JP5458428B2 - Motor function evaluation apparatus and program - Google Patents

Description

  The present invention relates to a motor function evaluation apparatus and the like that can detect a disorder level of a patient such as a stroke.

  When a movement disorder occurs due to a stroke or the like and a training is performed to restore its function, it is indispensable to correctly evaluate and grasp the dysfunction in order to select the type and level of training. It is also important to evaluate how much the function has improved after training in order to improve the later standard of living. However, in previous functional evaluations, evaluators such as doctors visually judge the state with an ordinal scale, and it can be denied that there may be a problem in the stability of evaluation and consistency among evaluators. There wasn't.

  Conventionally, there is a stroke dysfunction evaluation method referred to as SIAS (Stroke Impairment Assessment Set) (Non-patent Document 1). However, since SIAS is an evaluation on an order scale of about five steps, it was impossible to detect minute changes.

Conventionally, there is an evaluation system that uses the degree of jerk (smoothness, third-order differentiation of position information) (see Patent Document 1). This evaluation system objectively and easily evaluates the patient's skill in feedforward exercise in the feedforward exercise training device, and can restore the fast and accurate motor function in a relaxed state more effectively. It is a feedforward motion evaluation system that can.
Japanese Patent No. 3120065 (first page, FIG. 1 etc.) Internet web page <URL: ttp: //www.ko-rehabili.net/sias/index.html>

  However, since the conventional feedforward exercise evaluation system uses the jerk as an index, there is a problem that the exercise distance and exercise speed must be constant to some extent or must be normalized by the exercise distance and exercise speed. It was. In addition, the feed-forward exercise evaluation system must be clear at the start and end of exercise, and the third-order differentiation requires high data measurement accuracy, so it cannot be used easily in clinical settings.

  The apparatus for evaluating motor function according to the first aspect of the present invention is information relating to curvature from a plurality of pieces of position information indicating positions of a part of the body of the evaluated person when the evaluated person moves a part of the body. A curvature-related information acquisition unit that acquires curvature-related information; and an evaluation unit that acquires an evaluation result of a function of a part of the body of the evaluated person using the curvature-related information acquired by the curvature-related information acquisition unit; It is a motor function evaluation apparatus provided with the output part which outputs the evaluation result which the said evaluation part acquired.

  With this configuration, it is possible to evaluate the function of a part of the body using information on the curvature of the trajectory of the part of the body such as the hand.

  In addition, the motor function evaluation apparatus of the second invention further includes a position measurement unit that acquires the plurality of position information, as compared with the first invention, and the curvature related information acquisition unit is the position measurement unit. Is a motor function evaluation device that acquires curvature-related information from a plurality of position information acquired.

  With this configuration, it is possible to evaluate the function of a part of the body using the information on the curvature of the trajectory of the part of the body such as the hand while performing position measurement.

  In the motor function evaluation device according to the third aspect of the present invention, the curvature-related information acquisition unit performs second-order differentiation on the plurality of pieces of position information acquired by the position measurement unit with respect to either the first or second aspect of the invention. The curvature acquisition means for acquiring a plurality of curvatures, the curvature logarithmic value acquisition means for acquiring a plurality of curvature logarithm values that are logarithms of the plurality of curvatures acquired by the curvature acquisition means, and the curvature logarithm value acquisition means It is a motor function evaluation device comprising curvature related information acquisition means for acquiring curvature related information that is a median value of a plurality of curvature logarithmic values.

  With such a configuration, it is possible to perform an appropriate function evaluation of a part of the body using information on the curvature of the trajectory of the part of the body such as the hand.

  In the motor function evaluation device according to the fourth aspect of the present invention, for any one of the first to third aspects, the evaluation unit includes curvature evaluation correspondence information indicating a correspondence between a range of curvature related information and an evaluation value. A plurality of stored curvature evaluation correspondence information storage means; and an evaluation value acquisition means for acquiring an evaluation value corresponding to the curvature related information acquired by the curvature related information acquisition unit from the curvature evaluation correspondence information storage means. And the said output part is a motor function evaluation apparatus which outputs the evaluation value which the said evaluation value acquisition means acquired as an evaluation result.

