JP2009129069A - Gripping state estimation system and gripping state estimation method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To estimate a gripping state when directly gripping an object to be gripped at a gripping part regardless of the size, shape and materials of the object. <P>SOLUTION: This gripping state estimation system includes: an object motion detection sensor 11 mounted on an object 2 to be gripped for detecting the motion of the object 2 as an electric signal waveform; a gripping part motion detection sensor 12 mounted on a gripping part 3 of the object 2 for detecting the motion of the gripping part 3 as an electric signal waveform; a waveform comparison part 132 for comparing the output waveforms of the object motion detection sensor 11 and the gripping part motion detection sensor 12; and a gripping state estimation part 133 for estimating the gripping state of the object 2 to be gripped in the gripping part 3 on the basis of the comparison result of the waveform comparison part 132. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a gripping state estimation system and a gripping state estimation method for estimating a gripping state of an object.

In recent years, mobile devices such as notebook personal computers and mobile phones that are carried around by people are widely used. In designing these devices, it is important to estimate the ease with which they can feel. Specifically, human sensitivity is estimated by processing the data using a sensor. As such an estimation method, a method is known in which a gripping state is determined by holding a glove having a pressure sensor or the like on the surface and processing sensor data when an object is gripped (for example, Patent Document 1). This method is suitable for estimating the shape and ease of holding of a device that needs to be grasped relatively firmly, such as a cane or a handle.
JP 2006-92376 A

  However, the above prior art has the following problems.

(1) When there is a relatively small device such as a notebook computer or a mobile phone, it is not necessary to hold the grip as hard as a cane or handle, so it is difficult to measure the pressure distribution. The same applies to a device having a complicated surface shape.

(2) If a glove with a pressure sensor or the like attached to the surface is put in a hand, the human grasping sensation changes significantly and accurate measurement cannot be performed. There are also problems with the resolution of the pressure sensor and the accuracy of the measurement data on the curved surface, and the contact state cannot always be measured accurately.

(3) In gripping an object, the sliding friction on the contact surface between the hand and the object is greatly related to the ease of being felt by humans. However, the conventional method using a glove with a pressure sensor uses a hand with a glove attached to the object. Since the pressure is detected by indirectly gripping and the gripping state of the object is determined based on the pressure distribution, this sliding friction cannot be measured.

  In view of the above-described problems of the prior art, the present invention provides a gripping state estimation system and a gripping state estimation that can estimate a gripping state when a gripping target is directly gripped regardless of the size, shape, and material of the gripping target. It aims to provide a method.

  A gripping state estimation system according to the present invention is attached to a gripping object, attached to a gripping object motion detection sensor that detects a motion of the gripping object as an electric signal waveform, and a gripping execution site of the gripping object, A grasping part movement detection sensor that detects the movement of the grasping part as an electric signal waveform, a waveform comparison unit that compares output waveforms of the grasping object movement detection sensor and the grasping part movement detection sensor, and this waveform comparison And a gripping state estimation unit that estimates a gripping state of the gripping object at the gripping execution site based on a comparison result in the unit.

  The gripping state estimation method according to the present invention includes a gripping object motion detection step in which a gripping object motion detection sensor attached to a gripping object detects a motion of the gripping object as an electric signal waveform, and the gripping object When a gripping execution part motion detection sensor attached to the gripping execution part detects a movement of the gripping execution part as an electric signal waveform and grips the gripping object at the gripping execution part A waveform comparison step for comparing output waveforms of the gripping object motion detection sensor and the gripping execution part motion detection sensor, and a gripping state of the gripping object at the gripping execution part based on a comparison result in the waveform comparison step. A grasping state estimating step of estimating.

  According to the present invention, there are provided a gripping state estimation system and a gripping state estimation method capable of estimating a gripping state when a gripping target is directly gripped regardless of the size, shape, and material of the gripping target.

