Smart device-based exercise amount measurement system

Abstract

The present invention relates to a smart dumbbell for measuring a user's grip strength and a smart device-based exercise amount measurement system including same. The exercise amount measurement system comprises: a smart muscle band which is mounted on a specific body part of a user, obtains an electromyogram signal, generated from muscles of the body part, by using an embedded electrode for electromyogram measurement, and calculates muscle fatigue and muscle activity of the muscles on the basis of the electromyogram signal; a smart dumbbell which generates a strain sensing signal through an embedded first strain sensor when gripped by the user, and inputs the strain sensing signal to a pre-trained neural network-based grip strength prediction model to generate grip strength data; and an evaluation system which stores the muscle fatigue, the muscle activity, and the grip strength data in an internal storage and monitors same.

Description

Smart device-based exercise measurement system

[0001] The present invention relates to a smart device-based exercise volume measurement system that acquires and monitors data from wearable equipment worn by a user exercising or equipment used for exercising.

[0002] Patent No. 10-1941863 (registration date: January 18, 2019) relates to the invention of an electrical stimulation-type muscle exercise device, and features the invention in that the electrode terminals are arranged at equal intervals to evenly apply electrical stimulation to the user's muscles and fat, thereby enhancing the exercise effect. However, this invention discloses a configuration in which a control module operates according to the user's input module operation to adjust the intensity and frequency of the electrical stimulation, but does not disclose a configuration in which stimulation is applied according to the user's actual muscle fatigue and amount of exercise. Therefore, there is a limitation in that it cannot control the appropriate stimulation necessary for the user's fatigue recovery.

[0003] Patent No. 10-2177136 (registration date 2020.11.04.) relates to the invention of a wearable muscle strength measurement device and system, and discloses a configuration that measures in real time the electromyogram detected from the muscles that change according to the wearer's movements and the positional change between the bands, calculates the wearer's exercise volume using the electromyogram data, and feeds back the positional change between the bands and the exercise volume to the user via a smartphone or PC. However, this invention does not disclose a configuration that can analyze the wearer's fatigue level based on the amount of exercise or the number of exercises and take measures to recover from fatigue.

[0004] Patent No. 10-2495972 (registration date: January 31, 2023) relates to a method and system for measuring real-time muscle activity and muscle fatigue based on electromyography (EMG). The invention discloses a configuration for measuring muscle activity and muscle fatigue based on EMG signals received from electrodes attached to the user's body. However, this invention does not provide a method or system for directly measuring and monitoring data from exercise equipment used by the user during exercise.

[0005] The purpose of the present invention is to provide a smart device-based exercise amount measurement system that can comprehensively collect and monitor not only electromyography signals but also user exercise information measured by strain sensor strain or smart exercise equipment.

[0006] The purpose of the present invention is not limited to the purposes mentioned above, and other purposes not mentioned will be clearly understood by those skilled in the art from the description below.

[0007] A smart dumbbell according to one embodiment of the present invention is a device capable of measuring a user's amount of exercise, comprising: a strain sensor that generates a strain sensing signal when the user grips the smart dumbbell; an amplifier that amplifies the strain sensing signal; an A / D converter that converts the amplified strain sensing signal into a digital strain sensing signal; and a processor that inputs the digital strain sensing signal into a grip strength prediction model based on a pre-trained neural network to generate grip strength data.

[0008] In one embodiment of the present invention, the smart dumbbell may further include a communication module that transmits the grip strength data to an external evaluation system.

[0009] In one embodiment of the present invention, the grip strength prediction model receives grip strength data from the smart dumbbell, uses the grip strength value measured by the electronic grip dynamometer as a label, and uses a cross entropy function as a cost function, and learns weight parameters in a direction that minimizes the cost function.

[0010]

[0011] According to one embodiment of the present invention, a smart device-based exercise amount measurement system includes: a smart muscle band that is mounted on a specific body part of a user, acquires electromyography signals generated from muscles of the body part using built-in electromyography measurement electrodes, and calculates muscle fatigue and muscle activity of the muscles based on the electromyography signals; a smart dumbbell that, when gripped by the user, generates a strain sensing signal through a built-in first strain sensor, and inputs the strain sensing signal into a pre-learned neural network-based grip force prediction model to generate grip force data; and an evaluation system that stores and monitors the muscle fatigue, muscle activity, and grip force data in an internal storage.

[0012] In one embodiment of the present invention, the smart device-based exercise amount measurement system may further include a Bluetooth gateway that receives the muscle fatigue and muscle activity from the smart muscle band via Bluetooth communication, receives the grip strength data from the smart dumbbell via Bluetooth communication, and transmits the muscle fatigue, muscle activity, and grip strength data to the evaluation system via the cloud.

