Robotic Gait Trainer Reliability and Stroke Patient Case Study

With over 600,000 people each year surviving a stroke, it has become the leading cause of serious long-term disability in the United States [1, 2, 3]. Studies have proven that through repetitive task training, neural circuits can be re-mapped thus increasing the mobility of the patient [4, 5, 6, 7, 8]. This fuels the emerging field of rehabilitation robotics. As technology advances new therapy robots are developed that are increasingly compliant and captivating to use. This paper examines the robotic gait trainer (RGT) developed in the human machine integration laboratory at Arizona State University. The RGT is a tripod mechanism, where the patient's leg is the fixed link, controlled on a Mat-lab and Simulink platform. An eight week case study was conducted with a 22 year old female stroke survivor. Subjective feedback, robot performance and the patient's key performance indicators examined throughout the study are analyzed.

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