TWI789980B - Muscle quantification device, method, and system - Google Patents

Abstract

A muscle quantification device, method, and system are disclosed. The muscle quntifiaction method includes: attaching a muscle quantifing device to a human body; after the human body performs an action, an inertial signal measuring unit of the muscle quentifiaction deivce obtaining a motion parameter after computation according to the action, a muscle strength parameter after computation according to an electromyogram signal, and a muscle mass parameter after computation according to an electrical impedance signal; using an artificial intelligence model or a computer to evaluate the influence of a body quantity parameter, the motion parameter, the muscle strength parameter, and the muscle mass parameter on the muscle strength of the human body, thereby obtaining an evaluation parameter reflecting the muscle strength of the human body. According to the present disclosure, the muscle strength of the human body can be evaluated more accurately.

Description

Muscle strength assessment device, method and system

The present invention relates to a muscle strength evaluation device, and a muscle strength evaluation method and system using the muscle strength evaluation device.

Irwin Rosenberg pointed out in 1989 that the muscle mass and size of people's skeletal muscles will decrease with age, and proposed the term Sarcopenia. In terms of the diagnosis of sarcopenia, the easiest way is to use a gripper. It is generally believed that when the grip strength of men is less than 26 kg and the grip strength of women is less than 18 kg, it is possible to be diagnosed with sarcopenia. However, only relying on a single gripper is still insufficient for judging muscle strength.

Therefore, Chinese Patent No. CN 108209912 B provides a method and device for collecting electromyographic signals. The method includes: obtaining the type of action that the user is about to perform; inputting the type of action into a data analysis model to determine the Acquisition frequency matching the type of action; controlling the acquisition module in the wearable device to collect myoelectric signals from a preset position of the human body at the acquisition frequency. The aforementioned patent case can determine a kind of myoelectric signal acquisition frequency that is in line with the current actual exercise situation by knowing the type of action that the user should currently perform, so that the acquisition frequency of the wearable device can be associated with the user's muscle activity state at the current moment , so that the acquisition module in the wearable device no longer relies on a single acquisition mode to acquire EMG signals, but Different acquisition frequencies can be used in a targeted manner, thus improving the effectiveness and accuracy of EMG signal acquisition.

However, in the aforementioned patent case, it is still only relying on a single EMG signal to judge the state of muscle activity, which is less accurate in judging muscle strength.

Therefore, the inventor proposes a muscle strength assessment method, comprising the following steps: input a body measurement parameter of a human body to a computer, the computer includes or is signally connected to an artificial intelligence model trained in advance; combines at least one muscle in the human body A force evaluation device, the human body performs an action; an inertial signal measurement unit of the muscle strength evaluation device obtains an action parameter corresponding to the human body from the action through a first calculation circuit unit, and transmits the action parameter to The computer; a bioelectrical impedance measurement unit of the muscle strength evaluation device obtains an electrical impedance signal of the human body through an electrode unit, and then obtains the whole body or part of the human body through a second operation circuit unit. A muscle mass parameter is sent to the computer; a myoelectric signal measurement unit of the muscle strength evaluation device obtains a myoelectric signal of the human body through the electrode unit, and then obtains the myoelectric signal through a third operation circuit unit A muscle strength parameter of the human body is transmitted to the computer; the computer or the artificial intelligence model evaluates the influence of the body measurement parameter, the movement parameter, the muscle strength parameter and the muscle mass parameter on the muscle strength of the human body, and then An evaluation parameter of the muscle strength of the human body is obtained.

Further, a training body measurement parameter, a training action parameter, a training muscle strength parameter, a training muscle mass parameter and a clinical evaluation result are input to the computer or a server in advance, and the computer or the server performs deep learning to According to the clinical evaluation result, the influence of the training body measurement parameter, the training action parameter, the training muscle strength parameter and the training muscle mass parameter on the muscle strength of the human body is evaluated, and the artificial intelligence model is established.

Further, when the human body performs the action, the computer displays an action situation of the human body, and when the human body completes the action, the computer and/or the muscle strength evaluation device sends a prompt.

Wherein, the body measurement parameter includes one or a combination of age, gender, height, weight and body shape, the action parameter includes one or a combination of the position, acceleration, rotation, distance and time of the action, and the muscle strength parameter is In terms of domain, it includes one or a combination of root mean square value, integrated EMG value and average amplitude; in frequency domain, it includes average power frequency and/or median frequency, and the muscle mass parameter includes the body mass index of the human body , body fat percentage, body fat mass, muscle mass, body water content, body water percentage, intracellular fluid, segmental muscle mass, segmental body fat percentage, segmental body fat mass, bioelectrical resistance value, and bioelectrical reactance value or a combination thereof.

The inventor also provides a muscle strength evaluation device, which is combined with a human body. The muscle strength evaluation device includes: a casing; an inertial signal measurement unit, which is arranged on the casing, and the inertial signal measurement unit is used to obtain the A six-axis information of the human body when performing an action; an electrode unit, including a plurality of built-in electrodes, and the built-in electrodes are arranged on the casing and contact the human body; a bioelectrical impedance measurement unit, arranged on the casing and electrically connected to the electrode unit, the bioelectrical impedance measurement unit obtains an electrical impedance signal of the human body through the electrode unit; a myoelectric signal measurement unit is arranged in the housing and electrically connected to the electrode unit, the The electromyographic signal measurement unit obtains the electromyographic signal of the human body through the electrode unit; a communication unit is arranged on the casing, and the communication unit is connected to the inertial signal measurement unit, the bioelectrical impedance measurement unit and the a myoelectric signal measurement unit to transmit the six-axis information, the myoelectric signal and the electrical impedance signal; and a power supply unit arranged in the housing, the power supply unit electrically connected to the inertial signal measurement unit, the biological An electrical impedance measurement unit, the myoelectric signal measurement unit and the communication unit.