  With such a configuration, an appropriate output of a function of a part of the body can be obtained.

  According to the motor function evaluation apparatus according to the present invention, it is possible to evaluate the function of a part of the body of the evaluated person.

  Hereinafter, embodiments of the motor function evaluation apparatus and the like will be described with reference to the drawings. In addition, since the component which attached | subjected the same code | symbol in embodiment performs the same operation | movement, description may be abbreviate | omitted again.

  (Embodiment)

  In the present embodiment, a motor function evaluation apparatus that performs appropriate function evaluation of a part of the body using information on the curvature of the trajectory of the part of the body such as the hand will be described. In addition, a part of the body includes an upper limb, a lower limb, a finger, a head, and the like. Moreover, it is suitable that the body part is an upper limb or a lower limb (limb). Furthermore, in the present embodiment, the case where the body part is the upper limb will be mainly described.

  FIG. 1 is a block diagram of a motor function evaluation apparatus 1 in the present embodiment. The motor function evaluation apparatus 1 includes a position measurement unit 11, a curvature related information acquisition unit 12, an evaluation unit 13, and an output unit 14.

  The curvature related information acquisition unit 12 includes a curvature acquisition unit 121, a curvature logarithmic value acquisition unit 122, and a curvature related information acquisition unit 123.

  The evaluation unit 13 includes curvature evaluation correspondence information storage means 131 and evaluation value acquisition means 132.

  The position measuring unit 11 acquires a plurality of pieces of position information of a part of the body of the person to be evaluated when the person to be evaluated moves a part of the body. For example, the position measurement unit 11 acquires trajectory information indicating the movement trajectory of the cup. That is, for example, an operation in which the person to be evaluated takes a hand holding a cup from the table to the mouth in a sitting position is repeated for 30 seconds. Note that 30 seconds is an example. Moreover, the position measurement part 11 acquires the several positional information on a to-be-evaluated person's upper limb, for example with a sampling frequency of 200 Hz. Note that the sampling frequency of the position measuring unit 11 does not matter. The position information is, for example, position information (x, y, z) in space. Needless to say, the data structure of the position information is not limited. The trajectory information is a set of a plurality of position information. The position measuring unit 11 is realized by, for example, a three-dimensional position measuring device. As an example of the three-dimensional position measuring apparatus, there is an apparatus called OPTOTRAK (registered trademark) using infrared rays. This three-dimensional position measurement device can measure the position of a marker by emitting infrared rays from an infrared light emitting diode (infrared marker) affixed to the measurement position and receiving it with three cameras at the top of the main body. In addition, this three-dimensional position measuring apparatus can simultaneously measure several tens of markers. The three-dimensional position measuring device may be FASTRAK (registered trademark) which is a magnetic position measuring device. In addition, the position measurement unit 11 may be anything as long as the position information of the evaluator's upper limb can be acquired. The upper limb is, for example, a hand tip, a shoulder, an upper arm, a forearm, a hand, or the like. Further, the position of the upper limb may be a position of the upper limb such as a cup held by the upper limb, or a position corresponding to the position of the upper limb.

The curvature related information acquisition unit 12 acquires curvature related information, which is information related to curvature, from a plurality of pieces of position information. The curvature related information is information that can be acquired from the curvature. The curvature-related information is preferably the median value of the logarithm of curvature described later, but may be the shape of a histogram created using the logarithm of curvature, the average value of the curvature logarithm value, or the like. Further, the curvature related information may be a set of curvatures. The curvature related information is preferably information related to the distribution of curvature. Note that the curvature (k) is information indicating how much the direction of the line changes every time a unit length is moved on the bent line, and information on transition of position information (x, y, z). Using (plurality of position information), it is expressed as Equation 1. The reciprocal of the curvature (k) is the curvature radius (ρ). The curvature related information may be considered as information related to the curvature radius (ρ).

