A decision-theoretic approach in the design of an adaptive upper-limb stroke rehabilitation robot.

This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a partially observable Markov decision process (POMDP) as its primary engine for decision-making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises.

The development of an adaptive upper-limb stroke rehabilitation robotic system.

Background: Stroke is the primary cause of adult disability. To support this large population in recovery, robotic technologies are being developed to assist in the delivery of rehabilitation. This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task.