The present invention relates to an apparatus comprising: a seat for a patient; a support platform for the feet of the patient, positioned anteriorly to the seat; an alignment system allowing a patient's leg and its corresponding foot to be aligned in the three space planes in order to determine the tibia correct verticality; a system for properly positioning the foot on the platform including at least turrets able to allow pads to be applied in contact with the talus and/or scaphoid inside the foot and in contact with the calcaneus and/or cuboid outside the foot; a foot measurement system in a neutral position; an automatic real time driving system for properly positioning the leg and foot with numerical indication and direction of the movements to be carried out; a central unit capable for receiving and processing the data detected by the measurement system for footprint digitalization.

The invention relates to a method (500) for calculating personalized parameter values of a new custom sole for the design of custom soles, said method comprising: a step of loading (530) posture or mobility parameter values (101) of a user; a step of loading (540) shoe parameter values (201); a step of calculating (550) one or more personalized parameter values of a new custom sole (301).

The invention relates to a method (500) for calculating personalized parameter values of a new custom sole for the design of custom soles, said method comprising: a step of loading (530) posture or mobility parameter values (101) of a user; a step of loading (540) shoe parameter values (201); a step of calculating (550) one or more personalized parameter values of a new custom sole (301).

The present disclosure is related to three-dimensionally printed articles for use in footwear and associated systems and methods. In some embodiments, a three-dimensionally printed article may comprise a closed-cell foam. The closed-cell foam may have a gradient in and/or may be a single integrated material. In some embodiments, a three-dimensionally printed article may comprise a sensor. The use of such arrangements can, according to certain embodiments, allow for the production of improved articles of footwear and/or customized articles of footwear.

The present disclosure is related to three-dimensionally printed articles for use in footwear and associated systems and methods. In some embodiments, a three-dimensionally printed article may comprise a closed-cell foam. The closed-cell foam may have a gradient in and/or may be a single integrated material. In some embodiments, a three-dimensionally printed article may comprise a sensor. The use of such arrangements can, according to certain embodiments, allow for the production of improved articles of footwear and/or customized articles of footwear.

In a markerless foot size estimation device 100, a shape data input unit 10 acquires three-dimensional shape data of a foot of a subject and stores the three-dimensional shape data in a shape data storage unit 20. A characteristic extraction unit 30 extracts foot shape characteristics from the three-dimensional shape data of a subject's foot stored in the shape data storage unit 20, and stores the foot shape characteristics in a shape characteristic storage unit 40. A machine learning unit 50 uses, as teacher data, foot shape characteristics and arch height stored in the shape characteristic storage unit 40 to create, through machine learning, an estimation model used to estimate arch height based on foot shape characteristics, and stores the estimation model in an estimation model storage unit 60. An estimation output unit 70 uses the estimation model stored in the estimation model storage unit 60 to estimate arch height based on extracted shape characteristics and outputs the arch height.

In a markerless foot size estimation device 100, a shape data input unit 10 acquires three-dimensional shape data of a foot of a subject and stores the three-dimensional shape data in a shape data storage unit 20. A characteristic extraction unit 30 extracts foot shape characteristics from the three-dimensional shape data of a subject's foot stored in the shape data storage unit 20, and stores the foot shape characteristics in a shape characteristic storage unit 40. A machine learning unit 50 uses, as teacher data, foot shape characteristics and arch height stored in the shape characteristic storage unit 40 to create, through machine learning, an estimation model used to estimate arch height based on foot shape characteristics, and stores the estimation model in an estimation model storage unit 60. An estimation output unit 70 uses the estimation model stored in the estimation model storage unit 60 to estimate arch height based on extracted shape characteristics and outputs the arch height.

A prediction device includes: a communication device that acquires measurement subject data including measurement data of a shape of a foot of a measurement subject person in a loaded state; a storage that stores first sample data in the loaded state and second sample data in an unloaded state, the first sample data and the second sample data being calculated from measurement data of foot shapes of a plurality of samples, the samples being identical both in the loaded state and the unloaded state; and a processor that predicts the shape of the foot of the measurement subject person in the unloaded state. The processor calculates a difference between the measurement subject data and the first sample data, and predicts the shape of the foot of the measurement subject person in the unloaded state based on the difference and the second sample data.

A prediction device includes: a communication device that acquires measurement subject data including measurement data of a shape of a foot of a measurement subject person in a loaded state; a storage that stores first sample data in the loaded state and second sample data in an unloaded state, the first sample data and the second sample data being calculated from measurement data of foot shapes of a plurality of samples, the samples being identical both in the loaded state and the unloaded state; and a processor that predicts the shape of the foot of the measurement subject person in the unloaded state. The processor calculates a difference between the measurement subject data and the first sample data, and predicts the shape of the foot of the measurement subject person in the unloaded state based on the difference and the second sample data.

In at least some embodiments, an orthotic footbed assembly includes a three-dimensional footbed that closely approximates a size and configuration of a particular foot and at least one orthotic feature additively coupled to the three-dimensional footbed. In at least some other embodiments, a method of making the orthotic footbed assembly includes direct printing of orthotic features onto a 3-D footbed. Another method includes indirect printing of orthotic features and applying these features onto to a 3-D footbed. And yet another method includes direct printing of orthotic features onto a 2-D blank footbed and then conducting a three-dimensional forming operation to make the orthotic footbed assembly.