Biological information measurement device, biological information measurement method, program, and interface method

Abstract

This biological information measurement device includes: a plurality of sensors capable of measuring a biological signal of a user by coming into contact with the face of the user; a biasing member for bringing at least two sensors of the plurality of sensors into contact with the face of the user; and a measurement unit for measuring biological information of the user on the basis of at least two biological signals acquired from the at least two sensors.

Description

Biological information measuring device, biological information measuring method, program, and interface method

[0001] The present disclosure relates to a biological information measuring device, a biological information measuring method, a program, and an interface method.

[0002] Patent Document 1 discloses a myoelectric device according to the background art, which includes a stretchable base material that is wrapped around a user's upper arm and a plurality of electrodes exposed on the inner surface of the base material, and measures the myoelectric potential of the biceps brachii or the like based on the potential difference between the electrodes.

[0003] The myoelectric device disclosed in Patent Document 1 does not consider acquiring biosignals from the user's face.

[0004] Patent No. 7393003

[0005] The present disclosure aims to provide a biometric information measuring device, a biometric information measuring method, a program, and an interface method that can acquire biometric signals from a user's face with high accuracy, thereby measuring the user's biometric information with high accuracy.

[0006] A biometric measuring device according to one aspect of the present disclosure includes a plurality of sensors each capable of measuring a user's biometric signal by contacting the user's face, a biasing member that brings at least two of the plurality of sensors into contact with the user's face, and a measuring unit that measures the user's biometric information based on at least two biometric signals obtained from the at least two sensors.

[0007] 1 is a side view showing a simplified configuration example of VR goggles; FIG. 2 is a diagram showing a schematic configuration example of a measurement unit; FIG. 3 is a diagram showing a schematic configuration example of a measurement unit whose base material is deformed by contact with a user's face; FIG. 4 is a diagram showing a simplified configuration of a control device provided in VR goggles; FIG. 5 is a flowchart showing the content of processing executed by a processing unit; FIG. 6 is a flowchart showing details of steps; FIG. 7 is a diagram showing details of steps; FIG. 8 is a diagram showing a schematic configuration example of a measurement unit; FIG. 9 is a diagram showing a schematic configuration example of a measurement unit; FIG. 10 is a diagram showing a schematic configuration example of a measurement unit; FIG. 11 is a diagram showing a schematic configuration example of a measurement unit; FIG. 12 is a diagram showing a schematic configuration example of a measurement unit; FIG. 13 is a diagram showing a schematic configuration example of a measurement unit; FIG. 14 is a side view showing a simplified configuration example of VR goggles;

[0008] (Foundation of the Present Disclosure) Glasses-type devices such as VR (Virtual Reality) goggles or AR (Augmented Reality) glasses are becoming increasingly popular.

[0009] 2. Description of the Related Art Research is underway into technology for estimating a user's operational intention using biometric information such as electromyography, in order to enable hands-free operations such as object selection in eyeglass-type devices.

[0010] The present inventors have found that the accuracy of estimating a user's operational intention can be improved by measuring the electromyogram of specific muscles in the user's face, such as the procerus muscle.

[0011] However, the contours of each user's face vary. Furthermore, the eyeglasses can slip off as the user moves their face. Furthermore, to reduce the effects of noise and improve measurement accuracy, it is necessary to acquire at least two biosignals for each specific muscle. For these reasons, it has not been easy to measure the myoelectric potential of a specific muscle with high accuracy.

[0012] In order to solve this problem, the inventor discovered that by implementing multiple sensors to provide redundancy in the measuring unit and pressing the measuring unit against the user's face using a pressing member, it is possible to obtain at least two biological signals with high accuracy from at least two of the multiple sensors that are in close contact with the user's face, thereby making it possible to measure the user's biological information with high accuracy, and this led to the present disclosure.

[0013] Next, each aspect of the present disclosure will be described.

[0014] A biometric measuring device according to a first aspect of the present disclosure includes a plurality of sensors each capable of measuring a user's biometric signal by contacting the user's face, a biasing member for contacting at least two of the plurality of sensors with the user's face, and a measuring unit for measuring the user's biometric information based on at least two biometric signals obtained from the at least two sensors.

[0015] According to the first aspect, at least two biometric signals can be obtained with high accuracy from at least two sensors among the multiple sensors that are energized and placed in close contact with the user's face, thereby making it possible to measure the user's biometric information with high accuracy.

[0016] The biological information measuring device according to a second aspect of the present disclosure is the first aspect, and may further include a selection unit that selects the at least two sensors from the plurality of sensors.

[0017] According to the second aspect, at least two highly accurate biosignals can be selected from a plurality of output signals output by a plurality of sensors, thereby making it possible to measure the bioinformation of the user with high accuracy.

[0018] A biological information measuring device according to a third aspect of the present disclosure is the second aspect, wherein the selector selects the at least two sensors based on the impedance of each of the plurality of sensors.