Further, a prompt unit is arranged on the casing, and the prompt unit is electrically connected to the power supply unit.

Further, a connection port is provided on the housing, the electrode unit includes a plurality of external electrodes to contact the human body, the bioelectrical impedance measurement unit is connected to the connection port, the external electrodes are electrically connected to the connection port, and the bioelectrical impedance measurement unit is connected to the connection port. The impedance measurement unit obtains the electrical impedance signal through the external electrode; when the external electrode contacts a wrist of the human body, the external electrode is set at the center of an ulnar head of the human body; When an ankle of a human body is used, the external electrode is arranged at the center of an inner ankle of the human body.

Further, a connection port is provided on the housing, the electrode unit includes a plurality of external electrodes to contact the human body, the bioelectrical impedance measurement unit is connected to the connection port, the external electrodes are electrically connected to the connection port, and the bioelectrical impedance measurement unit is connected to the connection port. The impedance measurement unit obtains the electrical impedance signal through the external electrode; when the external electrode is in the form of a cylinder, the human body touches the external electrode by hand; when the external electrode is in the form of a mat , the human body touches the external electrode in a pedal manner.

The inventor further provides a muscle strength assessment system for measuring a human body. The muscle strength assessment system includes: a computer, which contains or is connected to an artificial intelligence model trained in advance; at least one muscle strength assessment device, combined with In the human body, the signal of the muscle strength evaluation device is connected to the computer, and the muscle strength evaluation device has an inertial signal measurement unit, a bioelectrical impedance measurement unit, a myoelectric signal measurement unit and an electrode unit. The electrical impedance measurement unit and the myoelectric signal measurement unit are respectively electrically connected to the electrode unit, and the electrode unit is in contact with the human body; a first computing circuit unit is signal-connected to the computer and the inertial signal measurement unit; a first Two computing circuit units, the signal is connected to the computer and the bioelectrical impedance measurement unit; and a third computing circuit unit, the signal is connected The computer and the electromyographic signal measurement unit; input a body measurement parameter of the human body to the computer, and the human body performs an action; the inertial signal measurement unit is obtained from the action through the operation of the first computing circuit unit An action parameter corresponds to the human body, and the action parameter is sent to the computer; the bioelectrical impedance measurement unit obtains an electrical impedance signal of the human body through the electrode unit, and then calculates and obtains the human body through the second operation circuit unit A muscle mass parameter of the whole body or part of the section is sent to the computer; the electromyographic signal measurement unit obtains the electromyographic signal of the human body through the electrode unit, and then obtains the electromyographic signal of the human body through the operation of the third computing circuit unit A muscle strength parameter is transmitted to the computer; the computer or the artificial intelligence model evaluates the influence of the body measurement parameter, the movement parameter, the muscle strength parameter and the muscle mass parameter on the muscle strength of the human body, and then obtains the An evaluation parameter of the muscle strength of the human body.

Further, a fastener is combined with the muscle strength evaluation device, so that the electrode unit fits on the human body.

Further, the first arithmetic circuit unit includes a microcontroller, and the inertial signal measurement unit is an inertial sensing chip, and the microcontroller signal is connected to the inertial signal measurement unit; through the inertial signal measurement unit, one six axis information, and input the six-axis information into the microcontroller for differential calculation to obtain the action parameters.

Further, the second computing circuit unit includes a multiplexer circuit signal connected to the bioelectrical impedance measurement unit, a voltage-controlled current source circuit signal connected to the multiplexer circuit, and a digital analog circuit signal connected to the voltage-controlled current source circuit , an instrumentation amplifier circuit signal is connected to the multiplexer circuit, a filter and automatic gain control circuit signal is connected to the instrumentation amplifier circuit, a modulator signal is connected to the filter and automatic gain control circuit, and a microcontroller signal is connected to the adjustment Transformer and the digital analog; through the microcontroller to control the digital analog to generate a sine wave, the voltage control The control current source circuit then outputs a current to the multiplexer circuit according to the sine wave, and selectively introduces it into the human body through the bioelectrical impedance measurement unit and the electrode unit, and the electrical impedance signal fed back from the human body is obtained from the The multiplexer circuit obtains an electrical impedance value through the instrumentation amplifier circuit, the filter and automatic gain control circuit, and the modulator signal processing, and then inputs it into the microcontroller for analysis to obtain the muscle mass parameter.

Further, the second computing circuit unit includes a multiplexer circuit signally connected to the bioelectrical impedance measurement unit, an integrated circuit chip signally connected to the multiplexer circuit, and a microcontroller signally connected to the integrated circuit chip; The multiplexer circuit is controlled by the microcontroller to selectively introduce a current into the human body through the bioelectrical impedance measurement unit and the electrode unit, and the electrical impedance signal fed back from the human body is transmitted from the multiplexer The circuit is processed by the integrated circuit chip to obtain an electrical impedance value, which is then input to the microcontroller for analysis to obtain the muscle mass parameter.

