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Title: GENTLE/A : adaptive robotic assistance for upper-limb rehabilitation
Author: Gudipati, Radhika
ISNI:       0000 0004 5347 8825
Awarding Body: University of Hertfordshire
Current Institution: University of Hertfordshire
Date of Award: 2014
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Advanced devices that can assist the therapists to offer rehabilitation are in high demand with the growing rehabilitation needs. The primary requirement from such rehabilitative devices is to reduce the therapist monitoring time. If the training device can autonomously adapt to the performance of the user, it can make the rehabilitation partly self-manageable. Therefore the main goal of our research is to investigate how to make a rehabilitation system more adaptable. The strategy we followed to augment the adaptability of the GENTLE/A robotic system was to (i) identify the parameters that inform about the contribution of the user/robot during a human-robot interaction session and (ii) use these parameters as performance indicators to adapt the system. Three main studies were conducted with healthy participants during the course of this PhD. The first study identified that the difference between the position coordinates recorded by the robot and the reference trajectory position coordinates indicated the leading/lagging status of the user with respect to the robot. Using the leadlag model we proposed two strategies to enhance the adaptability of the system. The first adaptability strategy tuned the performance time to suit the user’s requirements (second study). The second adaptability strategy tuned the task difficulty level based on the user’s leading or lagging status (third study). In summary the research undertaken during this PhD successfully enhanced the adaptability of the GENTLE/A system. The adaptability strategies evaluated were designed to suit various stages of recovery. Apart from potential use for remote assessment of patients, the work presented in this thesis is applicable in many areas of human-robot interaction research where a robot and human are involved in physical interaction.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Upper-limb rehabilitation ; Stroke ; Human Robot Interaction ; Adaptable system ; Lead-lag model ; Haptics for rehabilitation ; Robot-assisted training ; Assistive technologies ; Biomechanics of the upper-limb ; Embedded-Virtual environments