Clinical assessments performed during robotic rehabilitation by the gait training robot Lokomat

• Published in: 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005 pp. 345 - 348
• Main Authors: Lunenburger, L., Colombo, G., Riener, R., Dietz, V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Actuators; Brain injuries; Force control; Force measurement; Force sensors; Muscles; Pathology; Rehabilitation robotics; Robot sensing systems; Spinal cord injury
• ISBN: 9780780390034; 0780390032
• ISSN: 1945-7898; 1945-7901
• DOI: 10.1109/ICORR.2005.1501116

Neurological disorders, such as spinal cord injury, stroke, and traumatic brain injury, affect the motor performance of affected individuals. The most important result is the loss of function, e.g. gait function. A reduction of normal features and an increase in pathological features lead to this loss. Muscle weakness and increased involuntary muscle tone (spasticity) are most commonly affected. Robotic rehabilitation devices are available for re-training impaired functions. For example, the Lokomat supports patients during body-weight supported treadmill training. The robotic devices are equipped with sensors (e.g. position and force) and actuators needed for their control. Beyond pure training, advanced tools can use these sensors and actuators to measure physiological and other properties of the patient using the device. We report here the design, implementation, and first tests of tools that allow (i) measurement of spasticity and (ii) measurement of voluntary muscle force with the Lokomat. The spasticity tool measures mechanical stiffness during controlled passive movements of the legs. The voluntary force tool measures maximum isometric torque in the hip and knee joint. Mechanical stiffness is higher in patients with higher spasticity. The voluntary force tool can be used in patients with incomplete spinal cord injury. We conclude that the use of robotic devices for assessment of patients during their training would be an efficient and important addition to robotic-supported therapy in the future.