Assist wear evaluation system, method, and program

Abstract

To provide a technique capable of evaluating a reduction effect of a physical work burden while a user wears an assist wear actually.SOLUTION: An assist wear evaluation system comprises: a measurement device for measuring biological data of the total body of a user while wearing the assist wear; a calculation device for calculating a burden on the user on the basis of the biological data of the total body; and an output device for outputting a wearing evaluation of the user while wearing the assist wear on the basis of the burden.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a technology for evaluating the effect of reducing physical work load while wearing assist wear. [Background technology]

[0002] In recent years, assistive wear has been developed to assist with physical labor and daily activities. Assistive wear can be roughly categorized into wearable motion-assistance devices that are worn by the wearer (hereafter referred to as the user) as power assist, and core stabilization suits that reduce the load on the user's body by correcting their posture using the elasticity of the fabric without using power such as a motor.

[0003] In Patent Document 1, a wearable motion-assist device that generates a driving force to assist the user's waist movement is evaluated while attached to a humanoid motion test unit. The humanoid motion test unit has an external shape that is almost the same as the upper part of a human body from the knees up, and is provided with a joint mechanism that allows it to simulate a series of movements that put strain on the waist, from lifting an object to putting it down.

[0004] The load on the user's lower back caused by the wearable action-assist device is estimated based on the compressive force of the part of the trunk link that has an external configuration that mimics the human torso and head, which corresponds to the lower back, and the detection result of the six-axis force sensor as a load detection unit that detects the bending moment. Based on this estimated load, it becomes possible to determine to what extent the wearable action-assist device is reducing the burden on the lower back.

[0005] Patent Document 2 discloses a muscle strength assisting orthosis consisting of a corset tightening force adjuster attached to the outside of the back of the garment and a corset worn around the waist from the outside of the garment. This muscle strength assisting orthosis is a trunk stabilizing suit that reduces stress on the body by correcting the user's posture using the elasticity of fabric such as carbon fiber on the back without using a driving force.

[0006] Non-patent document 1 introduces efforts to develop an evaluation method for "STAYS," an assistive wear that reduces strain on the body without using driving force. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent No. 6556867 [Patent Document 2] Patent No. 6613397 [Non-patent literature]

[0008] [Non-Patent Document 1] TIRI NEWS February 2020 issue, published February 1, 2020, pp. 6-7, Assist wear that reduces strain on the body without using driving force, https: / / www.iri-tokyo.jp / uploaded / attachment / 11140.pdf Summary of the Invention [Problem to be solved by the invention]

[0009] However, Patent Document 1 has a problem in that it is not possible to evaluate the wearable action-assist device while the user is actually wearing it. Patent Document 2 has a problem in that it does not disclose an evaluation of the core stabilization suit while the user is actually wearing it. Non-Patent Document 1 only mentions the need to visualize the human sensation of "feeling comfortable when wearing it" as data, but does not disclose how to visualize it.

[0010] The present invention was completed through extensive research focusing on these issues, and its purpose is to provide technology that can evaluate the effect of assist wear on reducing physical work load when the user is actually wearing it. [Means for solving the problem]

[0011] In order to solve the above problems, the present invention is an assist wear evaluation system that includes a measuring device that measures the biometric data of the user's entire body while wearing the assist wear, a calculating device that calculates the load on the user based on the biometric data of the entire body, and an output device that outputs an evaluation of the user's wearing of the assist wear while wearing the assist wear based on the load.

[0012] Another invention is a method for evaluating assist wear, which measures the biometric data of a user's entire body while wearing the assist wear, calculates the load on the user based on the biometric data of the entire body, and outputs a wearing evaluation of the user while wearing the assist wear based on the load.

[0013] Another invention is an assist wear evaluation program that causes a computer to execute the steps of measuring the biometric data of a user's entire body while wearing the assist wear, calculating the load on the user based on the biometric data of the entire body, and outputting an evaluation of the user's wearing of the assist wear while wearing the assist wear based on the load. [Effects of the Invention]

[0014] According to the present invention, it is possible to provide a technology that can evaluate the effect of reducing physical work load when a user is actually wearing assist wear. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a functional block diagram of an evaluation system according to an embodiment of the present invention. [Figure 2] FIG. 4 is a diagram showing measurement positions of myoelectric potential according to the present embodiment. [Figure 3] 1 is a flowchart of an evaluation system according to the present embodiment. [Figure 4] 10 is a table showing attributes of users according to the present embodiment. [Figure 5] 10 is a table showing evaluation values ​​for wearing and not wearing the assist wear according to the present embodiment. [Figure 6] 10 is a graph showing a comparison between wearing and not wearing the assist wear according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] An embodiment of the present invention will be described with reference to the drawings.