Further, the third operational circuit unit includes an amplifier signal connected to the electromyographic signal measurement unit, a high/low pass filter signal connected to the amplifier, an adjustable gain and translation circuit signal connected to the high/low pass filter, And a microcontroller signal is connected to the adjustable gain and translation circuit; the electromyographic signal measurement unit passes the electromyographic signal through the amplifier, the high/low pass filter and the adjustable gain and translation circuit through the electrode unit After the signal is processed, it is input to the microcontroller for filtering, calculation and analysis, so as to obtain the muscle strength parameter of the human body.

According to the above-mentioned technical characteristics, the following effects can be preferably achieved:

1. With the help of body measurement parameters, movement parameters, muscle strength parameters and muscle mass parameters, the evaluation parameters of muscle strength are obtained by computer or artificial intelligence model, so as to avoid the inaccuracy of a single parameter and improve the accuracy of muscle strength evaluation. Save manpower and time of doctors.

2. When the movement is performed, the computer displays the movement situation, or the computer and/or the muscle strength evaluation device sends out a prompt when the movement is completed, making it easier for the user to operate.

3. The muscle strength assessment device can be directly tied to the human body with fasteners to allow the built-in electrodes to contact the human body, or to contact the human body through external electrodes, which is convenient for users to choose according to their needs.

4. Not only the assessment of sarcopenia, athletes, the elderly, and even ordinary people can quickly use the muscle strength assessment method to assess their own muscle strength.

5. The computer can be connected to the server to obtain the evaluation parameters of muscle strength, or the evaluation parameters can be obtained directly on the computer in a stand-alone mode, without having to connect to the server every time.

6. Integrating the inertial signal measurement unit, bioelectrical impedance measurement unit and myoelectric signal measurement unit in the same muscle strength evaluation device not only simplifies the measurement steps, reduces the overall system volume, but also greatly improves the efficiency of muscle strength evaluation convenience.

1,1a: Muscle strength assessment device

11: Housing

12: Inertial signal measurement unit

13: Port

14,14a: Electrode unit

141: Built-in electrode

1411: EMG electrode

1412: Reference electrode

142: External electrode

1421a: the first external electrode

1422a: second external electrode

15: Communication unit

16: Power supply unit

17: Prompt unit

18,18a: Bioelectrical impedance measurement unit

19: EMG measurement unit

2: Fasteners

3: computer

4: Server

41:Artificial intelligence model

42: Database

5: The first arithmetic circuit unit

51,51a: microcontroller

6,6a: The second arithmetic circuit unit

61,61a: multiplexer

62: Voltage controlled current source circuit

63:Digital Analog Device

64: Instrumentation amplifier circuit

65: Filtering and automatic gain control circuit

66:Modulator

67a: Integrated circuit chip

7: The third arithmetic circuit unit

71: Amplifier

72: High/Low Pass Filter

73: Adjustable gain and translation circuit

A: Human body

B: Body Measurement Parameters

C: action parameters

D: Muscle mass parameters

E: Muscle strength parameters

F: Evaluation parameters

[The first figure] is a three-dimensional appearance view of the muscle strength evaluation device according to the first embodiment of the present invention.

[The second figure] is a three-dimensional appearance view of the muscle strength evaluation device of the first embodiment of the present invention in another direction.

[The third figure] is the implementation schematic diagram 1 of the first embodiment of the present invention, showing that the muscle strength evaluation device is combined with the human body by fasteners.

[Figure 4] is the second implementation schematic diagram of the first embodiment of the present invention, showing that the external electrodes of the muscle strength assessment device are attached to the human body.

[FIG. 5] is the system block diagram 1 of the first embodiment of the present invention, showing the main signal connection relationship of the muscle strength evaluation system.

[Figure 6] is the second system block diagram of the first embodiment of the present invention, showing the first computing circuit unit, the second computing circuit unit and the third computing circuit unit.

[Figure 7] is a schematic flow chart of the first embodiment of the present invention, illustrating a method for evaluating muscle strength.

[Figure 8] is a functional block diagram of the first embodiment of the present invention, showing a muscle strength assessment method.

[Figure 9] is a broken-line schematic diagram of the first embodiment of the present invention, showing the evaluation parameters of muscle strength.

[Figure 10] is the implementation schematic diagram 1 of the second embodiment of the present invention, which shows that the human body touches the external electrode by holding it by hand and pedaling it.

[Figure 11] is the implementation schematic diagram 1 of the second embodiment of the present invention, showing the external electrodes.

[Figure 12] is a system block diagram of the second embodiment of the present invention, showing the second computing circuit unit.

Based on the above technical features, the main functions of the muscle strength assessment device, method and system of the present invention will be clearly presented in the following embodiments.

Please refer to the first and second figures, which disclose the first embodiment of the muscle strength assessment system of the present invention, which can be used to perform a muscle strength assessment method on a human body A [please match the human body A with the third figure].

The muscle strength evaluation system includes: at least one muscle strength evaluation device 1, combined with the human body A, the muscle strength evaluation device 1 includes: a housing 11, an inertial signal measurement unit 12, a connection port 13, an electrode Unit 14, One Communication unit 15, a power supply unit 16, a prompt unit 17, a bioelectrical impedance measurement unit 18 and a myoelectric signal measurement unit 19 [the bioelectrical impedance measurement unit 18 and the myoelectric signal measurement unit 19 please Match the fifth picture]. The inertial signal measurement unit 12 is an inertial sensing chip, and its model can be, for example, MPU-9520, which is not limited in actual implementation.