(Embodiment 1)
Hereinafter, a gripping state estimation system according to Embodiment 1 of the present invention will be described with reference to the drawings. FIG. 1 is a perspective view showing an example of the overall configuration of a gripping state estimation system according to the present embodiment. As shown in FIG. 1A, the gripping state estimation system is a computer system that includes a gripping object motion detection sensor 11, a gripping execution site motion detection sensor 12, and a data processing device 13.

  The gripping object motion detection sensor 11 is a sensor that is attached to the gripping object 2 and detects data during movement of the gripping object 2 (hereinafter referred to as “motion data”) as an electric signal waveform.

  In FIG. 1A, the gripping object motion detection sensor 11 is attached to the surface of the housing that is the gripping object 2. As the gripping object motion detection sensor 11, various sensors such as an angular velocity sensor and an acceleration sensor can be used. In the case of an angular velocity sensor, angular velocity data is acquired, and in the case of an acceleration sensor, acceleration data is acquired as motion data. Theoretically, the angular velocity of the object is the same value regardless of the position of the object, so it is preferable to use an angular velocity sensor. That is, when an angular velocity sensor is used, the sensor mounting position can be set freely, and there is an advantage that actual measurement and data processing are easy. Further, a posture sensor (not shown) is provided inside the grasped object motion detection sensor 11 and outputs an inclination angle with respect to the horizontal direction as posture data.

  The gripping execution part motion detection sensor 12 is a sensor that is attached to a gripping execution part 3 (for example, a human hand) that performs a gripping operation of the gripping object 2 and detects motion data of the gripping execution part 3 as an electric signal waveform. In FIG. 1A, the gripping part movement detection sensor 12 is fixed to the hand that is the gripping part 3 by a band-type sensor fixing member 121. FIG. 1B is a diagram for explaining a main part of FIG. Note that it is preferable to use a sensor of the same type as the gripping object motion detection sensor 11 because subsequent data processing becomes easy.

  The data processing device 13 acquires motion data in the gripping object motion detection sensor 11 and the gripping execution part motion detection sensor 12 wirelessly or by wire, and by analyzing the motion data, the gripping state of the gripping target object 2 in the gripping execution part 3 It is a computer that estimates FIG. 1A shows that motion data is acquired from each sensor by wireless communication.

  FIG. 2 is a block diagram illustrating an example of the overall configuration of the gripping state estimation system 1 according to the present embodiment. Note that the same reference numerals as those in FIG. As shown in the figure, the data processing device 13 includes a detection data conversion unit 131, a waveform comparison unit 132, and a gripping state estimation unit 133.

  The detection data conversion unit 131 simultaneously captures motion data (for example, angular velocity data and acceleration data) detected by the gripping object motion detection sensor 11 and the gripping part motion detection sensor 12 by wireless or wired communication, and a waveform described later. This is a program for aligning unit systems and coordinate systems so that the comparison unit 132 can perform comparison.

  The waveform comparison unit 132 is a program that compares the two waveforms output from the detection data conversion unit 131 and obtains the sliding friction state parameter from the amplitude ratio of the waveform and the elastic contact state parameter from the phase difference of the waveform.

  FIG. 3 is a diagram for explaining the sliding friction state parameter and the elastic contact state parameter. In the figure, the solid line indicates the waveform (waveform 1) of the movement data acquired from the grasping object movement detection sensor 11, and the broken line indicates the waveform (waveform 2) of the movement data acquired from the holding execution part movement detection sensor 12. . Here, the sliding friction state parameter is obtained by cutting a local section from the entire waveform and calculating the amplitude ratio | a / b |. The elastic contact state parameter is obtained by shifting the time of one of the waveforms so that the two waveforms overlap each other and calculating the phase difference c in the state where the two waveforms overlap most.