  The curvature related information acquisition unit 12 can be usually realized by an MPU, a memory, or the like. The processing procedure of the curvature related information acquisition unit 12 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The curvature acquisition means 121 constituting the curvature related information acquisition unit 12 second-order differentiates the plurality of position information acquired by the position measurement unit 11 and acquires a plurality of curvatures. The curvature acquisition unit 121 usually obtains the curvature after the position measurement unit 11 acquires all the position information of the upper limb of the evaluated person during the upper limb movement by the evaluated person for a predetermined time (for example, 30 seconds). Although it acquires, a curvature may be acquired in parallel, while the position measurement part 11 acquires position information. The curvature acquisition unit 121 substitutes a plurality of pieces of position information (x, y, z) acquired by the position measurement unit 11 into Expression 1, and executes Expression 1 to acquire a plurality of curvatures. The curvature acquisition unit 121 can be usually realized by an MPU, a memory, or the like. The processing procedure of the curvature acquisition unit 121 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The curvature logarithmic value acquisition unit 122 acquires a plurality of curvature logarithm values that are logarithms of the plurality of curvatures acquired by the curvature acquisition unit 121. The curvature logarithmic value acquisition means 122 substitutes each of the plurality of curvatures acquired by the curvature acquisition means 121 into the mathematical expression “Log (curvature)”, and acquires a plurality of curvature logarithmic values. Note that the curvature logarithmic value acquisition unit 122 holds information on an arithmetic expression of a mathematical expression “Log (curvature)” in advance. The curvature logarithmic value acquisition unit 122 can be usually realized by an MPU, a memory, or the like. The processing procedure of the curvature logarithmic value acquisition means 122 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The curvature related information acquisition unit 123 acquires curvature related information that is the median value of the plurality of curvature logarithmic values acquired by the curvature logarithmic value acquisition unit 122. The curvature related information acquisition unit 123 can be usually realized by an MPU, a memory, or the like. The processing procedure of the curvature related information acquisition unit 123 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  In addition, when the curvature related information acquisition part 12 is implement | achieved by the curvature acquisition means 121, the curvature logarithm value acquisition means 122, and the curvature related information acquisition means 123, the curvature related information acquisition part 12 uses the curvature logarithm value as a median value. Obtained as related information. However, as described above, the curvature related information acquisition unit 12 may acquire, for example, the shape of a histogram created using the logarithm of curvature, the average value of the logarithm of curvature, and the like as curvature related information. .

  The evaluation unit 13 uses the curvature-related information acquired by the curvature-related information acquisition unit 12 to acquire a result of functional evaluation of a part of the body of the evaluated person (hereinafter referred to as “evaluation result” as appropriate). The evaluation result may be curvature related information itself or an evaluation value described later. In such a case, the evaluation unit 13 is a process of substituting curvature related information into an evaluation result (variable), or NOP (No Operation). The evaluation unit 13 can usually be realized by an MPU, a memory, or the like. The processing procedure of the evaluation unit 13 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The curvature evaluation correspondence information storage unit 131 stores a plurality of curvature evaluation correspondence information indicating correspondence between ranges of curvature related information and evaluation values. The range of curvature related information may be only the curvature related information itself. The curvature evaluation correspondence information is, for example, information on a median range of curvature logarithm values and an evaluation value pair. The curvature evaluation correspondence information storage means 131 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium. There is no limitation on the process in which the curvature evaluation correspondence information is stored in the curvature evaluation correspondence information storage unit 131. For example, the curvature evaluation correspondence information may be stored in the curvature evaluation correspondence information storage means 131 via a recording medium, and the curvature evaluation correspondence information transmitted via a communication line or the like is stored in the curvature evaluation correspondence information storage means. The curvature evaluation correspondence information input via the input device may be stored in the curvature evaluation correspondence information storage unit 131.

  The evaluation value acquisition unit 132 acquires an evaluation value corresponding to the curvature related information acquired by the curvature related information acquisition unit 12 from the curvature evaluation correspondence information storage unit 131. The evaluation value acquisition unit 132 can usually be realized by an MPU, a memory, or the like. The processing procedure of the evaluation value acquisition unit 132 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 14 outputs the evaluation result acquired by the evaluation unit 13. Note that the output unit 14 may output the evaluation value acquired by the evaluation value acquisition unit 132 as an evaluation result. The output mode of the evaluation result does not matter. Output refers to display on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage in a recording medium, processing results to other processing devices or other programs, etc. It is a concept that includes delivery. The output unit 14 may be considered as including or not including an output device such as a display or a speaker. The output unit 14 can be realized by output device driver software, or output device driver software and an output device.