[0017] (Assist wear evaluation system) 1 is a functional block diagram of an assist wear evaluation system according to an embodiment of the present invention. The evaluation system 100 includes a measuring device 210 that measures whole-body biometric data of a user wearing the assist wear to be evaluated, a trigger switch 220 used to extract the user's movement intervals, a receiver 230 that receives signals from the measuring device 210 and the trigger switch 220, and an information processing device 300 that evaluates the assist wear based on the data from the receiver 230. Here, the whole-body biometric data of the user refers to the user's whole-body myoelectric potential data, the user's whole-body movement data, and the biosignals generated by these.

[0018] The information processing device 300 is configured by a group of functional blocks that can be operated by an evaluation program installed in a personal computer. Here, the implementation form of the information processing device 300 does not require that all of the functional blocks are configured in the personal computer. For example, the information processing device 300 may be installed and implemented on a dedicated server connected to a client terminal such as a personal computer via a wired or wireless communication line (such as the Internet line), or may be implemented using a so-called cloud service.

[0019] The measuring device 210 is composed of a plurality of electromyographic sensors. In this embodiment, eight wireless electromyographic sensors are used.

[0020] 2 is a diagram showing the measurement positions of myoelectric potential according to this embodiment. It shows the positions of myoelectric sensors attached to the front and back of the user. It can be seen that the myoelectric sensors are attached to the skin surface of the entire body (upper and lower body) of the user.

[0021] Reference numeral 211 denotes an electromyographic sensor attached to the skin surface of the rectus femoris. Similarly, reference numeral 212 denotes the position of the biceps femoris, 213 the position of the tibialis anterior, 214 the position of the gastrocnemius, and 215 the position of the gluteus maximus. In other words, five electromyographic sensors are attached to the legs (lower limbs) of the user.

[0022] 216 indicates the position of the rectus abdominis muscle (lower part), 217 indicates the position of the erector spinae muscle (lower part), and 218 indicates the position of the trapezius muscle (upper part). In other words, three electromyographic sensors are attached to the trunk of the user.

[0023] In this embodiment, the trunk is used as an example of a position on the upper body of a user where the myoelectric sensor is attached, but the position is not limited to this and the sensor may be attached to another position on the upper body.

[0024] In this embodiment, the myoelectric potential of the entire body (upper and lower body) of the user is measured, rather than measuring the myoelectric potential of only a specific part (such as only the lower body). The number of myoelectric sensors is not limited to eight as shown in Fig. 2, and may be increased or decreased as long as the myoelectric potential of the entire body is measured.

[0025] The receiver 230 receives a myoelectric waveform signal from the measuring device 210 (wireless myoelectric sensor), and also receives a signal from the trigger switch 220.

[0026] The information processing device 300 includes an electromyographic waveform storage unit 310 that stores electromyographic waveform data received by the receiver 230, a trigger data storage unit 320 that stores trigger data received by the receiver 230, a signal processing unit 330 that performs predetermined signal processing using the electromyographic waveform data and the trigger data, and an evaluation result calculation unit 340 that calculates an evaluation result of the assistwear based on the result of the signal processing.

[0027] The signal processing unit 330 includes an electromyographic waveform shaping unit 331 that shapes electromyographic waveform data, which is a raw waveform; an action section extraction unit 332 that extracts electromyographic waveform data only for the section in which the user moved from the shaped electromyographic waveform data based on trigger data; a PeakRMS extraction unit 333 that calculates the Peak RMS (Root Mean Square) value to extract the maximum amplitude (μV) of the extracted electromyographic waveform; and a PeakRMS average value extraction unit 334 that extracts the average value of the PeakRMS values ​​calculated each time the user performs a plurality of movements.