  The gripping state estimation unit 133 is a program that estimates the gripping state of the gripping target object 2 by the gripping execution site 3 based on the sliding friction state parameter and the elastic contact state parameter output from the waveform comparison unit 132. Substitute into the defined estimation function to estimate the grip state. In this estimation function, when the sliding friction is small, slip occurs between the gripping object 2 and the gripping execution site 3, so that the closer the sliding friction state parameter is to 1, the less slipping is possible and the object can be gripped firmly. Estimated. In addition, when the elastic coefficient at the time of elastic contact is small, the movement of the grasping object 2 is delayed with respect to the movement of the grasping execution part 3, so that the object can be grasped more firmly as the elastic contact state parameter is closer to zero. Estimated.

For example, when the value of the amplitude ratio is the sliding friction state parameter P ₁ and the value of the phase difference is the elastic contact state parameter P ₂ , the estimation function F is R = F (P ₁ , P ₂ ) = AP ₁ + BP ₂ By defining as (A and B are proportional constants), the gripping state R can be obtained as a continuous value. Various formulas are conceivable for the estimation function F, but data on the ease of holding that humans feel when the shape, size, weight, material, etc. of the grasped object 2 are different are statistically calculated. It is preferable to define by analysis.

  FIG. 4 is a diagram illustrating the relationship between the gripping state and the waveform. In the figure, the solid line indicates the motion data acquired from the gripping object motion detection sensor 11, and the broken line indicates the waveform of the motion data acquired from the gripping part motion detection sensor 12. In the case of FIG. 4A, both the sliding friction state parameter and the elastic contact state parameter output from the waveform comparison unit 132 are small. In this case, the gripping state estimation unit 133 estimates that the gripping state is stable and easy to hold. On the other hand, in the case of FIG. 4B, both the sliding friction state parameter and the elastic contact state parameter output from the waveform comparison unit 132 are large. In this case, the gripping state estimation unit 133 estimates that the gripping state is unstable and difficult to hold.

  FIG. 5 is a flowchart illustrating a specific example of processing in the detection data conversion unit 131.

  In S501, motion data (angular velocity data, acceleration data, etc.), unit system data, and posture data are acquired from the grasped object motion detection sensor 11. The unit system data is unit information of motion data, and therefore differs depending on the type of sensor.

  In S <b> 502, motion data, unit system data, and posture data are acquired from the gripping part motion detection sensor 12.

  In S503, the posture data acquired from both sensors is compared, and it is determined whether or not the postures of the gripping object motion detection sensor 11 and the gripping execution region motion detection sensor 12 are different from each other in the horizontal direction. If it is determined that the postures of the two sensors are different, the process proceeds to S504. On the other hand, if it is determined that the postures of both sensors match, the process proceeds to S505.

  In S504, the coordinate system of the motion data of the gripping part motion detection sensor 12 is converted with reference to the coordinate system of the motion data of the gripping object motion detection sensor 11, and the process proceeds to S505.

  In S505, the unit system data acquired from both sensors are compared, and it is determined whether or not the units of motion data of the grasping object motion detection sensor 11 and the gripping execution region motion detection sensor 12 are different. If it is determined that the unit of the exercise data is different, the process proceeds to S506. On the other hand, if it is determined that the units of the exercise data match, the process proceeds to S507.

  In S506, the unit of motion data of the gripping part motion detection sensor 12 is converted into the unit of motion data of the gripping object motion detection sensor 11, and the process proceeds to S507.

  In S507, the exercise data converted into the waveform is output to the waveform comparison unit 132, and the process ends.

  FIG. 6 is a flowchart illustrating a specific example of processing in the waveform comparison unit 132.

  In S601, the motion data converted into a waveform is acquired from the detection data conversion unit 131.

  In S602, an arbitrary section is extracted from the entire waveform.

  In S603, the sliding friction state parameter is obtained from the amplitude ratio of the two waveforms.

  In S604, the time of one of the waveforms is shifted so that the two waveforms overlap, and the phase difference in the state where the two waveforms overlap most is calculated to obtain the elastic contact state parameter.

  In step S605, each parameter obtained by comparing the waveforms is output to the gripping state estimation unit 133, and the process ends. Each parameter may be obtained from an average value in a plurality of sections of the waveform.