  Next, operation | movement of the motor function evaluation apparatus 1 is demonstrated using the flowchart of FIG.

  (Step S201) The position measuring unit 11 acquires a plurality of pieces of position information of the upper limb of the evaluated person when the evaluated person moves the upper limb for a predetermined time.

  (Step S202) The curvature acquisition unit 121 reads an arithmetic expression (Formula 1) for calculating the curvature. Note that the curvature acquisition means 121 holds information on the arithmetic expression (Expression 1) in advance.

  (Step S203) The curvature acquisition unit 121 assigns 1 to the counter i.

  (Step S204) The curvature acquisition unit 121 determines whether or not the i-th position information exists. If the i-th position information exists, the process goes to step S205, and if the i-th position information does not exist, the process goes to step S208.

  (Step S205) The curvature acquisition unit 121 substitutes the i-th position information for the arithmetic expression read in Step S202, calculates the curvature corresponding to the i-th position information, and arranges it on the memory.

  (Step S206) The curvature logarithmic value acquisition means 122 calculates a curvature logarithm value (Log (curvature)) that is a logarithm of the curvature acquired in Step S205, and arranges it on the memory.

  (Step S207) The curvature acquisition unit 121 increments the counter i by one. The process returns to step S204.

  (Step S208) The curvature related information acquisition unit 123 acquires the median value of the plurality of curvature logarithm values acquired in Step S206 and arranges them on the memory. This median value is curvature related information here.

  (Step S209) The evaluation unit 13 searches the curvature evaluation correspondence information storage unit 131 using the curvature related information acquired in step S208 as a key, acquires an evaluation result corresponding to the curvature related information, and arranges it on the memory. .

  (Step S210) The output unit 14 outputs the evaluation result acquired in step S209, and ends the process.

  In the flowchart of FIG. 2, the curvature related information is a median value of a plurality of curvature logarithm values, but is not limited to the median value as described above.

  Hereinafter, a specific operation of the motor function evaluation apparatus 1 in the present embodiment will be described. The subject to be evaluated of the motor function evaluation apparatus 1 here is a patient whose one of the upper limbs is paralyzed due to a stroke. That is, in this specific example, the motor function evaluation apparatus 1 is used for function evaluation in the rehabilitation of a stroke hemiplegic upper limb. In particular, the motor function evaluation apparatus 1 is used to output evaluation values before and after performing specific training. And the evaluator etc. can digitize the effect of the said training by comparing the evaluation value before and behind training. Specific examples will be described below.

  First, before training, the person to be evaluated repeats the action of taking the cup from the table to the mouth for 30 seconds. Note that an infrared marker is attached to the cup.

  Next, the position measuring unit 11 measures the hand position with a sampling of about 200 Hz or more with an accuracy of about 0.2 mm or more, and acquires a plurality of pieces of position information of the upper limb of the person to be evaluated. The position measuring unit 11 (position measuring device) is an existing technology. Then, for example, the position measuring unit 11 obtains a position information management table having a structure as shown in FIG. 3, and at least temporarily stores it in a recording medium (a volatile recording medium or a non-volatile recording medium). accumulate. The position information management table holds one or more records having “ID”, “time t (Sec)”, and “position information”. “ID” is information for identifying a record. “Time t (Sec)” is an attribute indicating an elapsed time from the start of the experiment. “Position information” is an attribute that stores information indicating the position of the upper limb of the evaluated person acquired by the position measurement unit 11.

  Next, the curvature acquisition means 121 which comprises the curvature related information acquisition part 12 substitutes the some positional information which the position measurement part 11 acquired to Numerical formula 1, and acquires a curvature (k). And the curvature acquisition means 121 acquires the curvature (k) used as the origin which comprises the graph shown in FIG. 4, for example. FIG. 4 is a graph showing the change in curvature with time.