[0013] In one embodiment of the present invention, the smart muscle band can obtain strain data through a built-in second strain sensor, calculate the number of times the muscle is moved based on the strain data, and transmit the calculated number of times the muscle is moved to the evaluation system.

[0014]

[0015] According to one embodiment of the present invention, a method for operating a smart device-based exercise amount measurement system includes: a step of allowing a smart muscle band mounted on a specific body part of a user to obtain an electromyography signal generated from a muscle of the body part using a built-in electromyography measurement electrode, and calculating muscle fatigue and muscle activity of the muscle based on the electromyography signal; a step of allowing the smart dumbbell to generate a strain sensing signal through a built-in first strain sensor when the user grips the smart dumbbell, and inputting the strain sensing signal into a pre-learned neural network-based grip strength prediction model to generate grip strength data; and a step of allowing an evaluation system to store and monitor the muscle fatigue, the muscle activity, and the grip strength data in an internal storage.

[0016] In one embodiment of the present invention, the method for operating the smart device-based exercise measurement system may further include a step in which the smart muscle band acquires strain data through a built-in second strain sensor and calculates the number of times the muscle is moved based on the strain data; and a step in which the evaluation system stores and monitors the number of times the muscle is moved in an internal storage.

[0017]

[0018] Through the smart device-based exercise amount measurement system according to the present invention, a user can monitor information such as muscle activity, muscle fatigue, and number of exercises generated while exercising, as well as information regarding the force applied to the exercise equipment.

[0019] A user wearing a smart muscle band included in a smart device-based exercise amount measurement system according to the present invention can perform exercise while monitoring his / her exercise amount and fatigue level, and can relieve muscle fatigue accumulated through exercise by receiving appropriate stimulation through electrodes.

[0020] The effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present invention belongs from the description below.

[0021] Figure 1 is an exemplary drawing of a service using a smart muscle band according to one embodiment of the present invention.

[0022] FIG. 2 is an exemplary drawing of a user wearing a smart muscle band according to one embodiment of the present invention.

[0023] Figure 3 is a block diagram showing the configuration of a smart muscle band according to one embodiment of the present invention.

[0024] Figure 4 is an example drawing of a smart muscle band worn on the arm.

[0025] Figure 5 is an example drawing of a smart muscle band worn on the leg.

[0026] FIG. 6 is a drawing showing an example of a fastening structure of a conductive pad and a band portion included in a smart muscle band according to the present invention and a mechanical configuration of the conductive pad.

[0027] FIG. 7 is a block diagram showing the configuration of a smart device-based exercise amount measurement system according to one embodiment of the present invention.

[0028] Figure 8 is a block diagram showing the configuration of a smart dumbbell according to one embodiment of the present invention.

[0029] FIG. 9 is an exemplary drawing of a smart dumbbell according to one embodiment of the present invention.

[0030] Figure 10 is a diagram illustrating the learning process of the grip strength prediction model used in the smart dumbbell.

[0031] Figure 11 is a diagram illustrating the inference process of the grip strength prediction model used in the smart dumbbell.

[0032] Figure 12 is a diagram showing predicted grip strength values ​​using a grip strength prediction model and measured grip strength values ​​using an electronic grip dynamometer.

[0033] FIG. 13 is a flowchart for explaining an operation method of a smart device-based exercise measurement system according to one embodiment of the present invention.

[0034] Figure 14 is a block diagram showing the configuration of an evaluation system according to one embodiment of the present invention.

[0035]

[0036] The advantages and features of the present invention, and the methods for achieving them, will become clear with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and these embodiments are provided only to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Meanwhile, the terminology used in this specification is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated in the phrase. The terms "comprises" and / or "comprising" as used in the specification do not exclude the presence or addition of one or more other components, steps, operations, and / or elements mentioned.

[0037] While terms like "first" and "second" may be used to describe various components, these components should not be limited by these terms. These terms may be used to distinguish one component from another. For example, without departing from the scope of the present invention, a first component could be referred to as a "second component," and similarly, a second component could also be referred to as a "first component."

[0038] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components in between. Conversely, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions that describe the relationship between components, such as "between" and "directly between" or "adjacent to" and "directly adjacent to", should be interpreted similarly.

[0039] In describing the present invention, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the present invention, the detailed description is omitted.

[0040] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. In order to facilitate an overall understanding in describing the present invention, the same reference numbers will be used for the same means regardless of the drawing numbers.

[0041]

[0042] FIG. 1 is an exemplary drawing of a service using a smart muscle band according to one embodiment of the present invention, and FIG. 2 is an exemplary drawing of a user wearing a smart muscle band according to one embodiment of the present invention.

