Back to Search
Start Over
optimizing self-exercise scheduling in motor stroke using Challenge Point Framework theory
- Source :
- ICORR
- Publication Year :
- 2019
-
Abstract
- An important challenge for technology-assisted self-led rehabilitation is how to automate appropriate schedules of exercise that are responsive to patients’ needs, and optimal for learning. While random scheduling has been found to be superior for long-term learning relative to fixed scheduling (Contextual Interference), this method is limited by not adequately accounting for task difficulty, or skill acquisition during training. One method that combines contextual interference with adaptation of the challenge to the skill-level of the player is Challenge Point Framework (CPF) theory. In this pilot study we test whether self-led motor training based upon CPF scheduling achieves faster learning than deterministic, fixed scheduling. Training was implemented in a mobile gaming device adapted for arm disability, allowing for grip and wrist exercises. We tested 11 healthy volunteers and 12 hemiplegic stroke patients in a single-blinded no crossover controlled randomized trial. Results suggest that patients training with CPF-based adaption performed better than those training with fixed conditions. This was not seen for healthy volunteers whose performance was close to ceiling. Further data collection is required to determine the significance of the results.
- Subjects :
- Adult
Male
030506 rehabilitation
medicine.medical_specialty
Computer science
medicine.medical_treatment
education
Crossover
Pilot Projects
Scheduling (computing)
Dreyfus model of skill acquisition
03 medical and health sciences
0302 clinical medicine
Physical medicine and rehabilitation
medicine
Humans
Aged
Aged, 80 and over
Rehabilitation
Data collection
Challenge point framework
Stroke Rehabilitation
Middle Aged
Wrist
Exercise Therapy
Stroke
Task analysis
Female
0305 other medical science
Motor learning
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 19457901
- Volume :
- 2019
- Database :
- OpenAIRE
- Journal :
- IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
- Accession number :
- edsair.doi.dedup.....b492d589940625cb435482c8a094c30a