Patient-specific walking pattern simulation in a gait trajectory guiding device

Repetitive training is of much importance for restoring full-fledged gait ability. At present, task-specific repetitive approach has been proved to be the most effective motor learning concept. In this regard, a gait trajectory guiding device with partial body weight support system can be a solution for gait rehabilitation. This paper presents a complete gait study with an objective to implement the motion of a natural walking pattern in the automated foot-boards of a gait trajectory guiding device. In our developed motion algorithm of foot-boards we have concentrated on adaptation of patient-specific true walking trajectory, determination of variable velocity pattern along different degrees of freedom and time-division for simulating different phases of a complete gait cycle. Gait database, collected from disparate sources and previous gait-studies have been used for kinetic and kinematic analysis of human walking. We have modeled those data based on the previous researches done in this area and adopt them for our motion algorithm. A precise velocity pattern and time-division have been described along different axes so that patient's biofeedback and postural stability in different walking phases can be recorded accordingly and motion-correction of the footboards can be done in consecutive cycles through iterative learning control algorithm with the help of motion sensors.