KR101421122B1 - Prediction Method of Muscle Fatigue and Prediction System of Muscle Fatigue - Google Patents

Abstract

A muscular fatigue prediction method according to an embodiment of the present invention comprises the steps of collecting information on an operation of a wearer and electromyogram through a sensor attached to the wearer; outputting muscular power of used muscles or moment power of a used articulation as first data based on the collected information; comparing the first data with second data stored in advance, and determining a fatigue accumulated state when the first data are within a predetermined range set by the second data.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for predicting muscle fatigue and a method for predicting muscle fatigue,

One embodiment of the present invention relates to a method of predicting a wearer's fatigue and a system capable of predicting fatigue.

Currently, physiological evaluation methods of human body muscles include measuring workloads according to workload given to workers by measuring physiological responses such as heart rate, oxygen consumption, and electromyography.

Since heart rate and oxygen consumption are involved in the energy metabolism of muscles, the amount of energy consumed during work can be estimated. However, heart rate and oxygen consumption are very useful for assessing systemic work, but are not appropriate for situations where the local muscle load is being assessed, so in these cases, the workload is assessed using EMG.

Because EMG can measure muscle on-off, muscle activity, and muscle fatigue, EMG is useful as a method of evaluating the fatigue of local muscles.

However, a method of predicting the fatigue of the human body due to cyclic loading and preventing muscle damage has not been developed until now.

It is an object of the present invention to provide a method and system for predicting the fatigue of a human muscle caused by cyclic loading to prevent human injury including fatigue fracture and ligament damage.

According to another aspect of the present invention, there is provided a method of predicting muscle fatigue according to an embodiment of the present invention, comprising: collecting information on an operation of a wearer and an electromyogram through an attached sensor; Outputting the muscle power of the used muscle or the moment power of the used joint to the first data based on the information, comparing the first data with the previously stored second data, and comparing the first data with the second data And determining that the fatigue accumulation state is in a predetermined range set in the fatigue accumulation state.

According to one example related to the present invention, the data may be information on the muscle power of each muscle used or the moment power of joints used when the wearer performs a specific operation.

According to an embodiment of the present invention, the second data includes stored values related to the number of times of use and muscle strength of the muscle so as to form a FN (Fatigue - Numbers of times) curve, Comparing the first data including the muscle force with the second data, and if the first data is within a predetermined range set in the second data, determining the fatigue accumulation state.

According to an embodiment of the present invention, the second data is a moment power of each joint, and the step of determining that the fatigue accumulation state is a state in which the first data including the moment power of the joint is constant If the value is decreased to more than the numerical value, it may be judged as the fatigue accumulation state.

According to an embodiment of the present invention, the step of outputting as the first data may include inputting information received from the sensor into a simulation program to analyze the operation of the wearer, inputting the EMG information received from the sensor into a simulation program And outputting the muscle power of the used muscle or the moment power of the used joint in association with the operation as the first data.

In order to achieve the above-described object, another embodiment of the present invention is a method for detecting a wearer's movement and EMG information by using sensors and information collected from the sensors, Wherein the first data and the second data are compared with each other, and when the first data is within a predetermined range set in the second data, the fatigue accumulation And a control unit for determining the state of the muscle fatigue.

According to an embodiment of the present invention, the apparatus may further include an output unit for outputting information on fatigue so that the wearer can recognize the fatigue.

According to an exemplary embodiment of the present invention, the apparatus may further include a wireless transceiver configured to transmit information on fatigue of the wearer to a central control center and receive an instruction of the central control center.

The fatigue prediction method and the fatigue prediction system according to at least one embodiment of the present invention configured as above can analyze the muscle fatigue of the human body muscles and predict the number of possible repetitive motions according to the load load. In addition, the F-N curve showing the number of iterations that can be repeated according to the load load for various operations is established as a muscle fatigue scale and used as a data base to analyze and predict muscle fatigue.

Using the fatigue prediction method and system according to the present invention, it is possible to predict the fatigue of the human muscle by the repeated load only by the simulation analysis, thereby preventing the human injury including the fatigue fracture and ligament damage, and minimizing the fatigue of the worker .