The inertial signal measurement unit 12, the connection port 13, the communication unit 15, the power supply unit 16, the prompt unit 17, the bioelectrical impedance measurement unit 18 and the myoelectric signal measurement unit 19 are all arranged in the shell body 11, the communication unit 15 is signally connected to the inertial signal measurement unit 12, the connection port 13, the prompt unit 17, the bioelectrical impedance measurement unit 18 and the myoelectric signal measurement unit 19. The power supply unit 16 is electrically connected to the inertial signal measurement unit 12, the communication unit 15, the prompt unit 17, the bioelectrical impedance measurement unit 18 and the myoelectric signal measurement unit 19. The unit 18 and the EMG measurement unit 19 are electrically connected to the electrode unit 14 respectively.

The electrode unit 14 actually includes a plurality of built-in electrodes 141 and a plurality of external electrodes 142 to contact the human body A [please match the external electrodes 142 with the fourth figure], the built-in electrodes 141 are arranged on the surface of the housing 11 and The signal is connected to the communication unit 15, the bioelectrical impedance measurement unit 18 and the myoelectric signal measurement unit 19, and one end of the external electrode 142 is in contact with the human body A, and the other end is electrically connected to the connection port 13 through a wire. The communication unit 15 , the bioelectrical impedance measurement unit 18 and the myoelectric signal measurement unit 19 can be connected by signal. In a preferred embodiment of the present invention, the built-in electrodes 141 are divided into two EMG electrodes 1411 and a reference electrode 1412 .

Please refer to the third and fourth figures, the muscle strength assessment system includes a fastener 2, such as a belt combined with Velcro and the like. When the muscle strength assessment device 1 is in contact with the human body A with the built-in electrodes 141, the muscle strength assessment device 1 can make the built-in electrodes 141 close by the fastener 2. Fit the human body A, as shown in the third figure; when the muscle strength assessment device 1 is in contact with the human body A with the external electrode 142, it is not necessary to be combined with the human body A by the fastener 2, as shown in the third figure Four pictures are shown.

For example, when the muscle strength assessment device 1 is in contact with the human body A with the built-in electrodes 141, the number of the muscle strength assessment devices 1 can be four, so as to be combined with the hands and the hands of the human body A respectively. On both feet, or more than two, two of the muscle strength evaluation devices 1 are combined on the same hand of the human body A as shown in the third figure.

When the muscle strength evaluation device 1 is in contact with the human body A with the external electrodes 142, the number of the muscle strength evaluation device 1 can be only one, and through eight external electrodes 142 as shown in the fourth figure Contact the human body A. When the external electrode 142 contacts a wrist of the human body A, the external electrode 142 is arranged at the center of an ulnar head of the human body A; when the external electrode 142 contacts an ankle of the human body A, the external electrode 142 The electrode 142 is disposed at the center of a medial malleolus of the human body A. As shown in FIG.

Please refer to the fifth and sixth figures, the muscle strength evaluation system includes a computer 3, and a server 4, a first computing circuit unit 5, a second computing circuit unit 6 and a The third arithmetic circuit unit 7 .

Input a training body measurement parameter, a training action parameter, a training muscle strength parameter, a training muscle mass parameter and a clinical evaluation result to the computer 3 or the server 4 in advance, the clinical evaluation result is for example from a doctor according to The assessment performed by the subject A.

The computer 3 or the server 4 performs deep learning to evaluate the training anthropometric parameters, the training action parameters, the training muscle strength parameters and the training muscle mass parameters for one muscle of the human body A according to the clinical evaluation results. An artificial intelligence model 41 is established, and the artificial intelligence model 41 and the muscle strength evaluation device 1 are directly or indirectly connected to each other by signals.

In a preferred embodiment of the present invention, the artificial intelligence model 41 is established by the server 4 and stored in the server 4. There is also a database 42 in the server 4, and the computer 3 is connected to the computer 3 with a signal. When the artificial intelligence model 41 and the database 42 are actually implemented, the computer 3 may also create the artificial intelligence model 41 so that the computer 3 includes the artificial intelligence model 41 . In addition, it should be noted that the computer 3 will be used for illustration below. In actual implementation, in addition to general computers, mobile devices can also be used for execution, such as smart devices such as mobile phones and tablets.

The training body measurement parameters include one or a combination of age, gender, height, weight, and body shape; the training action parameters include one or a combination of the position, acceleration, rotation, distance, and time of the action; the training muscle strength parameters are in The time domain includes one or a combination of the root mean square value, the integrated EMG value, and the average amplitude, and the frequency domain includes the average power frequency and/or median frequency. The training muscle mass parameter includes the human body A Body mass index, body fat percentage, body fat mass, muscle mass, body water content, body water percentage, intracellular and extracellular fluid, segmental muscle mass, segmental body fat percentage, segmental body fat mass, bioresistance value and bioresistance One or a combination of values. Preferably, the training anthropometric parameters, the training action parameters, the training muscle strength parameters, the training muscle mass parameters and the clinical evaluation results can be stored in the database 42 .

The algorithm for the computer 3 or the server 4 to perform deep learning to establish the artificial intelligence model 41 can adopt one of the following algorithms: Random Forest algorithm (Random Forest), Support Vector Machines (Support Vector Machines, SVM), K -K Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (eXtreme Gradient Boosting, XGBoost) and Logistic Regression Analysis (Logistic Regression).

The first arithmetic circuit unit 5 includes a microcontroller 51, and the microcontroller 51 can be, for example, a 32-bit one, which is not limited in actual implementation. The microcontroller 51 is signally connected to the inertial signal measuring unit 12 and the computer 3 .

