CROSS REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority from Japanese patent application No. 2019-120029, filed on Jun. 27, 2019, the disclosure of which is incorporated herein in its entirety by reference.

BACKGROUND

The present disclosure relates to a learning system, a rehabilitation support system, a method, a program, and a trained model.

Trainees such as patients may use a rehabilitation support system such as a walking training apparatus when they perform rehabilitation. As an example of the walking training apparatus, Japanese Patent No. 6052234 discloses a walking training apparatus including a walking assistance apparatus that is attached to a leg of a trainee and assists the trainee in walking.

In some rehabilitation support systems, when a trainee performs rehabilitation, a training staff member such as a doctor or a physical therapist may attend the rehabilitation as an assistant for the trainee, give encouraging talks to the trainee, give a helping hand to the trainee, and/or perform a setting operation for the rehabilitation support system.

SUMMARY

Incidentally, in order to obtain good training results, the setting operation of the rehabilitation support system performed by the training staff member needs to be performed so that the rehabilitation support system can appropriately assist the trainee. Further, the timing of the setting operation, i.e., the timing at which assistance is added or ceased, or at which the degree of assistance is changed also affects the training results. Therefore, in order to perform such a setting operation, the training staff member needs to make a choice as to what kind of assistance should be given to the trainee, and determine an appropriate degree of the assistance and its timing. Further, the training staff member needs to determine what kind of encouraging talks he/she should give to the trainee and when he/she should give such talks to the trainee, and determine the timing at which he/she should give a helping hand to the trainee.

However, in the present circumstances, the training staff member makes the above-described determinations by intuition and/or knack. Further, since the years of experience and the level of proficiency vary among the training staff members, the difference in training results among the training staff members becomes considerably large. Therefore, it is desirable to provide appropriate assistance so that good training results are obtained irrespective of the level of the training staff member. For this purpose, there is a need for a technique capable of, in a rehabilitation support system, making suggestions so that the above-described determinations can be made as if they were assisted by a competent training staff member (e.g., a training staff member who is highly appreciated for his/her training results) irrespective of the actual training staff member. Further, the assistance to the trainee is not limited to those given by the training staff member. That is, it is conceivable that the assistance may be given by other kinds of training assistants such as artificial assistants. Therefore, there is a need for a technique capable of making suggestions so that the above-described determinations can be made as if they were assisted by a competent training assistant irrespective of the actual training assistant.

The present disclosure has been made in order to solve the above-described problem and provides a learning system and the like that generate a learning model capable of suggesting a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using a rehabilitation support system. Further, the present disclosure also provides a learning system and the like that generate a learning model capable of suggesting a desirable action to a training assistant who assists a trainee when the trainee is doing training by using a training support system.

A first exemplary aspect is a learning system including: an acquisition unit configured to acquire a classification result obtained by classifying training assistants by performing a cluster analysis for first rehabilitation data about rehabilitation performed by a trainee by using a rehabilitation support system, the first rehabilitation data including at least assistant data indicating a training assistant who assists the trainee, action data indicating an assisting action performed by the training assistant to assist the trainee, and index data indicating a degree of recovery of the trainee; and a learning unit configured to generate a learning model, the learning model being configured to input second rehabilitation data including at least the action data and output the action data for suggesting a next action to be performed by the training assistant, in which the learning unit generates the learning model by using, as teacher data, the second rehabilitation data for which pre-processing has been performed based on the classification result. In this way, it is possible to generate a learning model capable of suggesting a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using the rehabilitation support system.

The second rehabilitation data may include at least one of the index data and the assistant data. In this way, the index data or the assistant data can be taken into consideration in the trained model.

The learning unit may generate the learning model by using, as the teacher data, the second rehabilitation data corresponding to the training assistant included in one group in the classification result. In this way, the trained model can be generated while taking the action of the training assistant belonging to one group into consideration.

Alternatively, the learning unit may generate the learning model by using the second rehabilitation data as the teacher data, the second rehabilitation data being data in which a plurality of groups that are labeled based on the classification result are associated with the assistant data each of which corresponds to a respective one of the plurality of groups. In this way, the trained model in which the action of the training assistant is taken into consideration on a group-by-group basis can be generated.

The learning system may further include an analysis unit configured to classify the training assistants by performing the cluster analysis for the first rehabilitation data, and the acquisition unit acquires a classification result obtained by classifying the training assistants from the analysis unit. In this way, the learning system can perform processing from the analysis stage.

The first and second rehabilitation data may include trainee data indicating a feature of the trainee. In this way, the feature of the trainee can be taken into consideration in the trained model.

The trainee data may include symptom data indicating at least one of a disease and a symptom of the trainee. In this way, the symptom data can be taken into consideration in the trained model.

The action data may include at least one of data indicating an operation by which a setting value in the rehabilitation support system has been changed and data indicating an assisting action for the trainee. In this way, the setting value changing operation or the state of the assisting action can be taken into consideration in the trained model.

The data indicating the operation may include data indicating a level of skill required for performing the operation. As a result, the level of skill required for the operation can be taken into consideration in the trained model.

The learning unit may generate the learning model for each of a plurality of groups in the classification result by using, as the teacher data, the second rehabilitation data corresponding to the training assistant included in that group. In this way, a plurality of types of trained models can be generated.

The learning system may include a group designation unit configured to designate one group in the classification result, and the learning unit may generate the learning model by using, as the teacher data, the second rehabilitation data corresponding to the training assistant included in the group designated by the group designation unit. In this way, a trained model only for the designated group can be generated.