[0019] According to the third aspect, at least two sensors can be appropriately selected from the plurality of sensors.

[0020] In the bioinformation measuring device according to the fourth aspect of the present disclosure, in the second aspect, the selection unit may select the at least two sensors based on the correlation between multiple output signals output by the multiple sensors.

[0021] According to the fourth aspect, at least two sensors can be appropriately selected from the plurality of sensors.

[0022] In the bioinformation measuring device of the fifth aspect of the present disclosure, in any one of the second to fourth aspects, the selection unit may dynamically change at least one of the positions and the number of the at least two sensors selected from the plurality of sensors.

[0023] According to the fifth aspect, even if the positions of the plurality of sensors are displaced in accordance with the movement of the user's face, at least two sensors can be appropriately selected from the plurality of sensors.

[0024] In the bioinformation measuring device according to the sixth aspect of the present disclosure, in any one of the first to fifth aspects, the biasing member may bring the at least two sensors into contact with the user's face by applying a biasing force in a direction that brings at least a portion of the curved surface on which the multiple sensors are arranged into close contact with the user's face.

[0025] According to the sixth aspect, at least two of the plurality of sensors can be reliably brought into close contact with the user's face.

[0026] In the bioinformation measuring device according to the seventh aspect of the present disclosure, in the sixth aspect, the biasing member may have a flexible base material that forms the curved surface, and the biasing force may be generated by the restoring force of the base material that deforms upon contact with the user's face.

[0027] According to the seventh aspect, at least two of the plurality of sensors can be reliably brought into close contact with the user's face by the restoring force of the base material that is deformed by contact with the user's face.

[0028] In the bioinformation measuring device according to the eighth aspect of the present disclosure, in the sixth aspect, the biasing member has a plastic bag body that forms the curved surface, and the biasing force is generated by the pressure of a fluid sealed inside the bag body.

[0029] According to the eighth aspect, at least two of the plurality of sensors can be reliably brought into close contact with the user's face by the pressure of the fluid sealed inside the bag.

[0030] In the bioinformation measuring device according to the ninth aspect of the present disclosure, in the sixth aspect, the biasing member may have a spring connected to the back surface of the curved surface, and the biasing force may be generated by the elastic force of the spring, which deforms upon contact with the user's face.

[0031] According to the ninth aspect, at least two of the plurality of sensors can be reliably brought into close contact with the user's face by the elastic force of the spring that is deformed by contact with the user's face.

[0032] In the bioinformation measuring device according to the tenth aspect of the present disclosure, in the sixth aspect, the biasing member has a repulsive material that forms the curved surface, and the biasing force is generated by the repulsive force of the repulsive material that deforms upon contact with the user's face.

[0033] According to the tenth aspect, at least two of the multiple sensors can be reliably attached to the user's face by the repulsive force of the repulsive material that deforms upon contact with the user's face.

[0034] In the bioinformation measuring device according to the eleventh aspect of the present disclosure, in the sixth aspect, the biasing member has a shape memory material that forms the curved surface, and the biasing force is generated by the shape recovery force of the shape memory material due to the user's body temperature upon contact with the user's face.

[0035] According to the eleventh aspect, at least two of the multiple sensors can be reliably attached to the user's face by the shape recovery force of the shape memory material due to the user's body temperature when it comes into contact with the user's face.

[0036] A biological information measuring device according to a twelfth aspect of the present disclosure is any one of the first to eleventh aspects, wherein the biological signal preferably includes a myoelectric signal.

[0037] According to the twelfth aspect, by measuring myoelectricity, it is possible to estimate the user's operational intention using the myoelectricity.

[0038] A biological information measuring device according to a thirteenth aspect of the present disclosure is the twelfth aspect, wherein the plurality of sensors are preferably arranged along the running direction of muscle fibers of the muscle to be measured.

[0039] According to the thirteenth aspect, by arranging a plurality of sensors along the direction in which the muscle fibers of the muscle to be measured run, the influence of noise can be reduced and the accuracy of measuring myoelectric potential can be improved.

[0040] A biological information measuring device according to a fourteenth aspect of the present disclosure is the thirteenth aspect, wherein the muscles to be measured may include the procerus muscle.

[0041] According to the fourteenth aspect, by measuring the electromyogram of the procerus muscle, it is possible to improve the accuracy of estimating the user's operational intention.

[0042] A biological information measuring device according to a fifteenth aspect of the present disclosure is any one of the first to fourteenth aspects, wherein the biological signal preferably includes a pulse wave.

[0043] According to the fifteenth aspect, by measuring the pulse wave, it becomes possible to estimate the user's operational intention using the pulse wave.

[0044] A biological information measuring device according to a sixteenth aspect of the present disclosure is the fifteenth aspect, wherein the plurality of sensors are preferably arranged along the running direction of the blood vessel to be measured.