[0043] As illustrated in FIG. 1, a user can exercise while wearing a plurality of smart muscle bands (100) according to the present invention on their arms and legs. At this time, the smart muscle band (100) obtains electromyography signals from electromyography measuring electrodes (111) that are in contact with the skin of a specific body part of the user, and obtains strain data of a strain sensor (121) that repeats elongation and contraction due to the changing muscle thickness of the user. The smart muscle band (100) predicts muscle activity and muscle fatigue based on the electromyography signals, determines a stimulation voltage according to the predicted muscle fatigue, and then applies electrical stimulation according to the determined stimulation voltage to the user's muscles using muscle stimulation electrodes (112), thereby relaxing the user's muscles that are stiff due to exercise.

[0044] A smart muscle band (100) may be equipped with a controller (113) and a communication device (114). When a user wears multiple smart muscle bands (100) and exercises, one of the controllers (113) of the multiple smart muscle bands (100) may function as a master controller, and the rest may function as slave controllers. At this time, the controller (113) functioning as a master controller may perform synchronization and power control of other smart muscle bands (100) via the communication device (114). For example, the controller (113) functioning as a master controller may transmit a control signal to a slave controller via the communication device (114) when the power is turned on, thereby synchronizing the slave controller, i.e., other smart muscle bands (100). In addition, when the power of the smart muscle band (100) including the master controller is turned off, the controller (113) acting as the master controller can transmit a power control signal to the slave controller through the communication device (114) so ​​that the power of other smart muscle bands (100) including the slave controllers is turned off. For example, when a user wears a smart muscle band (100) on each of the left arm, right arm, left leg, and right leg and exercises, the four smart muscle bands (100) are synchronized and the power is controlled simultaneously.

[0045] For example, when a user wears a wearable suit made of conductive fibers and exercises, a plurality of smart muscle bands (100) can be connected to each other by wires through contact points with the wearable suit. As another example, the communication device (114) can be equipped with a Bluetooth module, and in this case, even if the user does not wear the wearable suit, a plurality of smart muscle bands (100) can establish a wireless connection with each other through Bluetooth communication to transmit and receive control signals or data.

[0046]

[0047] FIG. 3 is a block diagram showing the configuration of a smart muscle band according to one embodiment of the present invention.

[0048] A smart muscle band (100) is a device that is attached to a specific body part of a user, collects electromyography signals generated from muscles of said body part and sensing data of a strain sensor (121) that changes according to changes in the thickness of the user's muscles, calculates and monitors muscle activity, muscle fatigue, and exercise amount (e.g., number of exercise sessions) based on the collected electromyography signals and sensing data, and applies appropriate electrical stimulation to the user's muscles according to the muscle fatigue to relieve the user's muscle fatigue.

[0049] Referring to FIG. 3, a smart muscle band (100) according to one embodiment of the present invention includes a conductive pad (110) and a band portion (120).

[0050] The conductive pad (110) includes an electrode for electromyography measurement (111), an electrode for muscle stimulation (112), a controller (113), a communication device (114), and a fixing part (115), and the band part (120) includes a strain sensor (121), a basic circumference measurement circuit (122), and an elastic part (123).

[0051] The conductive pad (110) and the band portion (120) are physically connected and electrically coupled through the fixing portion (115). The smart muscle band (100) illustrated in FIG. 3 is according to one embodiment, and the components of the smart muscle band (100) according to the present invention are not limited to the embodiment illustrated in FIG. 3, and may be added, changed, or deleted as needed.

[0052] The conductive pad (110) obtains electromyography signals generated from muscles of a specific body part of the user through an electromyography measuring electrode (111), calculates muscle fatigue of the muscle based on the electromyography signals, and determines a stimulation voltage to be applied to the muscle based on the muscle fatigue.

[0053] The band part (120) may have a belt shape. The band part (120) is physically and electrically connected to the conductive pad (110), and is mounted on the body part in a form where one end and the other end of the band shape are connected while wrapping around the circumference of the body part, thereby fixing the conductive pad (110) to the body part. At this time, the electromyography measuring electrode (111) and the muscle stimulation electrode (112) included in the conductive pad (110) are brought into close contact with the body part.

[0054] In addition, the band part (120) transmits strain data of the built-in strain sensor (121) to the conductive pad (110), and the conductive pad (110) can calculate the number of times the muscle is moved based on the strain data of the strain sensor (121).

[0055] As described above, the conductive pad (110) includes an electrode for electromyography measurement (111), an electrode for muscle stimulation (112), a controller (113), a communication device (114), and a fixing member (115).