[0028] The PeakRMS extraction unit 333 calculates the formula (1) while shifting it by an arbitrary number of samples, and outputs the maximum value of the obtained data as the maximum amplitude (μV) of the myoelectric waveform.

number

[0029] The myoelectric waveform shaping unit 331 shapes the myoelectric waveform, which is a raw waveform including a positive waveform (+) and a negative waveform (-), into a rectified waveform (+ only). Note that in this embodiment, since the Peak RMS value is calculated, the myoelectric waveform shaping unit 331 is not essential.

[0030] The movement section extraction unit 332 is necessary to extract the movement section (in this case, lifting luggage) required for evaluating the assist wear when the user performs continuous movements (for example, when lifting and lowering luggage continuously), but is not essential when continuous movements are not performed.

[0031] The myoelectric waveform storage unit 310, trigger data storage unit 320, and myoelectric waveform shaping unit 331 are software included in the measurement device (myoelectric sensor) 210. Here, both the input and output data of the myoelectric waveform shaping unit 331 are data in CSV (Comma Separated Value) format. Therefore, the order of processing by the myoelectric waveform shaping unit 331 and the movement section extraction unit 332 may be changed.

[0032] FIG. 3 is a flowchart of the evaluation system according to this embodiment. In this embodiment, first, the user performs five trials without wearing the assist wear. Next, the same user performs five trials again while wearing the assist wear. Here, the user lifts and lowers a load (six 2L plastic bottles; approximately 12 kg) five times in succession. Specifically, the user bends their legs and crouches on the ground, grabs the load placed on the ground with both hands, stretches both legs, and lifts it up. Then, the section of the movement that only involves lifting the load is extracted and counted as one trial.

[0033] First, we will explain the case where the user performs five trials without wearing the assist wear. As explained in Figure 2, the user wears eight electromyographic sensors. That is, the measuring device 210 measures each of the user's electromyographic potentials using eight-channel (8ch) electromyographic sensors (S110).

[0034] The information processing device 300 stores the data (data for five trials) received from the measuring device 210 and the trigger switch 220 via the receiver 230 in the myoelectric waveform storage unit 310 and the trigger data storage unit 320, respectively. Then, the myoelectric waveform shaping unit 331 shapes the myoelectric waveform, and the movement section extraction unit 332 extracts the waveform of the movement section corresponding to the trial. This extracted waveform is extracted as a specific frequency band signal (S120).

[0035] The PeakRMS value extracting unit 333 calculates the RMS value from the extracted specific frequency band signal using equation (1) (S130), and further extracts the PeakRMS value (S140).

[0036] The processing steps from S110 to S140 are executed for five trials, and the PeakRMS average value extraction unit 334 calculates the PeakRMS average value (S150). The processing steps from S110 to S150 are performed when the assist wear is not being worn.

[0037] Next, we will explain what happens when a user performs five trials while wearing the assist wear. Since the processing steps are the same as those from S110 to S150, we will explain them briefly. The measurement device 210 measures each of the user's myoelectric potentials using an eight-channel (8ch) myoelectric sensor (S210).

[0038] The information processing device 300 extracts a specific frequency band signal (S220) using the myoelectric waveform shaping unit 331 and the movement section extraction unit 332. The Peak RMS value extraction unit 333 calculates an RMS value from the extracted specific frequency band signal (S230), and further extracts the Peak RMS value (S240).

[0039] The processing steps from S210 to S240 are executed for five trials, and the PeakRMS average value extraction unit 334 calculates the PeakRMS average value (S250). The processing steps from S210 to S250 are performed while the assist wear is being worn.

[0040] The information processing device 300 causes the evaluation result calculation unit 340 to calculate the ratio of worn / unworn (S310). Specifically, the ratio of worn / unworn is calculated using the Peak RMS average values ​​of S150 (unworn) and S250 (worn).

[0041] The evaluation result calculation unit 340 outputs the evaluation result based on the calculated ratio of attached / unattached (S320).

[0042] FIG. 4 is a table showing the attributes of users according to this embodiment. The attributes of the users include the ages, heights, weights, and daily activities of three users. In this embodiment, the test (lifting luggage) described in FIG. 3 was performed on three users. In other words, the PeakRMS average values ​​calculated in S150 and S250 of FIG. 3 are the average values ​​of the three users.

[0043] Figure 5 is a table showing the evaluation scores for wearing and not wearing the assist wear according to this embodiment. In this embodiment, the assist wear used is "STAYS (Stay)" made by Hayama Principle Co., Ltd., a vest-type assist wear that reduces the burden on the body without using a driving force. The test was "lifting luggage."