The second arithmetic circuit unit 6 includes a multiplexer 61 signal connected to the bioelectrical impedance measurement unit 18, a voltage control current source circuit 62 signal connected to the multiplexer 61, a digital analog 63 signal connected to the voltage control current Source circuit 62, an instrumentation amplifier circuit 64 signal connection to the multiplexer 61, a filter and automatic gain control circuit 65 signal connection to the instrumentation amplifier circuit 64, a modulator 66 signal connection to the filter and automatic gain control circuit 65, and The microcontroller 51 is signally connected to the modulator 66 , the digital analog 63 and the computer 3 .

The third operational circuit unit 7 includes an amplifier 71 signal connected to the electromyographic signal measurement unit 19, a high/low pass filter 72 signal connected to the amplifier 71, an adjustable gain and translation circuit 73 signal connected to the high/low Pass filter 72, and the microcontroller 51 is connected to the adjustable gain and translation circuit 73 and the computer 3. For example, the gain value of the amplifier 71 may be 2000, and the f _3dB of the high/low pass filter 72 is 2 Hz/500 Hz, which is not limited in actual implementation.

It should be specially supplemented that, in a preferred embodiment of the present invention, the first arithmetic circuit unit 5, the second arithmetic circuit unit 6 and the third arithmetic circuit unit 7 respectively include the same microcontroller 51, In actual implementation, there may also be three independent microcontrollers 51 . The components and circuits can be electrically connected by using an integrated bus circuit (I ² C).

Please refer to the sixth figure to the eighth figure, and please match the first figure and the third figure. When implementing the muscle strength evaluation method, first input a body measurement parameter B of the human body A to the computer 3, the body measurement parameter B contains one or a combination of age, gender, height, weight, and body shape.

Then, the mode to be measured can be selected through the computer 3, and then the muscle strength evaluation device 1 is combined with the human body A. For example, the measurement mode can have an action muscle strength analysis mode, a single-segment body composition measurement mode and a whole-body body composition measurement mode, so as to obtain a movement parameter C, a muscle mass parameter D and a muscle Force parameter E. The following is an example of performing the action muscle strength analysis mode, the single-segment body composition measurement mode and the whole-body body composition measurement mode in sequence. The actual implementation sequence is not limited to this. The single-segment body composition measurement mode and The whole-body body composition measurement mode can also be performed in only one mode.

When the action muscle strength analysis mode is selected, the muscle strength assessment device 1 preferably directly contacts the human body A with the built-in electrode 141 as shown in the third figure, and the human body A performs another action, such as lifting a weight things and so on. The inertial signal measurement unit 12 obtains a six-axis information from the action through the six-axis chips of several accelerators and gyroscopes, and inputs the six-axis information to the microcontroller of the first arithmetic circuit unit 5 through the communication unit 15 The device 51 performs differential calculation to obtain the motion parameter C of each limb of the human body A, and the first computing circuit unit 5 transmits the motion parameter C to the computer 3 . The motion parameter C includes one or a combination of the motion's position, acceleration, rotation, distance, and time.

At the same time, the myoelectric signal measurement unit 19 obtains the myoelectric signal of the human body A through the electrode unit 14, and the myoelectric signal is amplified by the amplifier 71 of the third operation circuit unit 7 through the communication unit 15, and the High/low-pass filter 72 filters, imports the adjustable gain and translation circuit 73 for signal processing to obtain a better myoelectric signal, and then inputs it to the microcontroller 51 for filtering, calculation and analysis, including digital-to-analog conversion , digital IIR filtering, absolute value and integral calculation, and muscle strength analysis such as time domain and frequency domain respectively, and obtain the muscle strength parameter E of the human body A, and the third computing circuit unit 7 then uses the muscle strength parameter E Send to this computer3. The muscle strength parameter E includes in the time domain There is one or a combination of root mean square value, integrated EMG value and average amplitude, and in the frequency domain, it includes average power frequency and/or median frequency.

Please refer to the first picture, the fifth picture and the eighth picture, when the human body A performs the action [please match the human body A with the third picture], the computer 3 can display an action of the human body A, and the computer 3 can also The action situation is stored in the database 42, and when the human body A completes the action, the computer 3 and/or the computer 3 controls the prompt unit 17 to issue a prompt, such as a signal, sound or vibration. biofeedback signal.

Preferably, the computer 3 can preset multiple actions and display them to the human body A. The human body A completes each action in sequence according to the actions displayed by the computer 3, and the computer 3 stores each action. The action context and the action parameter C of the action. In this case, the prompt unit 17 can be a plurality of prompt lights of different colors, for example: when one of the actions is being performed, the prompt unit 17 displays a blue light; , the prompt unit 17 displays a red light; when all the actions are completed, the prompt unit 17 displays a green light, etc., but it is not limited to this in actual implementation.

Please refer to the sixth figure to the eighth figure. When selecting the single-segment body composition measurement mode, in addition to directly contacting the human body A with the built-in electrode 141 as in the third figure, you can also use the fourth figure The external electrode 142 is combined with some sections of the human body A, such as the upper right arm and the lower right arm, the right arm and the right thigh, and so on.