[0045] According to the sixteenth aspect, by arranging a plurality of sensors along the direction in which the blood vessel to be measured runs, the influence of noise can be reduced and the accuracy of measuring the pulse wave can be improved.

[0046] In the bio-information measuring device according to the seventeenth aspect of the present disclosure, in any one of the first to sixteenth aspects, the bio-signal may include at least one of electrooculography, electroencephalography, changes in muscle shape, changes in muscle hardness, sounds generated by the living body, skin temperature, skin conductivity, respiration, sweating, heart rate, and blood pressure.

[0047] According to the seventeenth aspect, a desired biological signal can be measured with high accuracy.

[0048] A bioinformation measurement method according to an 18th aspect of the present disclosure is a bioinformation measurement method using a bioinformation measurement device that includes a plurality of sensors that can each measure the user's biosignals by contacting the user's face, and a biasing member that brings at least two of the plurality of sensors into contact with the user's face, wherein the bioinformation measurement device acquires at least two biosignals from the at least two sensors, and measures the user's bioinformation based on the acquired at least two biosignals.

[0049] According to the 18th aspect, at least two biometric signals can be obtained with high accuracy from at least two sensors among the multiple sensors that are energized and placed in close contact with the user's face, thereby making it possible to measure the user's biometric information with high accuracy.

[0050] A program relating to a 19th aspect of the present disclosure is a program for causing an information processing device mounted on a bioinformation measuring device that has a plurality of sensors that can each measure the user's biosignals by contacting the user's face, and a biasing member that brings at least two of the plurality of sensors into contact with the user's face, to execute processing, and by executing the program, the information processing device acquires at least two biosignals from the at least two sensors, and measures the user's bioinformation based on the acquired at least two biosignals.

[0051] According to the 19th aspect, at least two biometric signals can be obtained with high accuracy from at least two sensors among the multiple sensors that are energized and placed in close contact with the user's face, thereby making it possible to measure the user's biometric information with high accuracy.

[0052] An interface method according to a twentieth aspect of the present disclosure uses the biological information measuring device according to any one of the first to seventeenth aspects to operate equipment based on at least the biological information measured by the measuring unit.

[0053] An interface method according to a 21st aspect of the present disclosure is the 20th aspect, further comprising detecting the user's line of sight, and operating the device based on the line of sight and the biometric information.

[0054] An interface method according to a 22nd aspect of the present disclosure is, in the 21st aspect, such that, in detecting the gaze, an operation target is selected based on the gaze, and, in operating the device, an operation on the operation target is performed based on the biometric information.

[0055] The present disclosure can also be realized as a program that causes a computer to execute each characteristic configuration included in such a method or apparatus, or as a system operated by this program. Needless to say, such a computer program can be distributed on a computer-readable non-transitory recording medium such as a CD-ROM or via a communication network such as the Internet.

[0056] (Embodiments of the Present Disclosure) Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Elements with the same reference numerals in different drawings indicate the same or corresponding elements. Furthermore, the components, the arrangement positions of the components, the connection forms, the order of operations, etc. shown in the following embodiments are merely examples and are not intended to limit the present disclosure. The present disclosure is limited only by the claims. Therefore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concept of the present disclosure are not necessarily required to achieve the objectives of the present disclosure, but are described as constituting more preferred forms.

[0057] In this embodiment, an example in which VR goggles are used as an application target of the present disclosure will be described, but the application target of the present disclosure is not limited to this. The present disclosure is widely applicable to eyeglass-type devices worn by a user that use cross-reality technology such as VR, AR, or MR. In addition, the present disclosure is widely applicable to eyeglass-type devices in general that acquire biosignals, even if they do not use cross-reality technology.

[0058] FIG. 1 is a simplified side view illustrating an example configuration of VR goggles 1 according to an embodiment of the present disclosure in a state where the VR goggles 1 are worn on the face of a user 2.

[0059] The VR goggles 1 are worn on the head of the user 2 using a head strap 12. The head strap 12 is connected to both left and right ends of the frame 11 and supports the sides and back of the user 2's head.

[0060] The VR goggles 1 include a measurement unit 14 for measuring biosignals of the user 2. The measurement unit 14 is supported by a support member 13 disposed inside the frame 11. In the example of this embodiment, the measurement unit 14 is disposed inside the frame 11 at a position that contacts the space between the eyebrows of the user 2 and between both eyes.

[0061] FIG. 2 is a diagram showing a schematic configuration example of the measurement unit 14. (A) shows a side view, and (B) shows a front view. The measurement unit 14 measures the biosignal of the measurement target. In this embodiment, the measurement target is the procerus muscle, and the biosignal is myoelectric potential. However, this is not limited to this example. The direction in which the muscle fibers of the procerus muscle run is the up-down direction. The direction connecting the forehead and chin is defined as the up-down direction. The direction connecting both eyes is defined as the left-right direction, and the direction perpendicular to both the up-down direction and the left-right direction is defined as the front-back direction.