  Moreover, the graph which expanded the graph of FIG. 4 is shown in FIG. In the graph of FIG. 5, the curvature from “0” to “0.05” in FIG. 4 is enlarged. Further, the vertical axis of the graph of FIG. 5 is the curvature (1 / mm), and the horizontal axis is the time (Sec).

  Next, the curvature logarithmic value acquisition unit 122 acquires a curvature logarithm value (Log (k)) that is a logarithm of each of the plurality of curvatures (k) acquired by the curvature acquisition unit 121. FIG. 6 is an example of a histogram of curvature logarithmic values acquired by the curvature logarithmic value acquisition means 122. In FIG. 6, the horizontal axis represents the curvature logarithmic value, and the vertical axis represents the number of samples.

  Next, the curvature related information acquisition unit 123 acquires curvature related information that is the median value of the plurality of curvature logarithmic values acquired by the curvature logarithmic value acquisition unit 122. Here, it is assumed that the median value of the curvature logarithm acquired by the curvature related information acquisition unit 123 is “−3.7”. This curvature related information is the curvature related information of the person to be evaluated (P1) shown in FIG. In FIG. 7, the horizontal axis represents the SIAS value, and the vertical axis represents the median value of the curvature logarithm. FIG. 7 shows that the median value of the curvature logarithm of the evaluated person (P1) is “−3.7” and the SIAS value is “3”.

  Next, the evaluation unit 13 acquires the curvature related information “−3.7” acquired by the curvature related information acquisition unit 12 as an evaluation result.

  Then, the output unit 14 outputs the evaluation result “−3.7” acquired by the evaluation unit 13.

  As described above, the motor function evaluation apparatus 1 performs the function evaluation of the upper limb of the person to be evaluated before training, and outputs the function level as “−3.7”.

  Next, it is assumed that the person to be evaluated has undergone rehabilitation and has considerably recovered the function of the upper limb.

  Then, it is assumed that the evaluated person calculates and outputs an evaluation value using the motor function evaluation apparatus 1 after training in the same manner as described above. That is, the evaluated person repeats the operation of taking the cup from the table to the mouth for 30 seconds.

  Next, the position measuring unit 11 obtains a position information management table having a structure as shown in FIG.

  Next, the curvature acquisition means 121 which comprises the curvature related information acquisition part 12 substitutes the some positional information which the position measurement part 11 acquired to Numerical formula 1, and acquires a curvature (k). And the curvature acquisition means 121 acquires the curvature (k) used as the origin which comprises the graph shown in FIG. 8, for example. In addition, FIG. 8 is information which shows the time change of a curvature.

  Moreover, the graph which expanded the graph of FIG. 8 is shown in FIG.

  Next, the curvature logarithmic value acquisition unit 122 acquires a curvature logarithm value (Log (k)) that is a logarithm of each of the plurality of curvatures (k) acquired by the curvature acquisition unit 121. FIG. 10 is an example of a histogram of the curvature logarithm acquired by the curvature logarithmic value acquisition unit 122.

  Next, the curvature related information acquisition unit 123 acquires curvature related information that is the median value of the plurality of curvature logarithmic values acquired by the curvature logarithmic value acquisition unit 122. Here, it is assumed that the median value of the curvature logarithm acquired by the curvature related information acquisition unit 123 is “−5.2”. This curvature related information is curvature related information of the person to be evaluated (P1) shown in FIG. FIG. 11 shows that the median value of the curvature logarithm of the evaluated person (P1) is “−5.2” and the SIAS value is “4”.

  Next, the evaluation unit 13 acquires the curvature related information “−5.2” acquired by the curvature related information acquisition unit 12 as an evaluation result.

  Then, the evaluator such as a doctor recognizes that the evaluation value of the evaluated person (patient) is changed from “−3.7” to “−5.2”, and confirms the effect of the rehabilitation. Note that the smaller the evaluation value here, the closer to a healthy person. The value of healthy persons is, for example, around “−5.8”.

  Next, it will be shown below that the present evaluation method using the median value of the curvature logarithm as curvature related information has the same accuracy as a stroke dysfunction evaluation method called SIAS.