  FIG. 7 is a flowchart illustrating a specific example of processing in the gripping state estimation unit 133.

  In step S <b> 701, the sliding friction state parameter and the elastic contact state parameter are acquired from the waveform comparison unit 132.

  In step S702, the gripping state is estimated by substituting each acquired parameter for a predefined estimation function.

  In S703, the estimated gripping state is output to a display device (not shown) or a database (not shown), and the process ends.

  As described above, by comparing the waveforms of the motion data detected by the sensors, the gripping state when the gripping target object 2 is directly gripped by the gripping execution part 3, that is, the gripping target object 2 and the gripping execution part 3. The degree of integration during exercise can be estimated. Then, by using this gripping state estimation system 1 when designing a product, the optimum shape, size, material, and the like of the product can be determined.

(Embodiment 2)
Hereinafter, a gripping state estimation system 1 according to Embodiment 2 of the present invention will be described with reference to the drawings. As described in the first embodiment, in order to compare the measurement waveforms of the gripping object motion detection sensor 11 and the gripping execution site motion detection sensor 12, it is necessary to align the coordinate system and the unit system. As a method for aligning unit systems, there is a method for aligning by using the same type of sensor as that for unit conversion, and any method may be used. On the other hand, as a method of aligning the coordinate system, there are a method of using the posture data detected by the posture sensor and a method of aligning mechanically, but in this embodiment, the posture execution sensor is not used and the grasping execution part motion detection sensor 12 is used. Are mechanically aligned by the sensor direction variable member 122 on the basis of the posture of the gripping object motion detection sensor 11.

  FIG. 8 is a perspective view showing an overall configuration example of the gripping state estimation system 1 according to Embodiment 2 of the present invention. Note that the same reference numerals as those in FIG.

  As shown in FIG. 8A, the grasping object motion detection sensor 11 is attached to the grasping object 2, and on the band-type sensor fixing member 121 attached to the hand which is the grasping execution part 3, A gripping execution site motion detection sensor 12 is attached via a sensor direction variable member 122. FIG. 8B is a diagram for explaining a main part of FIG. 8A, and a ball joint is used for the sensor direction variable member 122. The sensor direction variable member 122 may be another member as long as it has a mechanism that can arbitrarily change the posture of the sensor. When the posture of the sensor at the beginning of gripping is not appropriate, the user manually adjusts the posture of the gripping portion movement detection sensor 12 with reference to the posture of the gripping object motion detection sensor 11 to mechanically change the coordinate system. Can be aligned.

  With this configuration, since it is not necessary to provide a posture sensor (not shown) in the gripping execution part motion detection sensor 12, it is possible to reduce the size of the sensor compared to the first embodiment, and in the data processing device 13 Processing load can also be reduced.

(Embodiment 3)
Hereinafter, a gripping state estimation system 1 according to Embodiment 3 of the present invention will be described with reference to the drawings. FIG. 9 is a perspective view showing an example of the overall configuration of the gripping state estimation system 1 according to Embodiment 3 of the present invention. Note that the same reference numerals as those in FIG. 1 indicate the same items, and the processing in the data processing device 13 is the same, and the description thereof is omitted.

  As shown in FIG. 9A, in the present embodiment, with respect to one gripping object motion detection sensor 11, each finger whose gripping execution part motion detection sensor 12 is the gripping execution part 3 has a ring shape. The sensor fixing member 123 is attached. FIG. 9B is a diagram for explaining a main part of FIG.

  With this configuration, it is possible to estimate the gripping state for each finger. That is, it is possible to confirm which fingers are easy to hold when gripping and which fingers are difficult to hold. As a result, it is possible to improve the portion of the finger that is difficult to hold and to easily hold the finger.