[0062] The measurement unit 14 includes a plurality of sensors SE and a biasing member 21. In the following example, the measurement unit 14 includes seven sensors SE1 to SE7, but the number of sensors SE is not limited to seven and may be any number.

[0063] Each of the sensors SE1 to SE7 includes the same electrodes, and can measure biosignals of the user 2 by contacting the electrodes with the face of the user 2. The sensors SE1 to SE7 are arranged in a line at equal intervals on the curved surface along the direction of the muscle fibers of the muscle to be measured (in this example, the up-and-down direction). The multiple sensors SE1 to SE7 may be arranged in a serpentine pattern, at uneven intervals, or more densely in areas more likely to come into contact with the face of the user 2.

[0064] The biasing member 21 brings at least two of the sensors SE1 to SE7 into contact with the face of the user 2. The biasing member 21 brings at least two of the sensors SE into contact with the face of the user 2 by applying a biasing force in a direction in which at least a part of the curved surface on which the sensors SE1 to SE7 are arranged is brought into close contact with the face of the user 2.

[0065] The biasing member 21 has a flexible base material 21A that forms a curved surface. The base material 21A has a substantially C-shaped cross section. This allows the convex shape of the base material 21A to correspond to the concave shape between the eyebrows. The sensors SE1 to SE7 are disposed on the surface of the base material 21A. The biasing member 21 generates a biasing force due to the restoring force of the base material 21A, which is deformed by contact with the face of the user 2.

[0066] 3 is a diagram schematically illustrating the measuring unit 14 in which the base material 21A is deformed by contact with the face of the user 2. When the user 2 puts on the VR goggles 1, the base material 21A comes into contact with the space between the eyebrows of the user 2, and the base material 21A is sandwiched between the support member 13 and the space between the eyebrows from the front and back, causing the base material 21A to be deformed as if it were crushed in the front-to-back direction.

[0067] As indicated by arrow X, biasing member 21 generates a biasing force due to the restoring force of base material 21A, which is deformed in the front-to-back direction due to contact between base material 21A and the space between the eyebrows of user 2. This biasing force causes at least two sensors SE of sensors SE1 to SE7 to come into close contact with the user's face.

[0068] FIG. 4 is a simplified diagram showing the configuration of the control device 30 included in the VR goggles 1. The control device 30 includes a processing unit 31 as an information processing device and a storage unit 32. The processing unit 31 includes a processor such as a CPU. The storage unit 32 includes a semiconductor memory or the like. The storage unit 32 includes a computer-readable non-volatile storage medium, which stores a program 51 for causing the processing unit 31 to execute processing. The processing unit 31 includes an acquisition unit 41, a selection unit 42, a measurement unit 43, and an execution unit 44 as functions realized by the processor executing the program 51 read from the storage unit 32. The content of the processing executed by each of these processing units will be described later. Each of these processing units may also be configured using dedicated hardware circuits such as an ASIC or FPGA.

[0069] FIG. 5 is a flowchart showing the process executed by the processing unit 31.

[0070] First, in step ST1, the acquisition unit 41 acquires the output signals output from the sensors SE1 to SE7 from the sensors SE1 to SE7. The acquisition unit 41 inputs the output signals acquired from the sensors SE1 to SE7 to the selection unit .

[0071] Next, in step ST2, the selection unit 42 selects at least two sensors SE from the sensors SE1 to SE7 that are in close contact with the brows of the user 2. The output signal from the sensor SE that is in close contact with the brows of the user 2 can be evaluated as a biosignal of the user 2, and the output signal from the sensor SE that is not in close contact with the brows of the user 2 can be evaluated as not being a biosignal of the user 2. In this way, the selection unit 42 selects at least two biosignals from the output signals acquired from the sensors SE1 to SE7.

[0072] FIG. 6 is a flowchart showing the details of step ST2.

[0073] First, in step ST21A, the selector 42 measures the impedance of each of the sensors SE1 to SE7. When measuring the impedance, the potential of the forehead of the user 2, for example, is used as a reference potential.

[0074] Next, in step ST22, the selection unit 42 selects at least two sensors SE that are in close contact with the user 2's eyebrows based on the impedance measurement results. The impedance of a sensor SE that is in close contact with the user 2's skin is several ohms to several tens of ohms, whereas the impedance of a sensor SE that is not in close contact with the user 2's skin is several hundred ohms or more. If the measured impedance is less than a predetermined threshold, the selection unit 42 determines that the sensor SE is in close contact with the user 2's eyebrows. If the measured impedance is equal to or greater than the predetermined threshold, the selection unit 42 determines that the sensor SE is not in close contact with the user 2's eyebrows. The selection unit 42 selects at least two adjacent sensors SE and inputs at least two output signals acquired from the selected at least two sensors SE to the measurement unit 43 as at least two biosignals. For example, the selection unit 42 selects the sensor SE with the smallest impedance and one or two sensors SE adjacent to it as the at least two sensors SE. However, this example is not limiting.