[0056] The electromyography measuring electrode (111) acquires electromyography signals generated from the muscles. A plurality of electromyography measuring electrodes (111) are arranged on a conductive pad (110). For example, three electrodes, such as a ground electrode, a reference electrode, and a signal electrode, may be arranged on one conductive pad (110), and the controller (113) can analyze the electromyography signals generated from the three electrodes to predict the muscle activity and muscle fatigue of the corresponding muscle of the target body part.

[0057] The controller (113) includes an amplifier that can amplify an analog signal (e.g., an electromyography signal, strain data of a strain sensor (121)) and a processor that performs computational processing. The controller (113) analyzes the electromyography signal using FFT (Fast Fourier Transform) and spectrum analysis to calculate muscle activity and muscle fatigue of the muscle, and determines a stimulation voltage to be applied to the muscle based on the muscle fatigue. In addition, the controller (113) can calculate the number of times the muscle is exercised based on the strain data of the strain sensor (121).

[0058] As another example, the controller (113) can calculate muscle activity of a muscle of a target body part based on strain data of a strain sensor (121) and supplement (correct) the accuracy of the calculated muscle activity based on an electromyography signal.

[0059] The muscle stimulation electrode (112) applies a stimulation voltage (electrical stimulation) determined by the controller (113) to the muscle.

[0060] The communication device (114) transmits and receives control signals or data through wired / wireless communication with the communication device (114') of another smart muscle band (100') or an external device (e.g., a mobile device such as a smartphone or laptop).

[0061] For example, the controller (113) can transmit the collected muscle activity, muscle fatigue, and exercise count data to an external device via a communication device (114) so ​​that the user can check it.

[0062] Meanwhile, the communication device (114) may include a Bluetooth module. The controller (113) may synchronize another smart muscle band (100') with the smart muscle band (100) via the communication device (114), and may control the power of the other smart muscle band (100') to be turned off when the power of the smart muscle band (100) is turned off.

[0063] Specifically, the controller (113) transmits a predetermined control signal to a controller of another smart muscle band (100') (hereinafter referred to as a 'second controller') through a communication device (114) so ​​that the second controller is synchronized with the controller (113). In addition, the controller (113) can transmit muscle activity, muscle fatigue, and exercise number data to an external device (e.g., a mobile device) through the communication device (114).

[0064] The fixing part (115) physically couples and electrically connects the conductive pad (110) and the band part (120). For example, the fixing part (115) may physically couple the conductive pad (110) and the band part (120) via a magnet, and electrically connect the conductive pad (110) and the band part (120) via an electrical contact. Accordingly, the band part (120) may transmit strain data of the strain sensor (121) and a current signal (resistance value) of the basic circumference measurement circuit (122) to the controller (113). For example, the fixing part (115) may be a pogo pin.

[0065] As described above, the band portion (120) includes a strain sensor (121), a basic circumference measuring circuit (122), and an elastic portion (123).

[0066] The basic circumference measuring circuit (122) has a characteristic in which the resistance value varies according to the circumference of the body part at the time the band part (120) is worn on the user's specific body part. Specifically, the band part (120) has a band shape and is worn on the body part in a manner in which one end and the other end of the band shape are joined while wrapping around the circumference of the body part. At this time, the joining positions of the one end and the other end vary according to the circumference of the body part, and the basic circumference measuring circuit (122) has a structure in which the resistance value is determined according to the joining position, so that the resistance value is ultimately determined according to the circumference of the body part. Accordingly, the controller (113) can estimate the circumference of the body part based on the resistance value of the basic circumference measuring circuit (122). The controller (113) calculates the basic length of the body part based on the resistance value of the basic circumference measuring circuit (122) at the time the band part (120) is worn on the body part.

[0067] The strain sensor (121) obtains strain data due to changes in the length of the circumference. The strain data is data measured based on changes in the resistance value of a variable resistor built into the strain sensor (121).

[0068] The controller (113) calculates the basic length of the circumference of the body part based on the resistance value of the basic circumference measurement circuit (122), and calculates the final length of the circumference by correcting the basic length using the strain data. For example, the controller (113) can measure the length of the circumference of the target body part (e.g., arm, leg) in mm based on the strain data generated by the strain sensor (121). Since the strain data varies depending on the change in muscle thickness of the body part, the final length may continuously change while the user is exercising.

[0069] In addition, the controller (113) can calculate muscle activity based on strain data generated by the strain sensor (121), and can correct muscle activity based on electromyography signals acquired through the electromyography measuring electrodes (111). The controller (113) can calculate the user's exercise amount (e.g., number of exercise sessions) based on muscle activity, and can measure changes in the user's muscle mass by monitoring the circumference of the target body part over a long period of time, and can determine whether or not the user has sarcopenia.