(Embodiment 4)
Hereinafter, a gripping state estimation system 1 according to Embodiment 4 of the present invention will be described with reference to the drawings. In the present embodiment, considering that there is a case where it is difficult to produce a difference in waveform when the waveform is measured with the gripping object 2 held normally, a physical force (disturbance) is intentionally applied to the gripping object 2. ) And the grasping state is estimated by analyzing the waveform at this time. Specifically, a time from when a disturbance is applied in the waveform comparison unit 132 until the vibration is attenuated and the waveform is stabilized (hereinafter referred to as “attenuation time”) is obtained, and this attenuation time is determined as a sliding friction state parameter and Along with the elastic contact state parameter, it is used as an estimation parameter in the gripping state estimation unit 133.

  FIG. 10 is a diagram illustrating a specific example of a disturbance generation method. Note that the same reference numerals as those in FIG. In FIG. 10A, the vibration device 14 that itself vibrates is attached to the grasped object 2, and the grasping state is estimated by comparing the difference in waveform when the vibration is applied. . On the other hand, FIG. 10B shows a case where a physical force is applied to the grasped object 2 using the impact acting device 15 to estimate the grasping state. The arrows in the figure indicate the force applied to the object and its direction.

FIG. 11 is a diagram for explaining the relationship between the waveform and the decay time. FIG. 11A is a diagram illustrating a waveform when the gripping state of the gripping object 2 is stable and easy to hold. In the figure, the decay time is represented by T _1. On the other hand, FIG. 11B is a diagram illustrating a waveform when the gripping state of the gripping object 2 is unstable and difficult to hold. In the figure, the decay time is represented by T _2. Comparing FIGS. 11A and 11B, the decay time is longer when it is difficult to hold the object 2 to be grasped. That is, the physical stability margin is small.

  In this way, the gripping state estimation unit 133 can estimate the gripping state in more detail by adding the decay time to the parameter of the gripping state estimation function.

(Embodiment 5)
Hereinafter, a gripping state estimation system 1 according to Embodiment 5 of the present invention will be described with reference to the drawings. FIG. 12 is a block diagram showing an example of the overall configuration of the gripping state estimation system 1 according to Embodiment 5 of the present invention. The gripping state estimation system 1 according to the present embodiment is a computer system in which a load measuring device 16, a fatigue level estimation unit 134, a database unit 135, and an ease-of-holding estimation unit 136 are added to the system of the first embodiment. is there. Reference numerals common to FIGS. 1 and 2 indicate the same thing, and the mounting method of the gripping execution site motion detection sensor 12 uses any of the above-described embodiments, and thus the description thereof is omitted.

  The load measuring device 16 is a sensor that is attached to a part of the body that is the grasping execution site 3 and measures body data during exercise. For example, a myoelectric sensor, a heart rate measuring device, and the like can be cited. Here, the description will be made assuming that a myoelectric sensor is used.

  The fatigue level estimation unit 134 is a program that acquires body data measured by the load measuring device 16 and estimates the fatigue level based on the body data. Specifically, the muscle activity data measured by the myoelectric sensor is converted into a waveform, and the integrated value for a certain period of the waveform is calculated to estimate the degree of fatigue in the gripping operation. That is, it is estimated that the smaller the degree of fatigue, the easier it is to hold. Note that the degree of fatigue may be estimated using a pre-defined estimation function as in the first embodiment.

  The database unit 135 is a database that stores in advance the relationship between the gripping state and the estimated fatigue level and the ease of holding the gripping object 2. FIG. 13 is a diagram illustrating a specific example of the database unit 135. A1 to A4 and B1 to B4 indicate the levels of the estimated values obtained by the gripping state estimation unit 133 and the fatigue level estimation unit 134, respectively, and the level of ease of gripping when a human grips the gripping object 2 Are associated with C1 to C4.

  The ease-of-holding estimation unit 136 refers to the database unit 135 based on the fatigue level obtained by the fatigue level estimation unit 134 and the gripping state obtained by the gripping state estimation unit 133, and a person grips the grasped object 2. It is a program that estimates the ease of holding. Note that the relationship between the estimated gripping state, the degree of fatigue, and the ease of holding is attribute data (a shape, a material, a shape, a material, a gripping object 2) input from an input device (not shown) connected to the data processing device 13. It is preferable that the estimation result can be used effectively if it is stored in the database unit 135 in association with the size, weight, etc.