[0075] Referring to FIG. 5 , in step ST3, the measurement unit 43 measures the biometric information of the user 2 based on the at least two biometric signals input from the selection unit 42. For example, the measurement unit 43 calculates a differential signal between two biometric signals corresponding to two adjacent sensors SE, and measures the differential signal as the biometric information of the user 2. By taking the difference between the two adjacent biometric signals, the influence of noise commonly mixed into the two biometric signals can be reduced, thereby improving measurement accuracy. Note that if the at least two selected sensors SE cross an innervation zone, the biometric signals will be in opposite phase, and the selection unit 42 will reselect at least two sensors SE that do not cross an innervation zone. The measurement unit 43 inputs the measured biometric information of the user 2 to the execution unit 44. In this embodiment, the biometric information of the user 2 includes an electromyogram of the procerus muscle of the user 2.

[0076] Next, in step ST4, the execution unit 44 executes processing based on the biometric information input from the measurement unit 43. For example, the execution unit 44 inputs the biometric information input from the measurement unit 43 into a machine-learned estimation model to estimate the user's intention to operate an object and execute the operation on the object. For example, a user interface (UI) object to be operated by the user 2 is displayed on the screen of a device such as the VR goggles 1. The object includes an icon that triggers the launch of an application. The VR goggles 1 also have a separate gaze tracking device or head direction measurement device to determine which icon the user 2 is gazing at. When the user 2 gazes at the icon of a specific application and intends to launch the application, this intention causes a slight contraction or relaxation of the procerus muscle of the user 2. The measurement unit 43 outputs a myoelectric potential signal associated with the slight contraction or relaxation of the procerus muscle as a biometric signal. The execution unit 44, upon receiving the biometric signal, estimates, using the estimation model, that the biometric signal indicates an intention to run an application and executes a device operation, such as launching the application.

[0077] According to this embodiment, at least two biometric signals can be obtained with high accuracy from at least two sensors SE among the multiple sensors SE1 to SE7 that are energized and placed in close contact with the face of user 2, thereby making it possible to measure the biometric information of user 2 with high accuracy.

[0078] Furthermore, according to this embodiment, at least two highly accurate biosignals can be selected from the multiple output signals output by the multiple sensors SE1 to SE7, thereby making it possible to measure the bioinformation of the user 2 with high accuracy.

[0079] Furthermore, according to this embodiment, at least two sensors SE can be appropriately selected from the plurality of sensors SE1 to SE7 based on the measurement results of the impedance of each sensor SE.

[0080] Furthermore, according to this embodiment, the biasing member 21 brings at least two sensors SE into contact with the face of the user 2 by applying a biasing force in a direction that brings at least a portion of the curved surface on which the plurality of sensors SE1 to SE7 are arranged into close contact with the face of the user 2. This allows at least two sensors SE out of the plurality of sensors SE1 to SE7 to be reliably brought into close contact with the face of the user 2.

[0081] Furthermore, according to this embodiment, the restoring force of the substrate 21A, which is deformed by contact with the face of the user 2, allows at least two sensors SE out of the multiple sensors SE1 to SE7 to be securely attached to the face of the user 2.

[0082] Furthermore, according to this embodiment, by measuring myoelectricity, it is possible to estimate the operation intention of the user 2 using the myoelectricity.

[0083] Furthermore, according to this embodiment, by arranging a plurality of sensors SE1 to SE7 along the direction of the muscle fibers of the muscle to be measured, the influence of noise can be reduced and the accuracy of measuring myoelectric potential can be improved.

[0084] Furthermore, according to this embodiment, by measuring the electromyogram of the procerus muscle, it is possible to improve the accuracy of estimating the operational intention of the user 2 .

[0085] Various modifications of the present disclosure will be described below. The modifications described below can be applied in any combination.

[0086] (First Modification) FIG. 7 is a flowchart showing the details of step ST2.

[0087] First, in step ST21B, the selector 42 calculates the correlation between the plurality of output signals output from the plurality of sensors SE1 to SE7. As an index representing the correlation between two signals, for example, a correlation coefficient can be used.

[0088] Next, in step ST22, the selection unit 42 selects at least two sensors SE that are in close contact with the between the eyebrows of the user 2 based on the calculation result of the correlation. The output signals of sensors SE that are in close contact with the skin of the user 2 have a high correlation, and the output signals of sensors SE that are not in close contact with the skin of the user 2 have a low correlation. If the calculated correlation is equal to or greater than a predetermined threshold, the selection unit 42 determines that those sensors SE are in close contact with the between the eyebrows of the user 2, and if the calculated correlation is less than the predetermined threshold, the selection unit 42 determines that those sensors SE are not in close contact with the between the eyebrows of the user 2. The selection unit 42 selects at least two adjacent sensors SE and inputs at least two output signals acquired from the selected at least two sensors SE to the measurement unit 43 as at least two biological signals.