At this time, the second arithmetic circuit unit 6 controls the digital analog 63 to generate a sine wave through the microcontroller 51, and the voltage-controlled current source circuit 62 outputs a current to the multiplexer 61 according to the sine wave to select The formula is introduced into the human body A through the bioelectrical impedance measurement unit 18 and the electrode unit 14 . An electrical impedance signal fed back from the human body A is passed by the bioelectrical impedance measurement unit 18 through the The electrode unit 14 receives, and passes through the communication unit 15 from the multiplexer 61 through the instrumentation amplifier circuit 64, the filter and automatic gain control circuit 65 [the communication unit 15 please match the first figure], and the modulator 66 The signal is processed to obtain an electrical impedance value, which is then input to the microcontroller 51 for analysis to obtain the muscle mass parameter D of the part A of the human body, and the muscle mass parameter D is sent to the computer 3 . The muscle quality parameter D includes the body mass index (BMI), body fat percentage, body fat mass, muscle mass, body water content, body water percentage, intracellular and extracellular fluid, segmental muscle mass, and segmental body fat percentage of the human body A , segmental body fat mass, bioresistance value, and bioresistance value or a combination thereof. According to the combination angle of the electrode unit 14 , the longitudinal or transverse muscle mass parameter D of the human body A can be measured and obtained in subsequent calculations.

When the whole-body composition measurement mode is selected, the external electrode 142 is combined with the limbs of the human body A as in the fourth figure, and the calculation method is the same as that of the single-segment body composition measurement mode. Obtain the muscle mass parameter D of the whole body of the human body A, and send the muscle mass parameter D to the computer 3 .

Please refer to the fifth figure and the eighth figure, the body measurement parameter B, the movement parameter C, the muscle mass parameter D and the muscle strength parameter E can be directly input into the artificial intelligence model 41 through the computer 3, and can also be input into the artificial intelligence model 41 at the same time. The database 42 is stored, and then used as the training body measurement parameters, the training action parameters, the training muscle strength parameters and the training muscle mass parameters, so that the artificial intelligence model 41 is more accurate, and can also be displayed on the computer 3 in real time .

Please refer to the seventh to ninth figures, and please match the following table 1 and the following table 2, after the artificial intelligence model 41 obtains the body measurement parameter B, the movement parameter C, the muscle mass parameter D and the muscle strength parameter E , to evaluate the body measurement parameter B, the movement parameter C, the muscle mass parameter D and the muscle The influence of the force parameter E on the muscle strength of the human body A [please match the human body A with the third picture], and then obtain an evaluation parameter F of the muscle strength of the human body A.

In more detail, the artificial intelligence model 41 first defines the quantitative value of the human body A's completion of the action by the action parameter C, and then the muscle strength parameter E measured when the human body A performs the action, and the enumerated The weight of the lifting object and the muscle mass parameter D of the segment A of the human body or the whole body are comprehensively calculated to obtain the required evaluation parameter F.

Wherein, the muscle mass parameter D and the muscle strength parameter E of the human body A segment or the whole body can be calculated as the muscle strength of the human body A unit muscle, that is, the Y axis of the ninth figure; The weight of the lifted object is used as the X-axis, and then the slopes of the two curve segments (eg S11 and S12) under each level are calculated respectively, and the evaluation parameter F is obtained through calculation.

The computer 3 can transmit the parameters such as the body measurement parameter B, the movement parameter C, the muscle mass parameter D and the muscle strength parameter E to the artificial intelligence model 41, and the artificial intelligence model 41 obtains the evaluation parameter F , or, the computer 3 can also obtain the evaluation parameter F by itself. For example, after executing the muscle strength evaluation method for many times, the computer 3 compares the past and present parameters according to the evaluation parameter F obtained by the artificial intelligence model 41 in the past, and directly finds the corresponding evaluation parameter F, etc. wait. In this way, in addition to connecting the computer 3 to the server 4 to obtain the evaluation parameter F of the muscle strength, the evaluation parameter F can also be directly completed on the computer 3 in a stand-alone manner. It is not necessary to connect to the artificial intelligence model 41 of the server 4 every time.

Please refer to the tenth figure to the twelfth figure, which disclose the second embodiment of the muscle strength evaluation system of the present invention. The biggest difference between this embodiment and the first embodiment is: in the first embodiment [the first embodiment For example, please refer to the fourth figure], the external electrode 142 is in the form of a patch, and contacts the human body A in a pasted manner; in this embodiment, the external electrode can have two different forms, Instead, contact the human body A in a different manner. For the convenience of description, the two types of external electrodes are respectively named as a first external electrode 1421a and a second external electrode 1422a.

The first external electrode 1421a is in the form of a cylinder, such as a cylinder. The human body A can hold the first external electrode 1421a with both hands; the second external electrode 1422a is in the form of a mat, and the human body A can step on it The second external electrode 1422a. In this embodiment, the first external electrode 1421a is used as a positive terminal of voltage and current, and the second external electrode 1422a is used as a negative terminal of voltage and current, which is not limited in actual implementation.

In addition to the difference of the electrode unit 14a of the muscle strength evaluation device 1a described above, in this embodiment, the voltage-controlled current source circuit 62, the digital analog 63, the instrumentation amplifier circuit 64, the filter And the automatic gain control circuit 65 and the modulator 66 [the above components please match the sixth figure] are integrated into an integrated circuit chip 67a.

Similar to the first embodiment, the second arithmetic circuit unit 6a controls the multiplexer circuit 61a through the microcontroller 51a, and selectively introduces the current through the bioelectrical impedance measurement unit 18a and the electrode unit 14a The human body A, the electrical impedance signal fed back from the human body A is processed from the multiplexer circuit 61a through the integrated circuit chip 67a to obtain the electrical impedance value, and then input to the microcontroller 51a for analysis to obtain the muscle mass Parameter D [Please match the muscle mass parameter D with the sixth picture].

However, the remaining structures and actions of this embodiment are the same as or correspond to those of the first embodiment, and will not be repeated here. In actual implementation, the elements of the first embodiment and this embodiment can also be matched with each other, for example, the external electrode 142 of the first embodiment is used to match the integrated circuit chip 67a of this embodiment, and so on.

