METHOD AND SYSTEM FOR DETERMINING A TENSION VALUE OF A LIMB OF A PERSON AND COMPUTER PROGRAM

Abstract

Computer-implemented method for determining a tension value of a limb (5) of a person, the tension value of the limb (5) being used along with a skin value of the limb (5) for production of a custom-tailored compression garment for the limb (5), the skin value describing the circumference of the limb (5) without any applied compression and the tension value describing the circumference of the limb (5) with the compression garment applying a desired compression, wherein the skin value of the limb (5) is received and the tension value of the limb (5) is calculated from the skin value according to a calculation instruction parametrized by at least one parameter, the parameter being derived from a dataset (11) comprising multiple associated tuples of skin values and tension values.

Claims

1. Computer-implemented method for determining a tension value of a limb (5) of a person, the tension value of the limb (5) being used along with a skin value of the limb (5) for production of a custom-tailored compression garment for the limb (5), the skin value describing the circumference of the limb (5) without any applied compression and the tension value describing the circumference of the limb (5) with the compression garment applying a desired compression, characterized in that the skin value of the limb (5) is received and the tension value of the limb (5) is calculated from the skin value according to a calculation instruction parametrized by at least one parameter, the parameter being derived from a dataset (11) comprising multiple associated tuples of skin values and tension values.

2. Method according to claim 1, characterized in that the dataset (11) comprises at least 1.000 different tuples, in particular at least 100.000 different tuples, and/or tuples describing actually produced compression garments.

3. Method according to claim 1, characterized in that the parameter is a factor with which the skin value of the limb (5) is multiplied.

4. Method according to one of the preceding claim 1, characterized in that at least one input classification information, each relating to an information class, is provided along with the skin value of the limb (5), wherein the at least one parameter is chosen depending on the input classification information and/or as a parameter associated with the input classification information.

5. Method according to claim 4, characterized in that each tuple of the dataset (11) additionally comprises, for each information class, a dataset classification information associated with its skin value and tension value, wherein the parameter is determined from at least one subset of the dataset tuples comprising at least one dataset classification information matching the corresponding input classification information and/or by interpolation regarding at least one of the at least one input classification information.

6. Method according to claim 5, characterized in that subparameters are determined for at least two subsets relating to different information classes, wherein the parameter is determined by using an, in particular weighted, mean of the subparameters, and/or that the parameter or at least one subparameter is determined from a subset for which at least two input classification information match the corresponding dataset classification information.

7. Method according to claim 4, characterized in that the at least one information class is chosen from a group comprising a country class describing a country where the compression garment is to be used, a compression class, a garment information class, an indication class containing medical indications to be treated using the compression garment, a measurement position class comprising measurement positions along the length of the limb (5), a limb class, a person age class, a person weight class, a person gender class, a compression garment length class, and a person tissue property class.

8. Method according to claim 7, characterized in that an input classification information of the patient tissue property class is determined by measurement.

9. Method according to claim 1, characterized in that each tuple of the dataset (11) additionally comprises a reliability information associated with its skin value and tension value, wherein, when deriving the parameter from the dataset (11), tuples are excluded and/or weighted according to their reliability information.

10. Method according to claim 9, characterized in that the reliability information comprises information regarding complaints received relating to a compression garment produced using the skin value and the tension value of the tuple.

11. Method according to claim 1, characterized in that the skin value is determined, in particular contact-free, using a 3D scan device (2) scanning the limb (5).

12. Method according to claim 11, characterized in that a tablet (3) or mobile phone running a scanning application computer program is used as the 3D scan device (2).

13. Method for producing a compression garment for a limb (5) of a patient, comprising automatically performing the steps of a method according to claim 1, whereafter the compression garment is automatically produced by a garment production apparatus (13), in particular a knitting machine (14), using the received skin value and the determined tension value.

14. Determination system (1) for a tension value of a limb (5) of a person, the tension value of the limb (5) being used along with a skin value of the limb (5) for production of a custom-tailored compression garment for the limb (5), the skin value describing the circumference of the limb (5) without any applied compression and the tension value describing the circumference of the limb (5) with the compression garment applying a desired compression, characterized by an interface (8) for receiving the skin value of the limb (5), and a processor (9) for calculating the tension value of the limb (5) from the skin value according to a calculation instruction parametrized by at least one parameter, the parameter being derived from a dataset (11) stored in a storage means (12) of the determination system (1) and comprising multiple associated tuples of skin values and tension values.

15. Computer program, which performs the steps of a method according to claim 1 when the computer program is executed on a computing device (6, 10), in particular of a determination system (1).

Description

[0043] Further details and advantages of the current invention may be taken from the following description of preferred embodiments taken in conjunction with the drawings, in which:

[0044] FIG. 1 is a flowchart of an embodiment of a method according to the invention, and

[0045] FIG. 2 shows an embodiment of a determination system according to the invention.

[0046] The flowchart of FIG. 1 illustrates an embodiment of a method according to the invention. The aim of the method described in the following is to produce a custom-tailored compression garment for the limb of a person. In a step S1, the skin value of the limb is measured, for example by staff of a medical store. The skin value of the limb may be taken manually at multiple measurement positions along the length of the limb which are relevant for the compression garment to be produced. The skin value is defined as the circumference of the limb at the measurement position with no tension or force exerted onto the skin.

