Robotics, Motor Learning, and Neurologic Recovery

• Published in: Annual review of biomedical engineering Vol. 6; no. 1; pp. 497 - 525
• Main Authors: Reinkensmeyer, David J., Emken, Jeremy L., Cramer, Steven C.
• Format: Journal Article
• Language: English
• Published: United States 01.01.2004
• Subjects: Algorithms; Animals; Artificial Intelligence; Biomechanical Phenomena - methods; Humans; Man-Machine Systems; Models, Theoretical; Motor Activity; Neurons - pathology; Robotics
• ISSN: 1523-9829; 1545-4274
• DOI: 10.1146/annurev.bioeng.6.040803.140223

▪ Abstract  Robotic devices are helping shed light on human motor control in health and injury. By using robots to apply novel force fields to the arm, investigators are gaining insight into how the nervous system models its external dynamic environment. The nervous system builds internal models gradually by experience and uses them in combination with impedance and feedback control strategies. Internal models are robust to environmental and neural noise, generalized across space, implemented in multiple brain regions, and developed in childhood. Robots are also being used to assist in repetitive movement practice following neurologic injury, providing insight into movement recovery. Robots can haptically assess sensorimotor performance, administer training, quantify amount of training, and improve motor recovery. In addition to providing insight into motor control, robotic paradigms may eventually enhance motor learning and rehabilitation beyond the levels possible with conventional training techniques.