  FIGS. 12A and 12B show three graphs showing temporal changes in the curvature of the three persons using the motor function evaluation apparatus 1. FIG. The zoomed graphs of the three graphs are the three graphs on the right side of FIG. Note that the three line sets on the left side of FIG. 12 are sets of lines related to the trajectory showing the result of principal component analysis of information indicating the hand trajectory. From the above data, these three patients are relatively severe patients (SIAS evaluation value “3, 1a”), relatively mild patients (SIAS evaluation value “4, 1b”), healthy subjects (SIAS) Evaluation value “5, 5”). In addition, “X” of SIAS evaluation values “X, Y” indicates an evaluation value of the upper limb proximal test, and “Y” indicates an evaluation value of the upper limb distal test.

  Next, FIG. 13 is a graph obtained by zooming the graph of the time variation of the curvature of the six persons acquired using the motor function evaluation apparatus 1. Six people are P1, P2, P3, P4, P5 and P6.

  FIG. 14 is a histogram of the curvature-related information of the six persons acquired using the motor function evaluation apparatus 1.

  In FIG. 15, the horizontal axis represents the SIAS evaluation value, and the vertical axis represents the median value of the curvature logarithmic value (evaluation value of this evaluation method). FIG. 15 shows the measurement results (、, x) of the evaluation values of the six evaluation subjects of P1 to P6 twice. According to FIG. 15, the correlation coefficient between this evaluation method and the SIAS evaluation method was “−0.7”, and a high correlation was recognized. In addition, according to FIG. 15, since a value falls to the right, it becomes a negative correlation.

  As described above, according to the present embodiment, appropriate function evaluation of the upper limb can be performed using information on the curvature of the trajectory of the upper limb such as a hand. Moreover, according to this Embodiment, the minute change of the function of an upper limb can be detected. Furthermore, according to the present embodiment, since the function evaluation of the upper limb is performed using the curvature, the function evaluation of the upper limb can be easily performed at high speed.

  In addition, according to this Embodiment, you may think that the curvature relevant information acquisition part 12 performs the process until it acquires the logarithm value of a curvature, and an evaluation part acquires the median as an evaluation result. . In other words, the motor function evaluation apparatus 1 only needs to perform upper-limb function evaluation using the curvature.

  Moreover, according to this Embodiment, the motor function evaluation apparatus 1 comprised the position measurement part 11, the curvature related information acquisition part 12, the evaluation part 13, and the output part 14. FIG. However, the motor function evaluation apparatus 1 acquires a plurality of pieces of position information acquired by the position measurement unit 11 (position measurement apparatus) separated from the motor function evaluation apparatus 1 via a storage medium or a network, and the acquired plurality of pieces of information. The position information may be used to evaluate the function of the upper limb. In such a case, the motor function evaluation apparatus 1 may have a configuration including only the curvature related information acquisition unit 12, the evaluation unit 13, and the output unit 14.

In the present embodiment, the evaluation unit 13 is not necessarily composed of the curvature evaluation correspondence information storage unit 131 and the evaluation value acquisition unit 132. The evaluation part 13 should just acquire an evaluation result. The evaluation unit 13 may perform a predetermined calculation (for example, normalization processing) on the curvature related information acquired by the curvature related information acquisition unit 12 to acquire an evaluation result. In addition, when the evaluation part 13 is comprised from the curvature evaluation corresponding | compatible information storage means 131 and the evaluation value acquisition means 132, an evaluation result easy to understand for an evaluator, an evaluated person, etc. can be output.
Moreover, in this Embodiment, the case where a part of body was an upper limb was mainly demonstrated. However, as described above, the body part may be a lower limb, a finger, a head, or the like. That is, the above-described motor function evaluation apparatus 1 can also be used for function evaluation of lower limbs, fingers and the like.

