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Armbeta
- Source :
- OZCHI
- Publication Year :
- 2017
- Publisher :
- ACM, 2017.
-
Abstract
- The aim of this research is to create a simple wearable technology for people engaged in upper limb rehabilitation to track how much they move their arm as well as the activities that the arm is engaged in. This paper describes the design of the `ArmBeta' prototype, which is based on the Microsoft Band 2 device and a mobile app. A lab-based trial study of ArmBeta with four healthy adults showed the accuracy for recognising reach-and-retrieve tasks was 78%, but the accuracy for other tasks (opening doors, eating, stirring a pot) was below 50%. A consecutive two-hour trial in daily life showed that the information generated was easy to understand but that the accuracy and accessibility need to be improved. We discuss the trade-offs between accessibility, accuracy, and the significance of information generated to track arm movement. The paper closes with considerations for future work to refine the system and to engage with patients and clinicians involved in rehabilitation.
- Subjects :
- Computer science
medicine.medical_treatment
02 engineering and technology
Activity recognition
arm-tracking
080602 Computer-Human Interaction
Human–computer interaction
0202 electrical engineering, electronic engineering, information engineering
medicine
Doors
0501 psychology and cognitive sciences
activity recognition
upper limb rehabilitation
050107 human factors
Wearable technology
self-tracking
Rehabilitation
business.industry
Movement (music)
05 social sciences
Mobile apps
020207 software engineering
personal informatics
medicine.anatomical_structure
Upper limb
Upper limb rehabilitation
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 29th Australian Conference on Computer-Human Interaction
- Accession number :
- edsair.doi.dedup.....ff1fd3f219cb889debf7effb628cfca7
- Full Text :
- https://doi.org/10.1145/3152771.3156136