[0044] Column 510 shows the results of calculating the average PeakRMS value for each muscle for the three users when not wearing assist wear, while column 530 shows the results of calculating the average PeakRMS value for each muscle for the three users when wearing assist wear.

[0045] Column 520 indicates that each value in column 510 is set to 100%, and column 540 indicates what percentage each value in column 530 becomes in this case. In other words, column 540 corresponds to the evaluation result of the assist wear, and shows a comparison of the activity of each muscle when wearing or not wearing the wear.

[0046] 6 is a graph showing a comparison between wearing and not wearing the assist wear according to this embodiment. Columns 520 and 540 in FIG. 5 are represented by bar graphs to clearly illustrate the evaluation results of the assist wear.

[0047] Figure 6 shows that the values ​​for the rectus femoris and other muscles in the lower limbs generally decreased, meaning that the amount of activity decreased. This can be interpreted as meaning that wearing the assist wear stabilizes the posture of the entire body and reduces the physical strain required to maintain posture.

[0048] On the other hand, the trapezius muscle was the only muscle whose value increased, meaning that the amount of activity increased. This shows that when the wearer was not wearing the assist wear, the muscles of the upper body were not mobilized effectively. However, when the wearer was wearing the assist wear, the posture of the entire body was stabilized and the muscles of the upper body were mobilized effectively, which can be evaluated as the muscles of the entire body being used effectively to lift the load. In other words, when the wearer was wearing assist wear that did not use driving force, the work was shared with muscles that were not used when not wearing the wear, and it can be evaluated as preventing the load from being concentrated on specific muscles.

[0049] According to this embodiment, by having the user perform trials both with and without the assist wear and comparing the evaluation results, it becomes possible to evaluate the effect of reducing physical work burden when the user is actually wearing the assist wear.

[0050] Furthermore, there are many different types of assist wear products (such as wearable movement assist devices and trunk stabilization suits). Users visit a store that sells these products, try on several types of assist wear, and perform a trial run for each product (such as a trial run similar to the user's physical labor activities or daily life activities). This evaluation system makes it possible to compare which products can reduce the physical work burden on users, which will lead to sales promotion of assist wear.

[0051] Furthermore, even within the same type of assistive wear, some products are available in sizes tailored to different body sizes, such as small, medium, and large. In such cases, users can try on assistive wear of each size and compare which size will most effectively reduce the physical workload for the user.

[0052] In addition, even if assist wear is the same size, it will be possible to compare the extent to which physical work burden can be reduced depending on how well it is worn (for example, in the case of vest-type assist wear, how tightly the waist and shoulder belts are fastened).

[0053] (Action and effect) The evaluation system of this embodiment makes it possible to evaluate the effect of reducing physical work load when the user is actually wearing the assist wear.

[0054] (Variation 1; Motion Capture) In the embodiment, an electromyographic sensor was used to measure myoelectric potential as the measuring device 210 for measuring the biometric data of the entire body of a user wearing assistive wear, but the present invention is not limited to this. For example, markers are attached to the position of each of the user's joints. Then, the distance (m) from the center of rotation of each joint to the center of gravity line is measured using motion capture, and an equation of motion for the entire user's body (upper and lower body) is established. By inputting the user's weight (N) into the equation of motion, it is possible to calculate the joint torque (N m) for each joint of the upper and lower body.

[0055] (Variation 2: Center of Gravity Stabilometer) Alternatively, a force plate, used in balance function tests, can be placed on the floor and the user can perform the exercise on it. The force plate measures the force, and the measurement data can be used to calculate the user's center of gravity. The calculation results are converted into distance over time, and the lateral deviation of the center of gravity (distance, measured in meters) is measured. The length of this distance serves as an index for evaluating the effectiveness of reducing physical workload.

[0056] Although the embodiments of the present invention (including modified examples) have been described above, two or more of these embodiments may be combined and implemented. Alternatively, one of these embodiments may be partially implemented. Furthermore, two or more of these embodiments may be partially combined and implemented.