The present invention relates to muscle fatigue due to fatigue caused by cyclic loading exercises such as muscle strength and fatigue prediction in patients in medical and rehabilitation fields, muscle strength and fatigue prediction in athletes training in sports science, And the method and system according to the embodiment of the present invention can be equally implemented for wired, wireless portable or indoor installation

FIG. 1 is an algorithm showing a method and system for analyzing and predicting the fatigue of a human body muscle according to cyclic loading according to the present invention.
FIG. 2 is an algorithm showing a method and system for analyzing and predicting the fatigue of human muscles according to cyclic loading according to the present invention.
FIG. 3 is a graph showing the strength obtained from the load cell experiment and the strength obtained from the simulation analysis in the dumbbell curl operation.
Fig. 4 is a graph of muscle strength of the bicep of the upper arm obtained through simulation analysis in the case where no fatigue is accumulated in the dumbbell curl repeated operation and in the case where the fatigue is accumulated.
FIG. 5 is a graph of an EMG signal according to the accumulation of muscle fatigue of the bicep biceps caused by the dumbbell curl repetitive load.
FIG. 6 shows IEMG (Integrated EMG) analysis results of the biceps due to the dumbbell curl repetitive load.
FIG. 7 shows a fast Fourier transform (FFT) analysis result in the frequency domain of the bicep of the upper arm due to the repeated load load of the dumbbell curl.
FIG. 8 is a mean frequency analysis result of the bicep according to the repeated load of the dumbbell curl.
FIG. 9 shows a graph showing a decrease in joint moment power with fatigue accumulation of the bicep due to repeated loading of the dumbbell curl. FIG.
10 is a graph of a force-number of cycles of bicep biceps due to repeated loading of dumbbell curl according to the present invention.
FIG. 11 is a conceptual diagram of a system for displaying muscular fatigue prediction and real-time fatigue information according to the present invention and transmitting it to a central management center to prevent muscular and ligament damage due to fatigue.

Hereinafter, the fatigue prediction method and the fatigue prediction system according to the present invention will be described in detail with reference to the drawings. The suffix "module" and " part "for the components used in the following description are given or mixed in consideration of ease of specification, and do not have their own meaning or role. In the present specification, the same or similar reference numerals are given to different embodiments in the same or similar configurations. As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise.

The present invention relates to a method and system for measuring and analyzing muscle fatigue with repeated loads through an electromyography experiment and predicting muscle fatigue with repeated loads using a simulation program, The prediction method and the fatigue prediction system are shown in FIG. 1 as an example.

FIG. 1 is an algorithm for predicting the muscle fatigue of a human body using a stored database and outputting it (display, voice, alarm, etc.) in real time. The EMG signal and motion information measured through the EMG sensor and the motion sensor attached to the garment are analyzed through the built-up fatigue database to calculate the degree of fatigue accumulation. The calculated fatigue information is output to an output unit (for example, a display device, a voice output device capable of voice or alarm), and sent to a central management center through a wireless transceiver to collect all information from the central management center Management.

Specifically, as shown in FIG. 2, the method for predicting muscle fatigue and system according to the present invention comprises the steps of: a) analyzing a change in an EMG signal according to a cyclic load in a time domain and a frequency domain through an EMG signal measurement; b) Input muscle activation information and motion data obtained from EMG signal measurement into a human body model based on Hill base muscle, and output muscle force and joint moment using positive / reverse dynamics ; c) The muscle fatigue can be predicted by the FN curve showing the relationship between strength and repeatability frequency and the reduction rate (30%) of joint moment power at the time of muscle fatigue accumulation, using the muscle force and joint moment obtained in the above step as a database Deriving an indicator; d) preventing muscle or ligament damage by displaying muscle fatigue information by predicting muscle fatigue through the index obtained in the above step.

According to an embodiment of the present invention, a method of predicting muscle fatigue includes the steps of collecting information on an operation and an electromyogram of a wearer through an attached sensor, And outputting the moment power of the used joint to the first data, and comparing the first data with the previously stored second data, and when the first data is in a predetermined range set in the second data, And a step of judging the state.

The step of outputting the first data may include inputting information received from the sensor to a simulation program to analyze the operation of the wearer and inputting the EMG information received from the sensor into the simulation program, And outputting the muscle power of the used muscle or the moment power of the used joint as the first data.

When the wearer attaches the sensor and performs actual operation, the information about the operation and the EMG information generated by the operation can be obtained through the sensor. At this time, the controller can determine whether the operation information is a jump operation or a walking operation, for example, through the information sensed by the sensor. By using the information sensed from the EMG, it is possible to know which muscle is used in a specific operation.