[0070] The elastic portion (123) is a portion of elastic material that occupies a specific area of ​​the band portion (120), and can be stretched according to the length of the circumference of the body part of the user to which the band portion (120) is attached. Accordingly, the smart muscle band (100) can be worn in close contact with the body part.

[0071] In addition, the elastic portion (123) may further include a portion made of a shape memory alloy in addition to the elastic material. For example, the band portion (120) may automatically fit to a user's body part through the contraction action of the shape memory alloy wire. Since the shape memory alloy consumes a lot of current, an electrostatic clutch may be additionally provided in the elastic portion (123) to reduce the current consumption due to the shape memory alloy and further improve the fit. The controller (113) may improve the fit by applying an appropriate voltage to the electrostatic clutch to generate an electrostatic force.

[0072]

[0073] Fig. 4 is an example drawing of a smart muscle band worn on an arm, and Fig. 5 is an example drawing of a smart muscle band worn on a leg.

[0074] As illustrated in FIGS. 4 and 5, the smart muscle band (100) may include a plurality of conductive pads (110). In addition, the area or location occupied by the conductive pads (110) and the arrangement of the plurality of conductive pads (110) may vary depending on the body part on which the band is worn. The location or spacing of the conductive pads (110) may be determined according to the location of the muscles of the body part. This is because the electrodes (111) for measuring electromyography and the electrodes (112) for stimulating muscles must contact the skin at the location of the muscles of the target body part.

[0075] In addition, the strain sensor (121) is placed in contact with the elastic portion (123), thereby generating strain data that changes according to the elongation and contraction of the elastic portion (123).

[0076] Meanwhile, the basic circumference measuring circuit (122) includes a plurality of male metal buttons (122-1), a plurality of female metal buttons (122-2), and a resistor (122-3) between the female metal buttons (122-2).

[0077] A male metal button (122-1) is arranged at one end of the band portion (120), and a female metal button (122-2) is arranged at the other end of the band portion (120). A plurality of female metal buttons (122-2) that can be coupled to one male metal button (122-1) may be provided along the long axis (circumferential direction) of the band portion (120). The female metal button (122-2) coupled to the male metal button (122-1) varies depending on the length of the circumference of the target body part. That is, since the contact point between the male metal button (122-1) and the female metal button (122-2) at which the basic circumference measuring circuit (122) is formed varies depending on the length of the circumference, the resistance value of the basic circumference measuring circuit (122) varies depending on the length of the circumference.

[0078]

[0079] FIG. 6 is a drawing showing an example of a fastening structure of a conductive pad and a band portion included in a smart muscle band according to the present invention and a mechanical configuration of the conductive pad.

[0080] The conductive pad (110) is divided into a band upper portion (band-upper) located at the upper portion of the band portion (120) and a band lower portion (band-lower) located at the lower portion of the band portion (120) in Fig. 6. The band upper portion includes a battery and an LED (Light-Emitting Diode) that indicates the power on / off status.

[0081] The band upper part, the band part (120), and the band lower part are physically and electrically connected through the fixing part (115). That is, FIG. 6 is an example in which the fixing part (115) is a pogo pin. The fixing part (115) has a detachable structure, so that the conductive pad (110) and the band part (120) can be easily connected and separated. Since the conductive pad (110) and the band part (120) can be easily separated according to the detachable structure of the fixing part (115), there is an effect in which charging of the conductive pad (110) and cleaning of the band part (120) become convenient. In addition, since the fixed portion (115) serves as an electrical contact point between the conductive pad (110) and the band portion (120), it transmits an analog signal (strain data of the strain sensor (121), current signal (resistance value) of the basic circumference measurement circuit (122)) generated in the band portion (120) to the substrate (PCB) of the conductive pad (110) so that the controller (113) mounted on the substrate can receive the analog signal.

[0082]

[0083] FIG. 7 is a block diagram showing the configuration of a smart device-based exercise amount measurement system according to one embodiment of the present invention.

[0084] A smart device-based exercise volume measurement system (10, hereinafter referred to as 'exercise volume measurement system') according to one embodiment of the present invention includes a smart muscle band (100), a smart dumbbell (200), a Bluetooth gateway (500), and an evaluation system (600). In addition, the exercise volume measurement system (10) may further include one or a combination of a smart wrist band (300), a scale (400), a raw data server (700), a personal information server (800), and an algorithm server (900). The exercise volume measurement system (10) illustrated in FIG. 7 is according to one embodiment, and the components of the exercise volume measurement system (10) according to the present invention are not limited to the embodiment illustrated in FIG. 7, and may be added, changed, or deleted as needed.

[0085] The Bluetooth gateway (500) can receive data transmitted by the smart muscle band (100), smart dumbbell (200), smart wrist band (300), and scale (400) via Bluetooth communication, and transmit the received data to the evaluation system (600) via the cloud.