[0089] According to this modification, the selector 42 can appropriately select at least two sensors SE from among the plurality of sensors SE1 to SE7 based on the correlation between the plurality of output signals output by the plurality of sensors SE1 to SE7.

[0090] (Second Modification) Fig. 8 is a diagram schematically showing an example of the configuration of the measurement unit 14. The flexible substrate constituting the curved surface is not limited to substrate 21A having a substantially C-shaped cross section as shown in Fig. 2, but may be spherical substrate 21B as shown in Fig. 8. Furthermore, substrate 21B is not limited to a spherical shape, and may be cylindrical, semicircular, sector-shaped, hemispherical, or the like.

[0091] 9 is a diagram schematically illustrating an example configuration of the measurement unit 14. A flexible substrate 21C having a semicircular cross section is supported by a support member 22 having a V-shaped cross section, and an end point of the support member 22 is attached to the support member 13 by a joint 23 so as to be able to swing freely. The measurement unit 14 swings in accordance with the position between the eyebrows of the user 2.

[0092] FIG. 10 is a diagram schematically illustrating an example configuration of the measurement unit 14. The measurement unit 14 may include multiple rows of sensor arrays SA1 to SA3 (three rows in this example). Each of the sensor arrays SA1 to SA3 may measure the same biosignal, or different biosignals. For example, the sensor array SA1 may measure electromyography, the sensor array SA2 may measure skin temperature, and the sensor array SA3 may measure heart rate. Without being limited to this example, the biosignal may be electromyography, electrooculography, electroencephalography, changes in muscle shape or muscle hardness, sounds generated by the living body, skin temperature, skin conductivity, respiration, sweating, heart rate, blood pressure, or the like.

[0093] (Third Modification) Fig. 11 is a diagram schematically illustrating an example configuration of measurement unit 14. Biasing member 21 has a plastic bag body 21D that forms a curved surface on which sensors SE1 to SE7 are arranged, and may generate a biasing force in a direction that brings at least a portion of the curved surface into close contact with the face of user 2 by the pressure of a fluid sealed inside bag body 21D using pump 24. Bag body 21D may be made of a material such as cloth or resin. The fluid may be a gas or a liquid.

[0094] FIG. 12 is a diagram schematically illustrating an example configuration of the measurement unit 14. A plastic bag 21E is disposed to cover the entire inner surface of the frame 11, and the bag 21E defines multiple curved surfaces on which multiple sensor arrays SA11 to SA13 are disposed. The bag 21E is formed with openings 25 that expose both eyes of the user 2. A pump 26 is used to seal fluid inside the bag 21E, and the pressure of the fluid generates a biasing force in a direction that causes at least a portion of each curved surface to come into close contact with the face of the user 2. The sensor array SA11 measures the myoelectric potential of the procerus muscle, the sensor array SA12 measures the myoelectric potential of the orbicularis oculi muscle, and the sensor array SA13 measures the myoelectric potential of the temporalis muscle. However, the present invention is not limited to this example.

[0095] According to this modification, at least two sensors SE out of the plurality of sensors SE1 to SE7 can be reliably brought into close contact with the face of user 2 by the pressure of the fluid sealed inside bags 21D and 21E.

[0096] 13 is a diagram schematically illustrating an example configuration of measurement unit 14. Biasing member 21 may have spring 27 connected to the back surface of curved surface 21F on which sensors SE1 to SE7 are arranged, and may generate a biasing force in a direction that brings at least a portion of curved surface 21F into close contact with the face of user 2 due to the elastic force of spring 27 that deforms when curved surface 21F comes into contact with the face of user 2. Curved surface 21F may be made of a material such as cloth or resin.

[0097] According to this modification, at least two sensors SE out of the plurality of sensors SE1 to SE7 can be reliably brought into close contact with the face of the user 2 by the elastic force of the spring 27 which is deformed by contact with the face of the user 2.

[0098] (Fifth Modification) Fig. 14 is a diagram schematically illustrating an example configuration of the measurement unit 14. The biasing member 21 has a low-resilience material 21G that forms a curved surface on which the sensors SE1 to SE7 are arranged, and the biasing force of the low-resilience material 21G that deforms upon contact with the face of the user 2 may generate a biasing force in a direction that causes at least a portion of the curved surface to come into close contact with the face of the user 2. The material of the low-resilience material 21G may be urethane, sponge, seaweed, or the like. Furthermore, the material is not limited to a low-resilience material, and a high-resilience material may also be used.

[0099] According to this modified example, the repulsive force of the low-resilience material 21G, which deforms upon contact with the face of the user 2, allows at least two sensors SE out of the multiple sensors SE1 to SE7 to be securely attached to the face of the user 2.