Please refer to the eighth figure again, in the first embodiment, the artificial intelligence model is used by the body measurement parameter B, the movement parameter C, the muscle mass parameter D and the muscle strength parameter E of the segment or the whole body 41 Obtain the evaluation parameter F of the muscle strength, avoid the inaccuracy of a single parameter, and save the manpower and time of doctors while improving the accuracy of muscle strength evaluation.

Not only for the assessment of sarcopenia, athletes, the elderly, and even ordinary people can quickly use this muscle strength assessment method to assess their own muscle strength.

In addition, by integrating the inertial signal measurement unit 12, the bioelectrical impedance measurement unit 18, and the myoelectric signal measurement unit 19 into the same muscle strength evaluation device 1, not only the measurement steps, reduce the overall system volume, and greatly improve the convenience of muscle strength assessment.

Based on the description of the above-mentioned embodiments, it is possible to fully understand the operation of the present invention, use and the effect that the present invention produces, but the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be used to limit the implementation of the present invention. The scope, that is, the simple equivalent changes and modifications made according to the patent scope of the present invention and the content of the description of the invention, all fall within the scope of the present invention.

1: Muscle strength assessment device

12: Inertial signal measurement unit

14: Electrode unit

18: Bioelectrical impedance measurement unit

19: EMG measurement unit

3: computer

4: Server

41:Artificial intelligence model

42: Database

5: The first arithmetic circuit unit

6: The second arithmetic circuit unit

7: The third arithmetic circuit unit

B: Body Measurement Parameters

C: action parameter

D: Muscle mass parameters

E: Muscle strength parameters

F: Evaluation parameters

Claims (14)