In this case, the first data and the second data may be information on the muscle power of each muscle used or the moment power of the joints used when the wearer performs a specific operation. The second data is information previously stored in the database, and the first data may be the muscle power of the used muscle or the moment power of the used joint obtained by using the output result after inputting into the simulation program using the EMG information have. This will be described later.

The second data includes stored values related to the number of times of use and muscle strength of the muscle so as to form a FN (Fatigue - Numbers of times) curve, and the step of determining the fatigue accumulation state comprises: Comparing the first data with the second data, and if the first data is within a predetermined range set in the second data, judging it as a fatigue accumulation state.

The second data is a moment power of each joint, and the step of determining the state of fatigue accumulation is a step of, when the first data including the moment power of the joint is reduced to a predetermined value or more as compared with the second data, This may be a step of judging the fatigue accumulation state.

When implemented as a muscle fatigue prediction system, the muscle fatigue prediction system uses sensors that are attached to the body to sense the wearer's motion and EMG information, and information gathered from the sensors, And the first data is compared with the previously stored second data, and when the first data is within a predetermined range set in the second data, And a control unit for judging whether or not there is a difference.

In this case, the information processing apparatus may further include an output unit for outputting information on fatigue so that the wearer can recognize the information.

The information processing apparatus may further include a wireless transmitting / receiving unit configured to transmit information on the wearer's fatigue to the central management center and receive instructions from the central management center.

According to the present invention, since the muscle force, which is different from the rigid body, must be considered as the degree of activation, which is the neurotransmission of the muscles, the muscular strength is obtained through the simulation program based on Hill base muscle have. The driving of the simulation is divided into inverse dynamics and forward dynamics. First, the motion data (motion information) obtained from the experiment is input to the simulation and the position information is received from the inverse dynamics. And stores contraction information of the corresponding muscles. Then, in the forward dynamics, the position information is deleted, and the muscular strength (muscle strength) is derived only by the stored muscle contraction information and the activation curve data of the muscle. The activation curve of muscle is obtained after rectification (EMG) data measured by electromyography (Rectification, high / low pass filter, nomalization).

In the present invention, as shown in FIG. 3, the stiffness and damping of the muscles are reconfigured while matching the force measured by the load cell with the calculated kinematics of the wearer in order to calculate physical property values of the wearer's muscles.

FIG. 4 is a graph showing the strength of the biceps biceps of the human body in the last motion before the dumbbell curl operation is performed due to the first motion and muscle fatigue accumulated before the muscle fatigue accumulation is analyzed by the simulation using the EMG signal It is a graph.

The pattern in this graph shows a similar tendency to the EMG signal. In the graph, two vertices are shown in the graph. The first vertex represents the force acting on the biceps bicep when lifting the dumbbell, and the second vertex represents the force acting on the biceps bicep when lowering the dumbbell. This shows that the contraction of the biceps brachii is complete and can no longer exert its force. The graph shows that as the weight of the dumbbell increases, the strength of the biceps brachii increases before or after muscle fatigue accumulation. In addition, when the repeated dumbbell weight was applied, the force of the biceps brachium after the fatigue accumulation was increased before the fatigue accumulation, and the time of the same operation was also prolonged. This shows that the travel distance of the dumbbell curl motion in the calculation of moment power is the same every time but the work rate decreases as the fatigue accumulates.

In addition, according to an embodiment of the present invention, FIG. 5 is a rectified EMG signal of the biceps brachial that shows a change as muscle fatigue due to repeated load is accumulated. As the weight of the dumbbell increases, the amplitude of the EMG signal of the biceps brachii increases. This increase in amplitude is due to the increased activity of muscle fibers as more muscle is used for larger load loads. In the first dumbbell curl operation, muscle fatigue was accumulated compared to the EMG signal (left), and the amplitude of the EMG signal (right) immediately before the operation was no longer observed increased significantly. This is the result of synchronization and recruitment of the exercise units as the muscles become tired and the contraction capacity of the muscle fibers decreases.

