Patent ID: 12239435

The flowchart ofFIG.1illustrates 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, a three-dimensional dataset of the limb is acquired, for example by staff of a medical store.

The three-dimensional dataset is 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 a three-dimensional surface representation and thus the three-dimensional dataset. 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.

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. It is noted that the input classification information, which will, as described below, be used for determining a tension value as additional production value, may be complemented by results of the evaluation of the three-dimensional dataset, in particular be adding actually used measurement positions.

In a step S3, which may be performed on the 3D scan device or on a computing device, in particular a server, of a manufacturer of compression garments, the three-dimensional dataset is evaluated to derive reference information. Each reference information describes the position of at least one anatomical feature in the limb, as described in the three-dimensional dataset. These positions are, preferable, defined along the length of the limb. At least a part of the reference information is determined by analyzing how a characterizing value of the limb changes along the limb, for example a diameter or a circumference of the limb or a dimension of an anatomical structure. The characterizing value is determined from the three-dimensional dataset. For example, the sequence of characterizing values along the lengths of the limb may be analyzed to find a local or global maximum or minimum of the characterizing value along the length of the limb, for example to find a position where the limb is broadest or narrowest or the like. Other examples for reference information include the position of an anatomical landmark along the limb, for example a joint, a bone or the like.

In a step S4, a rule set, which may be stored in a storage means of the computing device and/or the 3D-scan device, is used to determine measurement positions for determining certain production values, in this case at least the skin value, from the reference information. Rules may define measurement positions depending on at least one position of an anatomical feature of at least one reference information and may also comprise conditions and/or logical operators.

For example, a measurement position may be defined as the broadest location of a foot or relatively, for example, as the narrowest location above the malleolus. Other rules may be to take a certain percentage of a distance between two anatomical features and/or use absolute distances to such an anatomical feature. Finally, definitions like a certain distance from the position of an anatomical feature, but at least 10 cm below another anatomical feature are also possible.

In this embodiment, for at least some measurement positions, two or more rules regarding the determination exist in the rule set. For example, a measurement position may relate to an anatomical feature as being a certain distance away, but also to another anatomical feature, for example as corresponding to the position of this other anatomical feature. In this case, a plausibility check may be performed if both reference informations are available. Additionally or complementary, both results of application of the rule may be statistically combined.

In a step S5, the production values are determined at (or in some cases even as) the measurement positions. In this embodiment, in the step S5, at least a skin value is determined at each measurement position. The skin value is defined as the circumference of the limb at the measurement position with no tension force exerted onto the skin. Thus, it may be easily derived from the three-dimensional dataset.

FIG.2shows an example of anatomical features and their positions1-14in a hip- and leg-area of a person, that is, legs15with corresponding feet16as well as the hip area17is shown. The lines18each mark measurement positions along the limb. The shown anatomical features, in this case, are as follows:

reference positionanatomical feature1bottom side of foot2broadest location of the foot3malleolus4narrowest location above malleolus5largest circumference of lower leg6lower edge of patella7middle of patella (popliteal cavity)8largest circumference leg9crotch10largest circumference buttocks11smallest circumference trunk12end of heel13metatarsophalangeal joint of the big toe14tip toe

In this example, the measurement position indicated by line18amay, for example, be determined using a rule “2 cm below the edge of the patella (position6)” and/or “5 cm below the middle of the patella (position7)”.

In a step S6, for each measurement position, 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. In this embodiment, the parameter is determined depending on input classification information.

In a storage means, which may also store the rule set, a tuple 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 tuple dataset is indicated by a step S7and takes place continuously.

In particular, all tuples present in the tuple 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 tuple 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 tuple dataset.

In a step S8, a parameter to be used in step S6is derived from the tuple dataset also using the input classification information. Two examples for the case of multiple information classes used shall be discussed as examples here.

In a first concrete example, for each information class, subsets are derived from the tuple 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.

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.

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.

In a second example, only one subset is generated from the tuple 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.

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.

The parameter derived from the tuple dataset depending on the input classification information in step S8is then used in step S6to calculate the tension value.

In a step S9, the measurement positions, their associated skin values and calculated tension values, optionally further production values 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 tuple dataset.

FIG.3illustrates an exemplary determination system19for performing the method according toFIG.1. In this case, the determination system19also comprises the 3D scan device20, in particular a tablet21, whose camera22may be used to accordingly scan the limb23of a person. The tablet21may also be used to gather and assemble at least a part of the input classification information.

The measured three-dimensional dataset is sent to a computing device24of the manufacturer of compression garments through the internet25and/or mobile networks. The three-dimensional dataset of the limb23and the input classification information are received by an interface26. The computing device24, which may be a server, in this case also comprises at least one processor27for performing the evaluations, determinations and calculations in steps S3, S4, S5, S6and S8. It is noted that the processor27may, at least in part, also be realized distributedly, for example regarding other computing devices28of the manufacturer, in particular other servers. In this example, the tuple dataset29is stored in a storage means30of a second computing device28, as is the rule set34. The storage means30and thus the tuple dataset29and the rule set34may be accessed by the processor27.

The production values (for each measurement position) and the input classification information are then transferred to a garment production apparatus31, in this case a knitting machine32, where they are used by a controller33to produce the custom-tailored compression garment for the limb23of the person. In particular, a knitting program may be derived from the production values. Alternatively, a knitting program may be compiled on a computing device24,28, according to these informations, and be transferred to the garment production apparatus31.

It is noted that in some embodiments, the determination system19may only comprise the at least one computing device24,28. If the garment production apparatus is added, the determination system19may also be understood as garment production system.