[0086] The smart muscle band (100) transmits muscle activity, muscle fatigue, strain data of the built-in strain sensor (121), and circumferential length data of the user's body part to the evaluation system (600). The smart dumbbell (200) transmits strain data of the built-in strain sensor (210) or the user's grip strength value to the evaluation system (600). The smart wrist band (300) transmits sensing data measured by the built-in sensor to the evaluation system (600). For example, the smart wrist band (300) can transmit photoplethysmogram (PPG) signals measured by a PPG photoplethysmogram sensor, oxygen saturation data measured by an oxygen saturation sensor, angular velocity data and / or acceleration data measured by an IMU sensor, etc. to the evaluation system (600). The weight scale (400) transmits the user's weight value to the evaluation system (600).

[0087] The evaluation system (600) can store, monitor, and analyze data collected from the smart muscle band (100), smart dumbbell (200), smart wristband (300), and scale (400) via the Bluetooth gateway (500) in internal storage and generate reports. For example, the evaluation system (600) can perform data trend, statistics, and outlier analysis for a set period (daily, weekly, monthly, quarterly, semi-annually, annually, etc.). For example, the evaluation system (600) can perform the above-described analysis by utilizing known statistical techniques (e.g., principal component analysis, kernel density estimation, etc.) or artificial intelligence models (e.g., autoencoder, nearest neighbor, etc.). When the size of raw data, personal information, and algorithms (statistical processing, artificial intelligence models) to be processed by the evaluation system (600) increases, the exercise measurement system (10) may be configured to support the evaluation system (600) by additionally providing a raw data server (700), a personal information server (800), or an algorithm server (900).

[0088]

[0089] FIG. 8 is a block diagram showing the configuration of a smart dumbbell according to one embodiment of the present invention, and FIG. 9 is an exemplary drawing of a smart dumbbell according to one embodiment of the present invention.

[0090] A smart dumbbell (200) according to one embodiment of the present invention includes a strain sensor (210), an amplifier (220), an A / D converter (230), a processor (240), and a communication module (250). The communication module (250) includes a Bluetooth module. The smart dumbbell (200) illustrated in FIG. 8 is according to one embodiment, and the components of the smart dumbbell (200) according to the present invention are not limited to the embodiment illustrated in FIG. 8, and may be added, changed, or deleted as needed.

[0091] The strain sensor (210) generates a strain sensing signal (analog signal) when the user grips the smart dumbbell (200). The amplifier (220) amplifies the strain sensing signal. The A / D converter (230) converts the amplified strain sensing signal into a digital strain sensing signal.

[0092] For example, the processor (240) can input the digital strain sensing signal into a learned neural network-based grip strength prediction model to generate grip strength data, and then transmit the data to the evaluation system (600) through a communication module (250) and a Bluetooth gateway (500).

[0093] As another example, the processor (240) may transmit the digital strain sensing signal to the evaluation system (600) via the communication module (250) and the Bluetooth gateway (500). In this case, the evaluation system (600) may input the received digital strain sensing signal into the grip strength prediction model to generate grip strength data.

[0094]

[0095] Fig. 10 is a diagram for explaining the learning process of the grip strength prediction model used in the smart dumbbell, and Fig. 11 is a diagram for explaining the inference process of the grip strength prediction model used in the smart dumbbell.

[0096] The grip strength prediction model described above may be configured as a neural network. The grip strength prediction model is a model that receives a digital strain sensing signal (same meaning as the "strain sensor value" in FIG. 10) as input and generates grip strength data. The grip strength prediction model learns weight parameters in a direction that minimizes a predetermined cost function using the grip strength values ​​measured by an electronic grip dynamometer as labels. The cost function may be a cross entropy function.

[0097]

[0098] Figure 12 is a diagram showing the predicted grip strength value using a grip strength prediction model and the measured grip strength value using an electronic grip dynamometer. Figure 12 shows a case where the grip strength value measured by the electronic grip dynamometer is 21 kg, and the predicted grip strength value (grip strength data) inferred by the grip strength prediction model, as in Figure 11, is 21.41 kg.

[0099]

[0100] FIG. 13 is a flowchart for explaining an operation method of a smart device-based exercise measurement system according to one embodiment of the present invention.

[0101] As illustrated in FIG. 13, the method for operating a smart device-based exercise quantity measurement system according to one embodiment of the present invention comprises steps S910 to S928. The method for operating a smart device-based exercise quantity measurement system illustrated in FIG. 13 is according to one embodiment, and the steps of the method for operating a smart device-based exercise quantity measurement system according to the present invention are not limited to the embodiment illustrated in FIG. 13, and may be added, changed, or deleted as needed.

