Abstract

Objective: To retain the locomotive ability after neur ological or orthopaedic impairments, consistent and fun ctional training should be emphasized to recover the m otor function of the extremities. The aim of this study is to develop a novel mechatronic approach in which kin ematics and kinetics of human locomotion have been i ncorporated into an o ptimum motion algorithm for robot enhanced gait rehabilitation. Methods: Our mechatronic system has two persona lized, gait trajectory guided and programmable foot boards equipped with force sensors. First, human loc omotion was analysed to generate personal ized gait p arameters, using various gait databases. Then, dynamic motions were modelled to be programmed in the system. Finally, we implemented the dynamic motion models to a gait trajectory guiding device for simulating precise, subject-oriented and smooth motion. Results: The mechatronic system simulated varied stride length, step time and its distribution among double and single support phase, according to the patient's height and average walking velocity. As the walking speed i ncreases, the duration of double support phase approached zero. Horizontal velocity, the values of z1F (t), z2F (t) and their corresponding velocities presented different phases of the walking cycle on the backward and fo rward plates of the device. Conclusions: By changing the valu es of patient 's height and velocity, we can simulate person-specific, ideal tr ajectory for the foot-boards. In conclusion, our developed system is an automated tool that can provide patients a natural walking practice and also guide them to follow an ideal pressure distribution and postural control through visual biofeedback.