[0047] Preferably, in step S1, the skin value may be measured using a 3D scan device, which may in particular be realized as a tablet or mobile phone on which an application computer program is provided. For example, a camera of the tablet or mobile phone may be used to image the limb from multiple views, which may be evaluated to derive the skin value. Alternatively to such a tablet or mobile phone, the 3D scan device may be a dedicated scanner, for example a whole-body scanner and/or a limb scanner. The use of a 3D scan device is advantageous, since a contact-free measurement is possible and the measurement is not confined to only a few measurement positions.

[0048] In a generally optional step S2, additional information regarding the person, in particular a patient, and/or the garment is gathered as input classification information for, in this case, multiple information classes. The information classes may comprise a country class describing a country where the compression garment is to be used, a compression class, a garment information class, an indication class containing medical indications to be treated using the compression garment, a measurement position class comprising measurement positions along the length of the limb, a limb class, a person age class, a person weight class, a person gender class, a compression garment length class and/or a person tissue property class. For example, input classification information may be entered using the 3D scan device, in particular the tablet or mobile phone. In the case of patient tissue properties, these may also be measured, for example by using a hardness tester or the like.

[0049] The measured skin values for each measurement position as well as the input classification information are transferred to a computing device, in particular a server, of a manufacturer of compression garments, where they are received in step S3. For example, the skin value of the limb and the additional classification information may, for example, be communicated using the internet.

[0050] In a step S4, a calculation instruction is used to calculate a tension value of the limb from the skin value of the limb using a calculation instruction, in this case by multiplying the skin value with a factor. The factor is thus a parameter of the calculation instruction. Since, in this embodiment, the parameter is determined depending on input classification information, in step S3, the input classification information has already been transferred to a parameter determination unit of the receiving computing device and/or an additional computing device.

[0051] In a storage means, a dataset comprising tuples of skin values and associated tension values, as well as dataset classification information associated with the pair of skin value and tension value, is stored. The maintenance of this dataset is indicated by a step S5 and takes place continuously.

[0052] In particular, all tuples present in the dataset relate to actually produced compression garments, such that a reliability information is also associated with each tuple. In this embodiment, the reliability information at least describes whether there has been a complaint regarding the fitting of the compression garment. New tuples, in this respect, as only added to the dataset once the respective reliability information becomes available. In particular, feedback regarding produced compression garments is awaited before a tuple is eligible for entry into the dataset.

[0053] In a step S6, a parameter to be used in step S4 is derived from the dataset also using the input classification information. Two examples for the case of multiple information classes used shall be discussed as examples here.

[0054] In a first concrete example, for each information class, subsets are derived from the dataset, wherein a subset for each information class is generated by selecting all tuples in which the input classification information of the information class equals the dataset classification information of the respective tuple. For example, if the information class is a person gender class, and the input classification information is female, a respective subset contains all tuples that relate to female persons.

[0055] For each subset generated in this manner, a subparameter is derived, for example, by fitting the calculation instruction to the tuples in the subset. In this process, tuples for which the reliability information shows a complaint regarding fitting may be excluded or lower weighted.

[0056] From the subparameters for all information classes, the parameter is derived by calculating the mean, in particular a weighted mean, such that the impact of certain information classes may be taken into account.

[0057] In a second example, only one subset is generated from the dataset, the subset containing all tuples for which all input classification information match the respective dataset classification information. The subset is thus an intersection of all the subsets generated in the first example. From this subset, the parameter is, again, derived by fitting the calculation instruction to the tuples.

[0058] It should be noted that is of course also possible to combine the first example and the second example, for example by forming subsets for groups of information classes instead of only single classes. If a classification information includes a continuous value, it is also possible to derive, in particular by interpolation, a function which describes how the parameter depends on the respective classification information. For the respective information class, the parameter or subparameter may thus be calculated.

[0059] The parameter derived from the dataset depending on the input classification information in step S6 is then used in step S4 to calculate the tension value.

[0060] In a step S7, the measured skin value of the limb, the calculated tension value of the limb and the input classification information, at least in part, are used to produce a custom-tailored compression garment for a person. As has already been noted, feedback regarding the fitting may be awaited before entering the newly calculated tuple into the dataset.

[0061] FIG. 2 illustrates an exemplary determination system 1 for performing the method according to FIG. 1. In this case, the determination system 1 also comprises the 3D scan device 2, in particular a tablet 3, whose camera 4 may be used to accordingly scan the limb 5 of a person. The tablet 3 may also be used to gather and assemble the input classification information. It is noted at this point that the input classification information may, in particular, also be used to determine measurement positions at which the skin value is to be determined.

[0062] The measured skin values at the respective measurement positions and the input classification information are sent to a computing device 6 of the manufacturer of compression garments through the internet 7 and/or mobile networks. The skin values of the limb 5 and the input classification information are received by an interface 8. The computing device 6, which may be a server, in this case also comprises at least one processor 9 for performing the calculations in step S4 and S6. It is noted that the processor 9 may, at least in part, also be realized distributedly, for example regarding other computing devices 10 of the manufacturer, in particular other servers. In this example, the dataset 11 is stored in a storage means 12 of a second computing device 10. The store means 12 and thus the dataset 11 may be accessed by the processor 9.

[0063] The measured skin values, the corresponding calculated tension values (for each measurement position) and the input classification information are then transferred to a garment production apparatus 13, in this case a knitting machine 14, where they are used by a controller 15 to produce the custom-tailored compression garment for the limb 5 of the person. Alternatively, a knitting program may be compiled on a computing device 6, 10, according to these informations, and be transferred to the garment production apparatus 13.

[0064] It is noted that in some embodiments, the determination system may only comprise the at least one computing device 6, 10. If the garment production apparatus is added, the determination system 1 may also be understood as garment production system.