Abstract

Robotics is increasing in popularity as a method of providing rich, personalized and cost-effective physiotherapy to individuals with some degree of upper limb paralysis, such as those who have suffered a stroke. These robotic rehabilitation systems are often high powered, and exoskeletal systems can attach to the person in a restrictive manner. Therefore, ensuring the mechanical safety of these devices before they come in contact with individuals is a priority. Additionally, rehabilitation systems may use novel sensor systems to measure current arm position. Used to capture and assess patient movements, these first need to be verified for accuracy by an external system. We present the ALAN-Arm, a humanoid robotic arm designed to be used for both accuracy benchmarking and safety testing of robotic rehabilitation systems. The system can be attached to a rehabilitation device and then replay generated or human movement trajectories, as well as autonomously play rehabilitation games or activities. Tests of the ALAN-Arm indicated it could recreate the path of a generated slow movement path with a maximum error of 14.2mm (mean = 5.8mm) and perform cyclic movements up to 0.6Hz with low gain (<1.5dB). Replaying human data trajectories showed the ability to largely preserve human movement characteristics with slightly higher path length and lower normalised jerk.

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