[0102] As illustrated in FIG. 13, steps S910 to S914, steps S916 to S920, steps S922 to S924, and steps S926 to S928 can be performed in parallel with each other.

[0103] Step S910 is an electromyography signal acquisition step. The conductive pad (110) of the smart muscle band (100) uses an electromyography measuring electrode (111) to acquire an electromyography signal generated from the muscles of a specific body part (target body part) of the user.

[0104] Step S912 is the muscle fatigue calculation and collection step. The conductive pad (110) of the smart muscle band (100) can calculate the muscle fatigue of the muscle by analyzing the electromyography signal using FFT (Fast Fourier Transform) and spectrum analysis. The communication device (114) built into the conductive pad (110) transmits the calculated muscle fatigue to the evaluation system (600) via the Bluetooth gateway (500), and the evaluation system (600) collects and stores the muscle fatigue data.

[0105] Step S914 is a stimulation voltage application step. The conductive pad (110) determines the stimulation voltage to be applied to the muscle based on muscle fatigue, and applies the determined stimulation voltage to the muscle using the muscle stimulation electrode (112).

[0106] Step S916 is a step for acquiring strain data of a smart muscle band (100). The band portion (120) included in the smart muscle band (100) transmits strain data of a built-in strain sensor (121) to a conductive pad (110).

[0107] The conductive pad (110) calculates muscle activity and the number of movements based on the strain data, and transmits the strain data, muscle activity, and the number of movements data to the evaluation system (600) via the Bluetooth gateway (500) (S918, S920).

[0108] Step S922 is a step for predicting the circumference of the target body part. The smart muscle band (100) calculates the basic circumference length of the target body part through the basic circumference measurement circuit (122), and corrects the basic length based on the strain data to predict the final circumference length of the target body part.

[0109] Step S924 is a step for calculating and collecting muscle thickness prediction values. The conductive pad (110) of the smart muscle band (100) calculates a muscle thickness prediction value of the target body part based on the final circumference length and transmits it to the evaluation system (600).

[0110] Step S926 is the step of acquiring a strain sensing signal from the smart dumbbell. The smart dumbbell (200) generates a strain sensing signal through a built-in strain sensor (210) and generates a digital strain sensing signal through amplification and digital conversion.

[0111] Step S928 is a grip strength prediction and collection step. The processor (240) of the smart dumbbell (200) inputs the digital strain sensing signal into a pre-learned grip strength prediction model to generate grip strength data, and then transmits the data to the evaluation system (600) via the Bluetooth gateway (500).

[0112] Step S930 is the exercise information monitoring step. The evaluation system (600) monitors the trends and occurrence of outliers in exercise information (muscle fatigue, strain data, muscle activity, number of exercises, muscle thickness prediction values, grip strength data, etc.) collected from the smart muscle band (100) and smart dumbbells (200).

[0113] In the description with reference to FIG. 13, each step may be further divided into additional steps or combined into fewer steps, depending on the implementation of the present invention. Furthermore, some steps may be omitted as needed, and the order of steps may be changed. Furthermore, even if other details are omitted, the contents of FIGS. 1 through 12 may be applied to the contents of FIG. 13. Furthermore, the contents of FIG. 13 may be applied to the contents of FIGS. 1 through 12.

[0114]

[0115] Figure 14 is a block diagram showing the configuration of an evaluation system according to one embodiment of the present invention.

[0116] An evaluation system (600) according to one embodiment of the present invention can be implemented in the form of a computer system (1000) illustrated in FIG. 14.

[0117] Additionally, the raw data server (700), personal information server (800), and algorithm server (900) can also be implemented in the form of the computer system (1000) of FIG. 14.

[0118] Referring to FIG. 14, a computer system (1000) may include at least one of a processor (1010), a memory (1030), an input interface device (1050), an output interface device (1060), and a storage device (1040) that communicate via a bus (1070). The computer system (1000) may further include a communication device (1020) coupled to a network. The processor (1010) may be a central processing unit (CPU), or a semiconductor device that executes instructions stored in the memory (1030) or the storage device (1040). The memory (1030) and the storage device (1040) may include various forms of volatile or non-volatile storage media. For example, the memory may include a read-only memory (ROM) and a random access memory (RAM). In embodiments of the present disclosure, the memory may be located internally or externally to the processor, and the memory may be connected to the processor via various known means. Memory is a variety of volatile or non-volatile storage media, and may include, for example, read-only memory (ROM) or random access memory (RAM).

[0119] Accordingly, embodiments of the present invention may be implemented as a computer-implemented method or as a non-transitory computer-readable medium storing computer-executable instructions. In one embodiment, when executed by a processor, the computer-readable instructions may perform a method according to at least one aspect of the present disclosure.