[0100] 15 is a diagram schematically illustrating an example configuration of the measurement unit 14. The biasing member 21 has a shape-memory material 21H that forms a curved surface on which the sensors SE1 to SE7 are arranged, and may generate a biasing force in a direction that brings at least a portion of the curved surface into close contact with the face of the user 2, due to the shape recovery force of the shape-memory material 21H caused by the body temperature of the user 2 upon contact with the face of the user 2. Note that the shape-memory material 21H may be heated or cooled not only by the body temperature of the user 2, but also by a Peltier element or the like.

[0101] According to this modified example, the shape recovery force of the shape memory material 21H due to the body temperature of the user 2 when it comes into contact with the face of the user 2 allows at least two sensors SE out of the multiple sensors SE1 to SE7 to be securely attached to the face of the user 2.

[0102] 16 is a simplified side view showing an example configuration of VR goggles 1 worn on the face of a user 2. An electrode 71 for measuring the potential of the forehead of the user 2 is disposed on the upper part of the frame 11. The potential of the forehead measured by the electrode 71 is used as a reference potential when measuring the impedance of each of the sensors SE1 to SE7.

[0103] Above the frame 11 at the location where the electrode 71 is disposed, a comb-like member 72 for pushing up the front hair of the user 2 is disposed. This prevents the front hair of the user 2 from overlapping the electrode 71 and interfering with the potential measurement.

[0104] (Eighth Modification) The selection unit 42 may select at least two sensors SE during calibration before starting to use the VR goggles 1, or may dynamically change the selection of at least two sensors SE during actual use after starting to use the VR goggles 1. When dynamically changing the selection of at least two sensors SE, the selection unit 42 may change only the positions of the sensors SE, may change only the number of sensors SE, or may change both the positions and the number of sensors SE. The calibration may include calibration of an eye-tracking device separately installed in the VR goggles.

[0105] The maximum number N of sensors SE that can be simultaneously selected is set according to the resources of the signal processing circuit, such as the amplifier circuit, AD conversion circuit, or communication circuit. The maximum number N may be changed according to the sampling rate.

[0106] The selector 42 may select N sensors SE in ascending order of impedance. In this case, if a sensor SE whose impedance is equal to or greater than a predetermined threshold is included in the selection targets, a fault flag may be added to the output signal of the sensor SE, enabling a downstream signal processing circuit to take appropriate action. Alternatively, the selector 42 may select a group with the smallest average impedance of N consecutive sensors SE. Alternatively, the selector 42 may select N consecutive sensors SE centered around the sensor SE with the smallest impedance.

[0107] According to this modification, the selector 42 dynamically changes at least one of the positions and the number of at least two sensors SE selected from the plurality of sensors SE1 to SE7. This makes it possible to appropriately reselect at least two sensors SE from the plurality of sensors SE1 to SE7 even if the position of the measurement unit 14 shifts in accordance with the movement of the face of the user 2.

[0108] (Ninth Modification) The biological signal is not limited to myoelectric potential, but may be a pulse wave. In this case, the sensors SE1 to SE7 may be arranged along the direction of the blood vessel to be measured.

[0109] According to this modification, by measuring the pulse wave, it is possible to estimate the user's operational intention using the pulse wave.

[0110] Furthermore, according to this modification, by arranging a plurality of sensors SE1 to SE7 along the direction in which the blood vessel to be measured runs, the influence of noise can be reduced and the accuracy of measuring the pulse wave can be improved.

[0111] (Tenth Modification) The processing unit 31 may detect a positional deviation in the wearing position of the VR goggles 1. Furthermore, when the processing unit 31 detects a positional deviation of the VR goggles 1, the processing unit 31 may prompt the user to put the VR goggles 1 back on, or may automatically perform the operation of putting the VR goggles 1 back on.

[0112] The processing unit 31 detects a positional shift of the VR goggles 1 when the number of sensors SE whose impedance is less than the threshold value falls below the lower limit value, or when the average value of the impedance of the selected N sensors SE exceeds the upper limit value.

[0113] The processing unit 31 may prompt the user 2 to put the VR goggles 1 back on by displaying a text message or an image on the screen of the VR goggles 1. Alternatively, the processing unit 31 may prompt the user 2 to put the VR goggles 1 back on by outputting a warning sound or audio guidance from the speaker of the VR goggles 1. The manner of putting the VR goggles 1 back on may be to correct any misalignment, or to remove the VR goggles 1 and then put them back on. At that time, the processing unit 31 may prompt the user 2 to take actions such as cleaning their skin or moving their hair aside.