A second exemplary aspect is a learning system including: an acquisition unit configured to acquire a classification result obtained by classifying training assistants by performing a cluster analysis for first data about training performed by a trainee by using a training support system, the first data including at least assistant data indicating a training assistant who assists the trainee, action data indicating an assisting action performed by the training assistant to assist the trainee, and index data indicating a degree of an improvement in a physical function of the trainee; and a learning unit configured to generate a learning model, the learning model being configured to input second data including at least the action data and output the action data for suggesting a next action to be performed by the training assistant, in which the learning unit generates the learning model by using, as teacher data, the second data for which pre-processing has been performed based on the classification result. In this way, it is possible to generate a learning model capable of suggesting a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using the training support system.

A third exemplary aspect is a rehabilitation support system capable of accessing a trained model, the trained model being a learning model trained by the learning system according to the first aspect, the rehabilitation support system including: an output unit configured to output the second rehabilitation data about rehabilitation performed by a trainee by using the rehabilitation support system as an input to the trained model, and a notification unit configured to notify the training assistant assisting the trainee in the rehabilitation of the action data output from the trained model. In this way, it is possible to suggest a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using the rehabilitation support system.

The rehabilitation support system may include a designation unit configured to designate the training assistant who assists the trainee in the rehabilitation, and can access a classification result storage unit configured to store the classification result. Further, when the training assistant designated by the designation unit is a training assistant whose teacher data has not been adopted when the trained model is generated, the output unit outputs the second rehabilitation data and the notification unit provides a notification. In this way, it is possible to prevent unnecessary notifications from being provided to training assistants who are assumed to require no notification.

A fourth exemplary aspect is a learning method including: an acquisition step of acquiring a classification result obtained by classifying training assistants by performing a cluster analysis for first rehabilitation data about rehabilitation performed by a trainee by using a rehabilitation support system, the first rehabilitation data including at least assistant data indicating a training assistant who assists the trainee, action data indicating an assisting action performed by the training assistant to assist the trainee, and index data indicating a degree of recovery of the trainee; and a learning step of generating a learning model, the learning model being configured to input second rehabilitation data including at least the action data and output the action data for suggesting a next action to be performed by the training assistant, in which in the learning step, the learning model is generated by using, as teacher data, the second rehabilitation data for which pre-processing has been performed based on the classification result. In this way, it is possible to generate a learning model capable of suggesting a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using the rehabilitation support system.

A fifth exemplary aspect is a method for supporting rehabilitation (a method for operating a rehabilitation support system) in a rehabilitation support system capable of accessing a trained model, the trained model being a learning model trained by the learning method according to the fourth aspect, the method including: an outputting step of outputting, by the rehabilitation support system, the second rehabilitation data about rehabilitation performed by a trainee by using the rehabilitation support system as an input to the trained model, and a notification step of notifying, by the rehabilitation support system, the training assistant assisting the trainee in the rehabilitation of the action data output from the trained model. In this way, it is possible to suggest a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using the rehabilitation support system.

A sixth exemplary aspect is a program for causing a computer to perform: an acquisition step of acquiring a classification result obtained by classifying training assistants by performing a cluster analysis for first rehabilitation data about rehabilitation performed by a trainee by using a rehabilitation support system, the first rehabilitation data including at least assistant data indicating a training assistant who assists the trainee, action data indicating an assisting action performed by the training assistant to assist the trainee, and index data indicating a degree of recovery of the trainee; and a learning step of generating a learning model, the learning model being configured to input second rehabilitation data including at least the action data and output the action data for suggesting a next action to be performed by the training assistant, in which in the learning step, the learning model is generated by using, as teacher data, the second rehabilitation data for which pre-processing has been performed based on the classification result. In this way, it is possible to generate a learning model capable of suggesting a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using the rehabilitation support system.

A seventh exemplary aspect is a rehabilitation support program for a computer of a rehabilitation support system, the rehabilitation support system being capable of accessing a trained model, the trained model being a learning model trained by the program according to the sixth aspect, the rehabilitation support program being configured to cause the computer to perform: an outputting step of outputting the second rehabilitation data about rehabilitation performed by a trainee by using the rehabilitation support system as an input to the trained model, and a notification step of notifying the training assistant assisting the trainee in the rehabilitation of the action data output from the trained model. In this way, it is possible to suggest a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using the rehabilitation support system.

An eighth exemplary aspect is a trained model that is any one of a learning model trained by the learning system according to the first (or second) aspect, a learning model trained by the learning method according to the fourth aspect, and a learning model trained by the program according to the sixth aspect. In this way, it is possible to provide a trained model capable of suggesting a desirable action to a training assistant assisting who assists a trainee when the trainee performs rehabilitation (or training) by using a rehabilitation support system (or a training support system).

According to the present disclosure, it is possible to provide a learning system that generates a learning model capable of suggesting a desirable action to a training assistant who assists a trainee when the trainee performs rehabilitation by using a rehabilitation support system. Further, according to the present disclosure, it is possible to provide a rehabilitation support system using a generated trained model, a method and a program for training a trained model, a trained model, and a method and a program for rehabilitation support using a trained model. Further, the present disclosure can also be applied to training other than rehabilitation, and thereby provides similar advantageous effects in the training other than rehabilitation.

The above and other objects, features and advantages of the present disclosure will become more fully understood from the detailed description given hereinbelow and the accompanying drawings which are given by way of illustration only, and thus are not to be considered as limiting the present disclosure.

