公开(公告)号：US20210060383A1

公开(公告)日：2021-03-04

申请号：US16553896

申请日：2019-08-28

Applicant: HIWIN TECHNOLOGIES CORP.

Inventor： Chao-Ju HUANG ,  Jia-Ming SHIU ,  Yi-Jing WU

IPC: A63B24/00 ,  A61B5/11 ,  A63B71/06 ,  A63B22/00 ,  A63B21/00

Abstract: A gait training machine includes a base, a driving module including a driving circuit, a motor and two moving platforms, a sensor module including a left pedal, a right pedal, a plurality of pressure sensors and a position sensor, a controller electrically connected to the driving circuit, each pressure sensor and the position sensor and a signal processing module disposed in the controller and including a gait detecting unit and a gait determining unit with a center-of-gravity calculation logic and a gait determining logic stored therein. Using the above gait training machine to determine whether the bearing weight of the left pedal and the right pedal is correct, and whether the center-of-gravity position is correct, so that the user can know the ankle joint movement and the center-of-gravity during gait training to adjust the health gait posture.

公开(公告)号：US20240024187A1

公开(公告)日：2024-01-25

申请号：US17872879

申请日：2022-07-25

Applicant: HIWIN TECHNOLOGIES CORP.

Inventor： Chi-Chia LIU ,  Tzu-Ning YEH ,  Jia-Ming SHIU

IPC: A61H1/02 ,  G16H20/30

CPC classification number: A61H1/0262 ,  G16H20/30 ,  A61H2201/5007 ,  A61H2230/605 ,  A61H2201/5061

Abstract: A method for real-time adjustment of gait training parameter includes steps: (a) collecting the muscle relaxation gait data of the first user measured during gait training in a muscle relaxation state and the active force output gait data of a first user measured during the gait training in the first user's active force output state, and establishing a standard motion model based on the ratio of the first user's active force output gait data to the first user's muscle relaxation gait data; (b) obtaining the muscle relaxation gait data of a second user in a state of muscle relaxation, and estimating the personalized training model by combining the muscle relaxation gait data of the second user with the standard motion model; (c) determining the second user meets the standard of the personalized training model, then adjusting the personalized training model, and providing an auxiliary training model.