[0114] The processing unit 31 may automatically execute the operation of putting back on the VR goggles 1 by removing the fluid from inside the bags 21D, 21E to temporarily deflate the bags 21D, 21E, and then using the pumps 24, 26 to refill the fluid into the bags 21D, 21E. Alternatively, the processing unit 31 may increase or decrease the pressure of the fluid sealed inside the bags 21D, 21E. Alternatively, the processing unit 31 may automatically execute the operation of putting back on the VR goggles 1 by temporarily deflating the shape-memory material 21H by cooling with a Peltier element, and then heating with the Peltier element to restore the shape of the shape-memory material 21H.

[0115] The present disclosure is widely applicable to eyeglass-type devices worn by a user, such as VR goggles, AR glasses, smart glasses, head-mounted displays, etc. Furthermore, the present disclosure is not limited to eyeglass-type devices, but is also widely applicable to computer user interfaces, digital assistants, information terminals, wearable devices, health checkups, etc.

Claims

1. A biometric information measuring device comprising: a plurality of sensors capable of measuring a user's biometric signals by contacting the user's face; a biasing member that brings at least two of the plurality of sensors into contact with the user's face; and a measuring unit that measures the user's biometric information based on at least two biometric signals obtained from the at least two sensors.

2. The biological information measuring device according to claim 1, further comprising a selection unit that selects the at least two sensors from the plurality of sensors.

3. The biological information measuring device according to claim 2, wherein the selection unit selects the at least two sensors based on the impedance of each of the plurality of sensors.

4. The biological information measuring device according to claim 2, wherein the selection unit selects the at least two sensors based on a correlation between a plurality of output signals output by the plurality of sensors.

5. The bioinformation measuring device according to claim 2, wherein the selection unit dynamically changes at least one of the positions and the number of the at least two sensors selected from the plurality of sensors.

6. The bio-information measuring device of claim 1, wherein the biasing member brings the at least two sensors into contact with the user's face by applying a biasing force in a direction that brings at least a portion of the curved surface on which the multiple sensors are arranged into close contact with the user's face.

7. The bioinformation measuring device according to claim 6, wherein the biasing member has a flexible base material that forms the curved surface, and the biasing force is generated by the restoring force of the base material that is deformed by contact with the user's face.

8. The bioinformation measuring device according to claim 6, wherein the biasing member has a plastic bag body that forms the curved surface, and the biasing force is generated by the pressure of a fluid sealed inside the bag body.

9. The bioinformation measuring device of claim 6, wherein the biasing member has a spring connected to the back surface of the curved surface, and the biasing force is generated by the elastic force of the spring which deforms upon contact with the user's face.

10. The bioinformation measuring device of claim 6, wherein the biasing member has a repulsive material that forms the curved surface, and the biasing force is generated by the repulsive force of the repulsive material that deforms upon contact with the user's face.

11. The bioinformation measuring device of claim 6, wherein the biasing member has a shape memory material that forms the curved surface, and the biasing force is generated by the shape recovery force of the shape memory material due to the user's body temperature upon contact with the user's face.

12. The bioinformation measuring device according to claim 1, wherein the biosignal includes myoelectricity.

13. The bioinformation measuring device according to claim 12, wherein the plurality of sensors are arranged along the direction of muscle fibers of the muscle to be measured.

14. The bioinformation measuring device according to claim 13, wherein the muscles to be measured include the procerus muscle.

15. The biological information measuring device according to claim 1, wherein the biological signal includes a pulse wave.

16. The bioinformation measuring device according to claim 15, wherein the plurality of sensors are arranged along the direction of the blood vessel to be measured.

17. The bioinformation measuring device of claim 1, wherein the biosignals include at least one of electrooculography, electroencephalography, changes in muscle shape, changes in muscle hardness, sounds generated by the living body, skin temperature, skin conductivity, respiration, sweating, heart rate, and blood pressure.

18. A bioinformation measurement method using a bioinformation measurement device comprising a plurality of sensors capable of measuring the user's biosignals by contacting the user's face, and a biasing member for bringing at least two of the plurality of sensors into contact with the user's face, wherein the bioinformation measurement device acquires at least two biosignals from the at least two sensors, and measures the user's bioinformation based on the acquired at least two biosignals.

19. A program for causing an information processing device to execute processing in a bioinformation measuring device that has a plurality of sensors that can each measure a user's biosignals by contacting the user's face, and a biasing member that brings at least two of the plurality of sensors into contact with the user's face, wherein by executing the program, the information processing device acquires at least two biosignals from the at least two sensors, and measures the user's bioinformation based on the acquired at least two biosignals.

20. An interface method using a bio-information measuring device according to any one of claims 1 to 17, for operating equipment based on at least the bio-information measured by the measuring unit.

21. The interface method according to claim 20, further comprising detecting the user's line of sight, and operating the device based on the line of sight and the biometric information.

22. The interface method according to claim 21, wherein in detecting the gaze, an object to be operated is selected based on the gaze, and in operating the device, an operation on the object to be operated is performed based on the biometric information.

Images