  Furthermore, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded and distributed on a recording medium such as a CD-ROM. This also applies to other embodiments in this specification. In addition, the software which implement | achieves the motor function evaluation apparatus in this Embodiment is the following programs. In other words, this program uses a computer to calculate curvature-related information, which is information related to curvature, from a plurality of pieces of position information indicating positions of a part of the body of the evaluator when the evaluator moves a part of the body. Using the curvature related information acquired by the curvature related information acquisition unit, an evaluation unit for acquiring an evaluation result of a part of the body of the person to be evaluated, and the evaluation unit Is a program for functioning as an output unit that outputs the obtained evaluation result.

  The program further causes the computer to function as a position measurement unit that acquires the plurality of position information, and the curvature related information acquisition unit obtains curvature related information from the plurality of position information acquired by the position measurement unit. It is suitable that it is a program for functioning as what is acquired.

  In the program, the curvature-related information acquisition unit performs second-order differentiation on the plurality of pieces of position information acquired by the position measurement unit, and obtains a plurality of curvatures, and a plurality of pieces of information acquired by the curvature acquisition unit. Curvature logarithmic value acquisition means for acquiring a plurality of curvature logarithm values that are logarithms of curvature, and curvature related information acquisition means for acquiring curvature related information that is a median value of the plurality of curvature logarithm values acquired by the curvature logarithm value acquisition means It is preferable to make a computer function.

  Further, in the above program, the evaluation unit includes a curvature evaluation correspondence information storage unit that stores a plurality of curvature evaluation correspondence information indicating correspondence between ranges of curvature related information and evaluation values, and the curvature related information acquisition unit includes: Evaluation value acquisition means for acquiring an evaluation value corresponding to the acquired curvature related information from the curvature evaluation correspondence information storage means, and the output unit receives the evaluation value acquired by the evaluation value acquisition means as an evaluation result It is preferable to make the computer function as the output.

  FIG. 16 shows the external appearance of a computer that executes the program described in this specification to realize the motor function evaluation apparatus and the like of the above-described embodiment. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 16 is an overview diagram of the computer system 340, and FIG. 17 is a diagram showing an internal configuration of the computer system 340. As shown in FIG.

  In FIG. 16, the computer system 340 includes a computer 341 including an FD drive 3411 and a CD-ROM drive 3412, a keyboard 342, a mouse 343, and a monitor 344.

  In FIG. 17, in addition to the FD drive 3411 and the CD-ROM drive 3412, the computer 341 stores an MPU 3413, a bus 3414 connected to the CD-ROM drive 3412 and the FD drive 3411, and a program such as a bootup program. ROM 3415 for connecting to CPU 3413, RAM 3416 for temporarily storing application program instructions and providing a temporary storage space, and hard disk 3417 for storing application programs, system programs, and data . Although not shown here, the computer 341 may further include a network card that provides connection to the LAN.

  A program for causing the computer system 340 to execute functions such as the motor function evaluation apparatus of the above-described embodiment is stored in the CD-ROM 3501 or FD 3502, inserted into the CD-ROM drive 3412 or FD drive 3411, and further a hard disk. 3417 may be transferred. Alternatively, the program may be transmitted to the computer 341 via a network (not shown) and stored in the hard disk 3417. The program is loaded into the RAM 3416 at the time of execution. The program may be loaded directly from the CD-ROM 3501, the FD 3502, or the network.

  The program does not necessarily include an operating system (OS), a third-party program, or the like that causes the computer 341 to execute functions such as the motor function evaluation device of the above-described embodiment. The program only needs to include an instruction portion that calls an appropriate function (module) in a controlled manner and obtains a desired result. How the computer system 340 operates is well known and will not be described in detail.

  Further, the computer that executes the program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

  In each of the above embodiments, each process (each function) may be realized by centralized processing by a single device (system), or by distributed processing by a plurality of devices. May be.

  The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that these are also included in the scope of the present invention.

  As described above, the motor function evaluation apparatus according to the present invention has an effect that a part of the body of the person to be evaluated can be evaluated, for example, a motor function for performing function evaluation in rehabilitation of a stroke hemiplegic upper limb. It is useful as an evaluation device.