A muscle strength assessment method, comprising the following steps: input a body measurement parameter of a human body to a computer, the computer contains or is signal-connected to an artificial intelligence model trained in advance; combining at least one muscle strength assessment device on the human body, the human body executes An action; an inertial signal measurement unit of the muscle strength evaluation device obtains an action parameter corresponding to the human body through the operation of a first computing circuit unit, and transmits the action parameter to the computer; the muscle A bioelectrical impedance measurement unit of the force evaluation device obtains an electrical impedance signal of the human body through an electrode unit, and then obtains a muscle mass parameter of the whole body or part of the human body through a second operation circuit unit and transmits it to the computer; a myoelectric signal measurement unit of the muscle strength evaluation device obtains a myoelectric signal of the human body through the electrode unit, and then obtains a muscle strength parameter of the human body through a third operation circuit unit and sent to the computer; the computer or the artificial intelligence model evaluates the influence of the body measurement parameter, the movement parameter, the muscle mass parameter and the muscle strength parameter on the muscle strength of the human body, and then obtains the muscle strength of the human body. An evaluation parameter of force magnitude. The muscle strength evaluation method as described in claim 1, further, input a training body measurement parameter, a training action parameter, a training muscle strength parameter, a training muscle quality parameter and a clinical evaluation result to the computer or a server in advance , the computer or the server performs in-depth learning to evaluate the effect of the training anthropometric parameters, the training action parameters, the training muscle strength parameters and the training muscle mass parameters on the human body's muscle strength according to the clinical evaluation results Influence, and build the artificial intelligence model. In the muscle strength assessment method described in claim 1, further, when the human body performs the action, the computer displays a movement situation of the human body, and when the human body completes the action, the computer and/or the muscle The force evaluation device issues a prompt. The muscle strength assessment method according to Claim 1, wherein the body measurement parameters include one or a combination of age, gender, height, weight, and body shape, and the action parameters include the position, acceleration, rotation, distance, and time of the action One or a combination thereof, the muscle strength parameters include one or a combination of root mean square value, integrated EMG value and average amplitude in the time domain, and include the average power frequency and/or median frequency in the frequency domain , the muscle mass parameters include body mass index, body fat percentage, body fat mass, muscle mass, body water content, body water percentage, extracellular fluid, segmental muscle mass, segmental body fat percentage, segmental body One or a combination of fat mass, bioresistance value and bioresistance value. A muscle strength evaluation device, combined with a human body, the muscle strength evaluation device includes: a housing; an inertial signal measurement unit, set in the housing, the inertial signal measurement unit is used to obtain the human body performing an action A six-axis information at the same time; an electrode unit, including a plurality of built-in electrodes, the built-in electrodes are set in the casing and contact the human body; a bioelectrical impedance measurement unit, set in the casing and electrically connected to the An electrode unit, the bioelectrical impedance measurement unit obtains an electrical impedance signal of the human body through the electrode unit; a myoelectric signal measurement unit is arranged on the housing and electrically connected to the electrode unit, and the myoelectric signal measurement The unit obtains a myoelectric signal of the human body through the electrode unit; a communication unit is arranged in the casing, and the communication unit is connected to the inertial signal measurement unit, the bioelectrical impedance measurement unit and the myoelectric signal measurement unit. a unit for transmitting the six-axis information, the myoelectric signal and the electrical impedance signal; and A power supply unit is arranged on the casing, and the power supply unit is electrically connected to the inertial signal measurement unit, the bioelectrical impedance measurement unit, the myoelectric signal measurement unit and the communication unit. As for the muscle strength evaluation device described in claim 5, further, a prompt unit is disposed on the casing, and the prompt unit is electrically connected to the power supply unit. The muscle strength evaluation device as described in claim 5, further, a connection port is arranged on the casing, the electrode unit includes a plurality of external electrodes to contact the human body, the bioelectrical impedance measurement unit is connected to the connection port, and the The external electrode is electrically connected to the connection port, and the bioelectrical impedance measurement unit obtains the electrical impedance signal through the external electrode; when the external electrode contacts a wrist of the human body, the external electrode is arranged on a wrist of the human body. The center of the ulnar head; when the external electrode contacts an ankle of the human body, the external electrode is arranged at the center of an inner malleolus of the human body. The muscle strength evaluation device as described in claim 5, further, a connection port is arranged on the casing, the electrode unit includes a plurality of external electrodes to contact the human body, the bioelectrical impedance measurement unit is connected to the connection port, and the The external electrode is electrically connected to the connection port, and the bioelectrical impedance measurement unit obtains the electrical impedance signal through the external electrode; when the external electrode is in the form of a cylinder, the human body touches the external electrode by hand Electrodes; when the external electrodes are in the form of stepping pads, the human body touches the external electrodes in the manner of pedaling. A muscle strength evaluation system is used to measure a human body, the muscle strength evaluation system includes: a computer, the computer contains or a signal connection with an artificial intelligence model trained in advance; at least one muscle strength evaluation device, combined with the human body, the The signal of the muscle strength evaluation device is connected to the computer. The muscle strength evaluation device has an inertial signal measurement unit, a bioelectrical impedance measurement unit, a myoelectric signal measurement unit and an electrode unit. The bioelectrical impedance measurement unit and the electromyographic signal measurement unit are respectively electrically connected to the electrode unit, and the electrode unit is in contact with the human body; A first computing circuit unit, the signal is connected to the computer and the inertial signal measurement unit; a second computing circuit unit, the signal is connected to the computer and the bioelectrical impedance measurement unit; and a third computing circuit unit, the signal is connected to the The computer and the electromyographic signal measurement unit; input a body measurement parameter of the human body to the computer, and perform an action on the human body; the inertial signal measurement unit obtains an action through the operation of the first computing circuit unit The action parameters correspond to the human body, and the action parameters are sent to the computer; the bioelectrical impedance measurement unit obtains an electrical impedance signal of the human body through the electrode unit, and then obtains the whole body of the human body through the operation of the second operation circuit unit or part of a muscle mass parameter and send it to the computer; the electromyographic signal measurement unit obtains an electromyographic signal of the human body through the electrode unit, and then obtains an electromyographic signal of the human body through the operation of the third computing circuit unit Muscle strength parameters are transmitted to the computer; the computer or the artificial intelligence model evaluates the influence of the body measurement parameters, the movement parameters, the muscle strength parameters and the muscle mass parameters on the muscle strength of the human body, and then obtains the human body An evaluation parameter of the muscle strength. In the muscle strength evaluation system according to Claim 9, further, a fastener is combined with the muscle strength evaluation device so that the electrode unit fits the human body. In the muscle strength evaluation system described in claim 9, further, the first arithmetic circuit unit includes a microcontroller, the inertial signal measurement unit is an inertial sensing chip, and the microcontroller signal is connected to the inertial signal measurement A unit; obtain a six-axis information through the inertial signal measurement unit, and input the six-axis information into the microcontroller for differential calculation to obtain the motion parameter. In the muscle strength evaluation system described in Claim 9, further, the second computing circuit unit includes a multiplexer circuit signal connection to the bioelectrical impedance measurement unit, and a voltage-controlled current source circuit A signal is connected to the multiplexer circuit, a digital analog signal is connected to the voltage control current source circuit, an instrumentation amplifier circuit signal is connected to the multiplexer circuit, a filter and automatic gain control circuit signal is connected to the instrumentation amplifier circuit, a modulation The signal of the device is connected to the filtering and automatic gain control circuit, and the signal of a microcontroller is connected to the modulator and the digital analog device; the digital analog device is controlled by the microcontroller to generate a sine wave, and the voltage control current source circuit is then According to the sine wave, a current is output to the multiplexer circuit to be selectively introduced into the human body through the bioelectrical impedance measurement unit and the electrode unit, and the electrical impedance signal fed back from the human body is transmitted from the multiplexer circuit An electrical impedance value is obtained through signal processing of the instrumentation amplifier circuit, the filtering and automatic gain control circuit, and the modulator, and then input to the microcontroller for analysis to obtain the muscle mass parameter. In the muscle strength evaluation system described in claim 9, further, the second computing circuit unit includes a multiplexer circuit signal connected to the bioelectrical impedance measurement unit, an integrated circuit chip signal connected to the multiplexer circuit, and A microcontroller signal is connected to the integrated circuit chip; the multiplexer circuit is controlled by the microcontroller to selectively introduce a current into the human body through the bioelectrical impedance measurement unit and the electrode unit, and from the human body The electrical impedance signal fed back is processed from the multiplexer circuit through the integrated circuit chip to obtain an electrical impedance value, and then input to the microcontroller for analysis to obtain the muscle mass parameter. As in the muscle strength evaluation system described in claim item 9, further, the third operational circuit unit includes an amplifier signal connected to the electromyographic signal measurement unit, a high/low pass filter signal connected to the amplifier, an adjustable gain and The translation circuit signal is connected to the high/low pass filter, and a microcontroller signal is connected to the adjustable gain and translation circuit; the myoelectric signal measurement unit passes the myoelectric signal through the amplifier, the high/low pass through the electrode unit low-pass filter and the adjustable gain and translation circuit for signal processing After processing, it is input to the micro-controller for filtering, calculation and analysis to obtain the muscle strength parameter of the human body.

Images