  FIG. 14 is a flowchart illustrating a specific example of processing in the fatigue level estimation unit 134.

  In S1401, load measurement data is acquired from the load measurement device 16.

  In S1402, an integrated value for a certain period of the waveform of the acquired load measurement data is calculated, and the degree of fatigue is estimated from this integrated value.

  In step S1403, the obtained fatigue level is output to the ease estimation unit 136, and the process ends.

  FIG. 15 is a flowchart illustrating a specific example of processing in the ease-of-hold estimation unit 136.

  In step S <b> 1501, the gripping state estimation value is acquired from the gripping state estimation unit 133.

  In S1502, an estimated value of the fatigue level is acquired from the fatigue level estimation unit 134.

  In S1503, the database unit 135 is referred to based on the acquired gripping state and the estimated fatigue level, and the ease of holding the gripping object 2 is estimated.

  In S1504, the estimated value of the ease of holding the grasped object 2 is output to the display device (not shown) or the database unit 135, and the process is terminated.

  In this way, since the ease of holding that a person feels when gripping is estimated using the degree of fatigue in addition to the gripping state, the ease of holding the gripping object 2 can be estimated in more detail.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. In addition, various inventions can be formed by appropriately combining a plurality of components disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

  In the above embodiment, the gripping execution site 3 is a human hand, but it may be a part of a machine such as an arm part of an industrial robot. In the case of a part of the machine, it is possible to estimate the gripping performance of the gripping execution site 3. It can also be applied to the evaluation of products worn on other parts of the body such as feet and heads.

  It is also possible to operate various devices by recognizing changes in the gripping state as operation information. FIG. 16 is a diagram illustrating a specific example of an apparatus that operates in accordance with the gripping state obtained by the gripping state estimation system 1. Here, the motor 21, the link 22, and the weight 23 are provided inside the grasped object 2, and the weight 23 is fixed to the link 22 tip connected to the motor 21, and the estimation result in the grasping state estimation unit 133 Based on the above, the operation of the motor 21 is controlled. When the motor 21 is driven, the weight 23 rotates, and acceleration can be generated for the grasped object 2 or the center of gravity of the grasped object 2 can be changed. Then, if the gripping state estimated again by the gripping state estimation unit 133 shows an improvement tendency, the rotation direction is maintained, and if the gripping state shows a deterioration tendency, feedback control is performed to rotate in the reverse direction. Thereby, the ease of holding can be dynamically changed on the grasped object 2 side.

It is a perspective view which shows the example of whole structure of the holding | grip state estimation system which concerns on Embodiment 1 of this invention. The block diagram which shows the example of whole structure of the holding | grip state estimation system which concerns on Embodiment 1 of this invention. The figure explaining the sliding friction state parameter and elastic contact state parameter which concern on Embodiment 1 of this invention. The figure explaining the relationship between the holding | grip state and waveform which concern on Embodiment 1 of this invention. 5 is a flowchart illustrating a specific example of processing in a detection data conversion unit according to the first embodiment of the present invention. 5 is a flowchart illustrating a specific example of processing in a waveform comparison unit according to the first embodiment of the present invention. The flowchart which shows the specific example of the process in the holding | grip state estimation part which concerns on Embodiment 1 of this invention. It is a perspective view which shows the example of whole structure of the holding | grip state estimation system which concerns on Embodiment 2 of this invention. It is a perspective view which shows the example of whole structure of the holding | grip state estimation system which concerns on Embodiment 3 of this invention. The figure which shows the specific example of the generation method of the disturbance which concerns on Embodiment 3 of this invention. The figure explaining the relationship between the waveform which concerns on Embodiment 4 of this invention, and decay time. The block diagram which shows the example of whole structure of the holding | grip state estimation system which concerns on Embodiment 5 of this invention. The figure which shows the specific example of the database part which concerns on Embodiment 5 of this invention. The flowchart which shows the specific example of the process in the fatigue estimation part which concerns on Embodiment 5 of this invention. The flowchart which shows the specific example of the process in the ease estimation part which concerns on Embodiment 5 of this invention. The figure which shows the specific example of the apparatus which operate | moves according to the holding state calculated | required with the holding state estimation system which concerns on this invention.

