Abstract: The learning system includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

Abstract: A barrier cap including a cap main body configured to be fitted onto a mouth of a container main body and an upper lid configured to be fitted onto the cap main body, the barrier cap being configured to be openable, wherein the cap main body includes a base layer and a barrier layer provided as an intermediate layer in the base layer, the base layer including a base resin, and the barrier layer containing a barrier resin, and the barrier layer includes a blend of the base resin and the barrier resin.

Abstract: When a non-aqueous electrolyte secondary battery in which a positive electrode active material comprising a layered lithium-composite oxide is used for a positive electrode is subjected to charge/discharge under a prescribed condition, in a graph showing the relationship between voltage “V” with discharge during 5th cycle and value dQ/dV from differentiation of battery capacity “Q” with discharge during 5th cycle by voltage “V”, peak intensity ratio “r” represented by the equation: r=|Ic|/(|Ia|+|Ib|+|Ic|) satisfies 0<r?0.25, in which |Ia| is absolute value dQ/dV for a peak top within a range of more than 3.9V to 4.4V or less, |Ib| is absolute value dQ/dV for a peak top within a range of more than 3.5V to 3.9V or less, and |Ic| is absolute value dQ/dV for a peak top within a range of 2.0V or more to 3.5V or less.

Abstract: An autonomous delivery box includes a first memory, a first processor coupled to the first memory, and a delivery box configured to house a package. The autonomous delivery box is configured to travel autonomously between a dispatch base from which the package is dispatched and a handover location at which the package is handed over. The first processor is configured to authenticate opening permission for a door of the delivery box, and to control whether or not the autonomous delivery box is permitted to travel such that the autonomous delivery box is not permitted to travel in a case in which a travel-permitting condition of the autonomous delivery box has not been satisfied at the handover location.

Abstract: A learning unit of a learning system generates the following learning model. That is, this learning model is a model that inputs rehabilitation data for each predetermined period and predicts a change in index data, the rehabilitation data being data about rehabilitation performed by a trainee using a rehabilitation support system, the index data indicating at least one of a symptom, a physical ability, and a degree of recovery of the trainee. This rehabilitation data includes at least the index data and training data of the trainee, the training data being acquired during the rehabilitation in the rehabilitation support system. Further, the learning unit generates the learning model by using, as teacher data, data obtained in a period until the index data reaches a predetermined target level.

Abstract: The learning system includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

Abstract: A cargo conveying device includes: a horizontal direction conveying section that conveys cargo in a horizontal direction, and that includes a transfer section at which cargo is transferred between the transfer section and a cargo handler; a height adjusting section that adjusts a vertical direction height of the transfer section; and a control section that, at a time at a time at which cargo is transferred between the transfer section and a cargo handler, operates the height adjusting section, and adjusts the vertical direction height of the transfer section.

Abstract: A walking training apparatus is a motion support system that includes a walking assistance apparatus as an assistance apparatus, a selected assistance level setting unit, a load data acquisition unit, an angle sensor as a displacement sensor, and an overall control unit as a control unit. The assistance apparatus assists a motion of a joint performed by a user. The assistance level setting unit sets an assistance level that is a level of a force exerted by the assistance apparatus. The load data acquisition unit acquires load data related to a load of the assistance apparatus. The displacement sensor detects a displacement of the joint. The control unit determines a recommended assistance level, which is an assistance level to be recommended, based on the assistance level, the load data, and an output of the displacement sensor, and outputs information about the determined recommended assistance level.

Abstract: The learning system includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

Abstract: A substrate processing apparatus includes a processing tank, a substrate holding section, a bubble supply section, and processing liquid supply sections. The substrate holding section immerses a substrate in a processing liquid stored in the processing tank. The bubble supply section supplies bubbles to the processing liquid from below the substrate. The processing tank includes a first side wall and a second side wall. The processing liquid supply sections include one or more first processing liquid supply sections and one or more second processing liquid supply sections. The one or more first processing liquid supply sections are disposed on a side of the first side wall and supply the processing liquid toward the bubbles. The one or more second processing liquid supply sections are disposed on a side of the second side wall and supply the processing liquid toward the bubbles.

Abstract: The learning apparatus includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

Abstract: The server is a learning apparatus including a data acquisition unit and a learning unit. The data acquisition unit acquires profile data and a selected assistance level as learning data. The profile data indicates a profile related to a trainee before executing rehabilitation regarding rehabilitation executed using a walking training apparatus as a rehabilitation support system. The selected assistance level is an assistance level selected at the time of executing the rehabilitation. The learning unit learns to determine a recommended assistance level recommended to be selected when the trainee uses the rehabilitation support system based on the learning data. Further, the learning unit generates a trained model that receives the profile data and outputs the recommended assistance level based on the learning.

