A method for real-time
adaptation of
gait training parameters (10) applicable to a
gait training device (100), wherein the
gait training device (100) comprises a detection unit, a training unit and a
control unit, wherein the
control unit is electrically connected to the detection unit and the training unit and controls the operation of the training unit, the method for real-time
adaptation of
gait training parameters (10) comprising the steps of: (a) Collecting, by the acquisition unit, the
muscle relaxation gait data of at least one first user measured during
gait training in a
muscle relaxation state and the active
power output gait data of the at least one first user measured during
gait training in the active
power output state of the first user; and then, creating, by the
control unit, a standard motion model based on the ratio of the active
power output gait data of the first user to the
muscle relaxation gait data of the first user; (b) Obtaining a movement model of a second user comprising the
muscle relaxation gait data of the second user measured during gait training under the
muscle relaxation state of the second user, and then estimating a plurality of personalized training models of different difficulty levels by combining the
muscle relaxation gait data of the second user with the standard movement model with 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100% difficulty curves, so that personalized training models with different difficulty levels are created and the difficulty curves can be adjusted according to requirements by the control unit; and (c) determining whether the actual training status of the second user matches the standard of the one of the personalized training models, and then adjusting the one of the personalized training models and providing a replacement training model by the control unit, wherein the gait training comprises at least one
gait cycle and the
gait cycle is divided into a center of gravity shift interval (F1), a hip flexion interval (F2) and a
knee extension interval (F3), wherein, in step (b), the calculation method of the control unit for estimating the personalized training model from the muscle relaxation gait data of the second user comprises, when the muscle relaxation state of the second user is obtained, obtaining a maximum value of the force in a muscle relaxation state and a minimum value of the force in a muscle relaxation state in the
gait cycle, whereupon, when the second user is actively outputting force, estimating a predicted maximum value of the
active force output and a predicted minimum value of the
active force output in the gait cycle of the second user by the standard motion model, whereupon, when the second user is in the
active force output state, using the value obtained by the detection unit to obtain an actual maximum value of the active
force output and an actual minimum value of the active
force output in the gait cycle,When the second user actively exerts force, the actual maximum value of the active
force output, the predicted maximum value of the active force output, and the maximum value of the force in the muscle relaxation state are input into a calculation formula for the center of gravity shift interval; and the actual minimum value of the active force output, the predicted minimum value of the active force output, and the minimum value of the force in the muscle relaxation state are input into a calculation formula for the hip flexion interval to obtain the force output level of the center of gravity shift interval (F1) and the force output level of the hip flexion interval (F2), after which the lower force output level is used as these personalized training models of different difficulty levels for the second user.