Explanation of symbols

1 ... gripping state estimation system,
2 ... gripping object,
3 ... gripping execution part,
11: Grasping object motion detection sensor,
12 ... Gripping execution part motion detection sensor,
13: Data processing device,
14 ... Exciting device,
15 ... impact working device,
16 ... load measuring device,
21 ... Motor,
22 ... Link,
23 ... weight,
121 ... Band-type sensor fixing member,
122 ... Sensor direction variable member,
123 ... Ring-type sensor fixing member,
131... Detection data conversion unit,
132 ... waveform comparison unit,
133 ... gripping state estimation unit,
134 ... Fatigue degree estimation part,
135 ... Database section,
136: Ease of use estimation unit.

Claims (10)

A gripping object motion detection sensor which is attached to the gripping object and detects the motion of the gripping object as an electric signal waveform;
A grasping part movement detection sensor that is attached to a grasping part of the grasping object and detects the movement of the grasping part as an electric signal waveform;
A waveform comparison unit that compares output waveforms of the gripping object motion detection sensor and the gripping execution site motion detection sensor;
A gripping state estimation unit that estimates a gripping state of the gripping object in the gripping execution site based on a comparison result in the waveform comparison unit;
A gripping state estimation system characterized by comprising:   The gripping state estimation system according to claim 1, wherein the waveform comparison unit estimates a sliding friction state from the amplitude ratio of the waveform and an elastic contact state from the phase difference of the waveform.   The gripping state estimation system according to claim 1 or 2, wherein the gripping object motion detection sensor and the gripping execution site motion detection sensor are angular velocity sensors.   The grip execution part motion detection sensor is attached to the grip execution part via a sensor direction variable member, and the direction can be changed according to the posture of the grip target object motion detection sensor by the sensor direction variable member. The grasping state estimation system according to any one of claims 1 to 3.   The gripping part movement detection sensor is attached to each of a plurality of gripping part parts, and the gripping state is estimated for each of the gripping part parts. State estimation system.   Ease of use estimation unit that estimates the easiness of holding of the gripping object based on the gripping state estimated by the gripping state estimation unit and the degree of fatigue measured when the gripping target object is gripped at the gripping execution site. The gripping state estimation system according to claim 1, further comprising: A database unit for storing in advance the relationship between the gripping state, the degree of fatigue, and the ease of holding;
The gripping state estimation system according to claim 6, wherein the ease of holding estimation unit estimates the ease of holding with reference to the database unit based on the estimated gripping state and fatigue level. A gripping object motion detection step in which a gripping object motion detection sensor attached to the gripping object detects the motion of the gripping object as an electric signal waveform; and
A gripping execution part motion detection sensor, which is attached to a gripping execution part of the gripping object, detects a motion of the gripping execution part as an electric signal waveform; and
A waveform comparison step of comparing output waveforms of the gripping object motion detection sensor and the gripping performance part motion detection sensor when the gripping target part is gripped by the gripping part;
A gripping state estimation step for estimating a gripping state of the gripping object at the gripping execution site based on a comparison result in the waveform comparison step;
A gripping state estimation method characterized by comprising:   9. The gripping state estimation method according to claim 8, wherein in the waveform comparison step, a sliding friction state is estimated from an amplitude ratio of the waveform, and an elastic contact state is estimated from a phase difference of the waveform.   The gripping state estimation method according to claim 8 or 9, wherein the gripping object motion detection sensor and the gripping execution site motion detection sensor are angular velocity sensors.

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