[0120] The communication device (1020) can transmit or receive wired or wireless signals.

[0121] In addition, the method according to the embodiment of the present invention may be implemented in the form of program commands that can be executed through various computer means and recorded in a computer-readable medium.

[0122] The computer-readable medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable medium may be specially designed and configured for embodiments of the present invention, or may be known and usable by those skilled in the art of computer software. The computer-readable recording medium may include a hardware device configured to store and execute the program commands. For example, the computer-readable recording medium may be a magnetic medium such as a hard disk, a floppy disk, and a magnetic tape, an optical medium such as a CD-ROM or a DVD, a magneto-optical medium such as a floptical disk, a ROM, a RAM, a flash memory, etc. The program commands may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer through an interpreter, etc.

[0123] Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

[0124]

[0125] [Explanation of symbols]

[0126] 100, 100': Smart Muscle Band

[0127] 110: Conductive pad

[0128] 111: Electromyography electrodes

[0129] 112: Electrodes for muscle stimulation

[0130] 113: Controller

[0131] 114, 114': Communication device

[0132] 115: Fixed part

[0133] 120: Band Club

[0134] 121: Strain sensor

[0135] 122: Basic circumference measurement circuit

[0136] 122-1: Metal button

[0137] 122-2: Female metal button

[0138] 122-3: Resistance

[0139] 123: New construction

[0140] 200: Smart Dumbbell

[0141] 210: Strain sensor

[0142] 220: Amplifier

[0143] 230: A / D converter

[0144] 240: Processor

[0145] 250: Communication module

[0146] 300: Smart Wristband

[0147] 400: Scale

[0148] 500: Bluetooth Gateway

[0149] 600: Evaluation System

[0150] 700: Data Server

[0151] 800: Personal Information Server

[0152] 900: Algorithm Server

Claims

1. In a smart dumbbell that can measure the user's exercise volume, A strain sensor that generates a strain sensing signal when the user holds the smart dumbbell; An amplifier that amplifies the above strain sensing signal; An A / D converter that converts the amplified strain sensing signal into a digital strain sensing signal; and A processor that inputs the above digital strain sensing signal into a learned neural network-based grip strength prediction model to generate grip strength data; Smart dumbbells including.

2. In paragraph 1, A communication module that transmits the above-mentioned evil force data to an external evaluation system; Smart dumbbells that include more.

3. In the first paragraph, the evil force prediction model is, Input grip strength data from the above smart dumbbell, Label the grip strength value measured by the electronic dynamometer, With the cross entropy function as the cost function, Weight parameters are learned in the direction of minimizing the above cost function. In Smart Dumbbell.

4. A smart muscle band that is worn on a specific body part of a user, acquires electromyography signals generated from muscles of said body part using built-in electromyography measuring electrodes, and calculates muscle fatigue and muscle activity of said muscles based on said electromyography signals; A smart dumbbell that generates a strain sensing signal through a built-in first strain sensor when the user holds it, and inputs the strain sensing signal into a pre-trained neural network-based grip force prediction model to generate grip force data; and An evaluation system that stores and monitors the above muscle fatigue, muscle activity and grip strength data in an internal storage; A smart device-based exercise measurement system including:

5. In paragraph 4, A smart device-based exercise volume measurement system further comprising a Bluetooth gateway that receives muscle fatigue and muscle activity data from the smart muscle band via Bluetooth communication, receives grip strength data from the smart dumbbell via Bluetooth communication, and transmits the muscle fatigue, muscle activity, and grip strength data to the evaluation system via the cloud.

6. In the fourth paragraph, the smart muscle band, Obtaining strain data through the built-in second strain sensor, calculating the number of movements of the muscle based on the strain data, and transmitting the calculated number of movements of the muscle to the evaluation system. A smart device-based exercise measurement system.

7. A step of a smart muscle band mounted on a specific body part of a user, obtaining electromyography signals generated from muscles of said body part using built-in electromyography measuring electrodes, and calculating muscle fatigue and muscle activity of said muscles based on said electromyography signals; When the user holds the smart dumbbell, the smart dumbbell generates a strain sensing signal through the built-in first strain sensor, and inputs the strain sensing signal into a pre-learned neural network-based grip strength prediction model to generate grip strength data; and A step in which the evaluation system stores and monitors the muscle fatigue, muscle activity and grip strength data in an internal storage; A method of operating a smart device-based exercise measurement system including:

8. In paragraph 7, The smart muscle band obtains strain data through a built-in second strain sensor and calculates the number of times the muscle is exercised based on the strain data; and A method of operating a smart device-based exercise measurement system, wherein the evaluation system further includes a step of storing and monitoring the number of times the muscle is exercised in an internal storage.

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