[0057] Furthermore, the present invention is not limited to the above-described embodiments. Various modifications within the scope of the claims and within the scope of those skilled in the art are also included in the present invention. For example, the trigger switch 220 used is one attached to the electromyographic sensor, but this is not limited to this. For example, a web camera may be used to capture video of the user's movements, and the video data may be sent to a personal computer to extract the movement sections. [Explanation of symbols]

[0058] 100 rating system 210 Measuring Equipment 220 Trigger Switch 230 receiver 300 Information processing device 310 EMG waveform storage unit 320 Trigger data storage section 330 Signal Processing Unit 331 EMG Waveform Shaping Unit 332 Motion section extraction unit 333 PeakRMS extraction part 334 PeakRMS average value extraction section 340 Evaluation result calculation unit

Claims

1. An evaluation system for assist wear that does not use driving force, a measuring device that measures biological data of at least one location on the upper body including the trapezius muscle and at least one location on the lower body of the user both in a state where the assist wear is worn and in a state where the assist wear is not worn, while the user is lifting an object; a calculation device that calculates, based on the biometric data, a load on the user, at least a load on one or more of the rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, or gluteus maximus among the user's trapezius muscle and muscles of the lower body other than the trapezius muscle; an output device that outputs, as comparable information, an increase in the load on the trapezius muscles and a decrease in the load on muscles other than the trapezius muscles that are applied when the user lifts an object, before and after wearing the assist wear; An evaluation system comprising:

2. The evaluation system according to claim 1 , wherein the measuring device is an electromyographic sensor.

3. The evaluation system according to claim 2 , wherein the computing device shapes an output waveform from the electromyographic sensor.

4. The evaluation system according to claim 1 , wherein the computing device extracts movement intervals from the biological data.

5. An assist wear evaluation system, a measuring device that measures biometric data of a user both in a state where the assist wear is worn and in a state where the assist wear is not worn, when the user is lifting a load; a computing device that calculates a load on the user based on the biometric data; an output device that outputs information to be used for evaluating the user's wearing of the assist wear based on the load applied to the user both when the assist wear is worn and when the user is not wearing the assist wear; Equipped with the measuring device attaches a marker to each joint position of the user, and measures the distance from the center of rotation of each joint to the center of gravity line as the biometric data by motion capture; the calculation device establishes an equation of motion for the user based on the distance and calculates a joint torque for each of the joints as a load applied to the user; The output device is an evaluation system that outputs comparable information on the load when the user is wearing the assist wear and the load when the user is not wearing the assist wear.

6. An assist wear evaluation system, a measuring device that measures biometric data of a user both in a state where the assist wear is worn and in a state where the assist wear is not worn, when the user is lifting a load; a computing device that calculates a load on the user based on the biometric data; an output device that outputs information to be used for evaluating the user's wearing of the assist wear based on the load applied to the user both when the assist wear is worn and when the user is not wearing the assist wear; Equipped with the measuring device measures, in time series as the biological data, a distance of deviation of the center of gravity position, which correlates with the amount of muscle activity of the user, using a center of gravity stabilometer; the calculation device calculates the magnitude of the distance of deviation of the center of gravity position in time series as a load applied to the user; The output device is an evaluation system that outputs comparable information on the load when the user is wearing the assist wear and the load when the user is not wearing the assist wear.

7. A method for evaluating assist wear that does not use driving force, measuring biological data of at least one location of the user's upper body including the trapezius muscle and at least one location of the user's lower body both in a state where the assist wear is worn and in a state where the assist wear is not worn, while the user is lifting an object; Based on the biological data, calculate a load applied to at least one of the rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, or gluteus maximus among the user's trapezius muscle and muscles of the lower body other than the trapezius muscle, as a load applied to the user; An evaluation method that outputs, as comparable information, the increase in load on the trapezius muscles and the decrease in load on muscles other than the trapezius muscles when the user lifts an object before and after wearing the assist wear.

8. An evaluation program for assist wear that does not use driving force, measuring biological data of at least one location of the upper body including the trapezius muscle and at least one location of the lower body of the user both in a state where the assist wear is worn and in a state where the assist wear is not worn, while the user is lifting an object; a step of calculating, based on the biological data, a load applied to at least one of the rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and gluteus maximus muscles of the user's lower body other than the trapezius muscle, as a load applied to the user; outputting, as comparable information, an increase in load on the trapezius muscles and a decrease in load on muscles other than the trapezius muscles when the user lifts an object before and after wearing the assist wear; An evaluation program that causes a computer to execute the above.

Images