Patricia Kan

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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. The system uses a decision(More)
This paper presents a real-time system that guides stroke patients during upper extremity rehabilitation. The system automatically modifies exercise parameters to account for the specific needs and abilities of different individuals. We describe a partially observable Markov decision process (POMDP) model of a rehabilitation exercise that can capture this(More)
In this paper the approach of using a partially observable Markov model for games with dy-namical difficulty adjustment is introduced. This approach leads implicitly to a strategy which balances gathering information about the player through his or her behavior with adjusting the game to the estimated player's abilities and preferences. We will show how(More)
The sensor types used are encoders, photo-resistors, and interface querying. These will provide: • comprehensive, yet non-intrusive, monitoring of client • accurate hand movement mechanics • monitoring of proper upper-limb posture • physical and psychological fatigue level The control system is responsible for guiding the client through the exercise. It(More)
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