Block diagram of motor function evaluation apparatus in embodiment Flow chart for explaining the operation of the motor function evaluation apparatus Figure showing the location information management table Graph showing time change of curvature before training The graph which expanded the graph of the same figure 4 The figure which shows the example of the histogram of the same logarithm of curvature Graph showing curvature-related information of the person being evaluated Graph showing change in curvature over time after training The graph which expanded the graph of the same figure 8 The figure which shows the example of the histogram of the same logarithm of curvature Graph showing curvature-related information of the person being evaluated Graph showing time variation of same curvature A zoomed graph of the time-varying graph of the same curvature The figure which shows the example of the histogram of the curvature related information Figure showing the correlation between the SIAS and this evaluation method Overview of the computer system The figure which shows the internal configuration of the computer system

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Motor function evaluation apparatus 11 Position measurement part 12 Curvature related information acquisition part 13 Evaluation part 14 Output part 121 Curvature acquisition means 122 Curvature logarithm value acquisition means 123 Curvature related information acquisition means 131 Curvature evaluation corresponding | compatible information storage means 132 Evaluation value acquisition means

Claims (6)

An apparatus for evaluating motor function for detecting a level of disability of an evaluated subject having a movement disorder in a part of a body,
Curvature-related information that acquires curvature-related information, which is information related to curvature, from a plurality of pieces of position information indicating the position of a part of the subject's body when the subject is moving a part of the body three-dimensionally With an information acquisition unit,
The curvature related information acquisition unit
It said plurality of position information by differentiating upstairs curvature obtaining means for obtaining a plurality of curvatures,
Curvature logarithmic value acquisition means for acquiring a plurality of curvature logarithm values that are logarithms of a plurality of curvatures acquired by the curvature acquisition means;
Curvature-related information acquisition means for acquiring curvature-related information, which is information based on a plurality of curvature logarithm values acquired by the curvature logarithmic value acquisition means, and information relating to the distribution of curvature,
Using the curvature related information acquired by the curvature related information acquisition unit, an evaluation unit that acquires an evaluation result of a function of a part of the body of the evaluated person;
A motor function evaluation apparatus comprising: an output unit that outputs an evaluation result acquired by the evaluation unit. A position measuring unit for acquiring the plurality of position information;
The curvature related information acquisition unit
The motor function evaluation apparatus according to claim 1, wherein curvature-related information is acquired from a plurality of pieces of position information acquired by the position measurement unit. The evaluation unit is
Curvature evaluation correspondence information storage means for storing a plurality of curvature evaluation correspondence information indicating the correspondence between the range of curvature related information and the evaluation value;
Evaluation value acquisition means for acquiring an evaluation value corresponding to the curvature related information acquired by the curvature related information acquisition unit from the curvature evaluation correspondence information storage means,
The output unit is
The motor function evaluation apparatus according to claim 1, wherein the evaluation value acquired by the evaluation value acquisition unit is output as an evaluation result. The curvature related information acquisition means includes:
The motor function evaluation apparatus according to claim 1, wherein curvature-related information that is a median value of a plurality of curvature logarithms acquired by the curvature logarithmic value acquisition unit is acquired. The motor function evaluation apparatus according to claim 1, wherein the part of the body is an upper limb. A program for causing a computer to perform motor function evaluation for detecting a level of disability of an evaluated subject having a movement disorder in a part of the body,
Computer
Curvature-related information that acquires curvature-related information, which is information related to curvature, from a plurality of pieces of position information indicating the position of a part of the subject's body when the subject is moving a part of the body three-dimensionally An information acquisition unit,
It said plurality of position information by differentiating upstairs curvature obtaining means for obtaining a plurality of curvatures,
Curvature logarithmic value acquisition means for acquiring a plurality of curvature logarithm values that are logarithms of a plurality of curvatures acquired by the curvature acquisition means;
Curvature related information acquisition unit including curvature related information acquisition means for acquiring curvature related information, which is information based on a plurality of curvature logarithm values acquired by the curvature logarithmic value acquisition means, and information relating to the distribution of curvature,
Using the curvature related information acquired by the curvature related information acquisition unit, an evaluation unit that acquires an evaluation result of a function of a part of the body of the evaluated person;
The program for functioning as an output part which outputs the evaluation result which the said evaluation part acquired.