Patent ID: 11944427
Assignee: TOYOTA JIDOSHA KABUSHIKI KAISHA
Field: Medical technology (Instruments)
Classification: CPC A  B  G | IPC A  B  G

Claim 3:
4. A learning method comprising the steps of:
acquiring rehabilitation data from a walking training system comprising a plurality of actuators configured to assist a walking motion of a trainee, a treadmill on which the trainee walks, a plurality of sensors configured to detect data regarding the walking motion assisted by the actuator, and a control unit configured to control the plurality of actuators in accordance with a plurality of setting parameters;
generating learning data based on the rehabilitation data; and
performing machine learning using the learning data, wherein
the plurality of actuators includes a front pulling unit configured to apply pulling force to a leg of the trainee from front side and a rear pulling unit configured to apply pulling force to the leg of the trainee from a rear side for assisting a swing motion of the leg;
the plurality of sensors are provided to detect a plurality of motion amounts in the walking motion of the trainee,
the plurality of the setting parameters includes a speed of the treadmill, the pulling forces of the front pulling unit, a ratio between the pulling forces of the front and rear pulling units,
it is evaluated that, for each walking cycle of the walking motion, when at least one of the motion amounts matches one of predetermined abnormal walking criteria, the walking motion of the trainee is an abnormal walking pattern that meets the matched abnormal walking criterion,
the motion amounts include distances along a walking direction from a hip joint to a foot joint of the trainee at a time of swinging of the leg and at a time of landing of the leg;
each of the pieces of rehabilitation data in a walking cycle before and after a change in results of evaluation of the abnormal walking pattern is generated as learning data,
each of the pieces of rehabilitation data in the walking cycle before and after the change in the results of the evaluation is sequentially input as one data set, thereby performing machine learning by a supervised machine learning with using the setting parameter as a correct-answer label, and
a learning model that receives the abnormal walking pattern and outputs the setting parameter is constructed.