Abstract: A motion state monitoring system, a training support system, a motion state monitoring method, and a program capable of suitably managing measurement results according to an attaching direction of a sensor are provided. A motion state monitoring system according to the present disclosure monitors a motion state of a target part of a subject's body. The motion state monitoring system includes an acquisition unit, an attaching direction detection unit, and a control processing unit. The acquisition unit acquires sensing information of a sensor attached to the target part. The attaching direction detection unit detects an attaching direction of the sensor. The control processing unit outputs information related to the sensing information in association with the attaching direction.

Abstract: A health management support system includes: an acquisition unit configured to acquire information about a current state of a user, information about a target health condition of the user after a lapse of a set period, and information about a budget of the user; a plan calculation unit configured to calculate at least one plan for a behavioral item that the user should perform during the set period based on the information about the current state of the user, the target health condition of the user after the lapse of the set period, and the budget; and an output unit configured to output the at least one plan calculated by the plan calculation unit.

Abstract: A health support system includes a group generation unit configured to generate, based on payment data, a group including a plurality of the users as members; a data detection unit configured to detect a change in a value of a predetermined data item in the physical data of the members; a purchase history extraction unit configured to extract purchase history of a product or a service that changes the value of the predetermined data item from the payment data of the member regarding whom the change in the value of the predetermined data item has been detected; a recommendation information generation unit configured to generate recommendation information for recommending the products or services specified by the extracted purchase history; and a notification unit configured to notify the other members of the group of the generated recommendation information.

Abstract: A health management support system includes: a first acquisition unit configured to acquire information about a user, including a health condition of the user; a second acquisition unit configured to acquire information about at least one purchase-candidate food; an estimation unit configured to estimate an effect on the health condition of the user that is caused when the user ingests the at least one purchase-candidate food; a calculation unit configured to calculate a cost that is incurred to alleviate the effect on the health condition of the user estimated by the estimation unit; and an output unit configured to output the effect on the health condition of the user estimated by the estimation unit and the cost calculated by the calculation unit.

Abstract: A health management support system includes: a first acquisition unit configured to acquire information about each of members of a specific group to which a user belongs, including his/her health condition; a second acquisition unit configured to acquire information about a food purchased by the user; an inference unit configured to infer, when an abnormality is detected in a health condition of any one of a plurality of members belonging to the specific group, as to which food has caused the abnormality in the health condition of the member from a history of foods purchased by the user; and a notification unit configured to notify the user that the food inferred by the inference unit may have caused the abnormality in the health condition of the member.

Abstract: An upward laminar flow of a treatment fluid is formed inside a treatment chamber. Substrates are immersed in the treatment fluid. Eight bubble supply pipes are disposed inside the treatment chamber, and supply bubbles in the treatment fluid from below the substrates. Two innermost bubble supply pipes out of the eight bubble supply pipes are disposed inside a recess of a punching plate so that the two bubble supply pipes are lower than the other bubble supply pipes. Even when a lifter lowers the substrates to an immersion position, an extremity of a back plate is prevented from hitting the two bubble supply pipes. Furthermore, the eight bubble supply pipes including the two innermost bubble supply pipes can uniformly supply bubbles to the surface of the substrates.

Abstract: Appropriate housing equipment according to the physical ability of a trainee who has performed rehabilitation in order to restore or maintain the physical ability is easily presented. A housing equipment presentation apparatus includes a storage unit configured to store definition information defining housing equipment information according to a physical ability, an acquisition unit configured to externally acquire physical ability information of a trainee who has performed training in order to restore or maintain his/her physical ability, a conversion unit configured to convert the acquired physical ability information into the housing equipment information based on the definition information, and an output unit configured to output presentation information including the converted housing equipment information.

Abstract: A state estimation program is for causing a computer to function to determine a state of training in a rehabilitation support system used by a trainee to perform training of a preset motion and includes a threshold setting step and a state estimation step. The threshold setting step is for acquiring a sensor output, the sensor output being an output of a sensor included in the rehabilitation support system in the training performed by the trainee and setting a threshold for determining one of a normal state and an abnormal state of the training based on the sensor output. The state estimation step is for estimating whether the training is performed in the normal state or the abnormal state based on the threshold.