and there were indications that decreased workload could be possible when using the system compared to exercising alone.
Original language | English |
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Title of host publication | HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction |
Publisher | Association for Computing Machinery |
Pages | 911-915 |
Number of pages | 5 |
ISBN (Electronic) | 9798400703232 |
DOIs | |
Publication status | Published - 11 Mar 2024 |
Event | 19th Annual ACM/IEEE International Conference on Human-Robot Interaction 2024 - Boulder, United States Duration: 11 Mar 2024 → 15 Mar 2024 |
Conference
Conference | 19th Annual ACM/IEEE International Conference on Human-Robot Interaction 2024 |
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Abbreviated title | HRI 2024 |
Country/Territory | United States |
City | Boulder |
Period | 11/03/24 → 15/03/24 |
Keywords
- Coaching
- Personalisation
- Rehabilitation
- Stroke
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Electrical and Electronic Engineering