6 is an integrated EMG (IEMG) obtained by calculating the area of the extracted signal to estimate the use of the biceps according to the repetition frequency of the load and the force exerted by the muscles from the EMG signal of FIG. This IEMG value represents the total momentum of each cycle. This graph shows that as the load load increases, the IEMG value increases due to an increase in muscle fiber activity potential. This result is consistent with the results shown in Fig. In addition, except for the case of 10 kg, the increase and decrease of the IEMG value are repeated as the number of repetition of the load load increases, and the tendency to increase as a whole is shown. The increase in the IEMG value is due to the increase in the amplitude of the EMG as the muscle fatigue accumulates, as confirmed from the EMG change in FIG. On the other hand, the increase and decrease in the overall increase of IEMG are repeated because the local fatigue due to the decreased response of the single muscle caused by the repeated stimulation occurs, and after the exercise of a certain amount, there is a recovery period. Since the biceps brachii can not exert a great deal of force during this recovery period, the sickle flexor and shoulder muscles are supplemented more to support the load.

In FIG. 6, unlike the cases of 3, 5, and 7 kg, the IEMG is slightly increased and decreased repeatedly in the case of 10 kg, but the tendency does not increase as a whole. In the overload condition above the maximum biceps force of the biceps brachii, the other biceps such as the sagittal and shoulder muscles were used relatively more than under the maximum load, and the total momentum of the biceps brachii was kept constant. This is because the value is stably maintained.

FIG. 7 is a graph showing a result of converting a time-domain EMG signal (FIG. 5) into a frequency domain signal through a fast Fourier transform (FFT). As the muscle fatigue due to the repetitive motion is accumulated for all the load loads, the amplitude increases and a frequency shift phenomenon occurs from high frequency to low frequency. This is because the muscle becomes fatigued, the amplitude increases but the frequency decreases. That is, decreasing the conduction velocity of the fascia due to muscle fatigue increases the duration of the action potential and lowers the frequency. In addition, it is confirmed that the amplitude is slightly smaller than 7 kg in the case of 10 kg of accumulated fatigue (FIG. 7, right) as the load load increases, but it is consistent with the IEMG change pattern of FIG. 6 .

Fig. 8 shows the mean frequency, mean power frequency (MPF), which is used as an evaluation measure of fatigue from the signal in the frequency domain according to the cyclic loading (Fig. 7). According to Fig. 8, it has a low average frequency (MPF) as the load load increases. Also, as muscle fatigue accumulates, MPF decreases.

FIG. 9 is a graph showing an analysis of the elbow joint moments immediately before the fatigue is accumulated and the muscle fatigue is accumulated and the dumbbell curl is no longer performed. The moments of the joints tend to increase as the load load increases. However, as the duration of the dumbbell curl motion continued, the muscle fatigue was accumulated and the muscle fatigue showed a tendency to decrease compared to the power before the accumulation. According to FIG. 9, when the fatigue is accumulated, the power of the joint moment is decreased by 30% on average. Thus, the present invention defined a 30% reduction in joint moment power as a measure of muscle fat accumulation.

Fig. 10 shows an F-N curve showing a measure of muscle fat accumulation. By using this as a database, it is possible to predict muscle fatigue by simulation analysis alone, and it is possible to prevent damage to muscles or ligaments.

11, when the fatigue prediction system according to the present invention is used, real-time fatigue information is output (display, voice, alarm, etc.) by predicting the fatigue using the database according to the present invention, By sending it to the command center, a system can be built to monitor the fatigue of soldiers or workers. Such a system can prevent muscle and ligament damage due to fatigue of the soldier or the worker and increase the working efficiency.

Example  One

A 25-year-old male measured the EMG signal, which is the activation level of the bicep of the subject, according to the cyclic load during dumbbell curl operation. Using the dumbbell of 3kg, 5kg, 7kg and 10kg, .

When measuring the EMG, the distance between the electrodes was set to 2 cm in the direction of the muscle fiber, and the ground electrode was placed outside or inside.

Since the electrode may be detached depending on the position of the muscle due to muscle contraction, the electrode is attached to the lower one third of the bicep muscle, which is promoted when the muscle is contracted to the maximum. It was sterilized with alcohol cotton before attaching the electrode.

The EMG sensor was connected to the EMG module, and one channel EMG signal was collected at a sampling rate of 1500 Hz, a low pass filter of 500 Hz, and a high pass filter of 10 Hz.

The wearer performed the dumbbell curl operation with the right arm only while keeping the standing posture with the leg open at the shoulder width. The dumbbell was lifted at a constant speed for each load and the EMG signal was measured to the extent that the wearer could no longer hold the dumbbell.

After storing the measured EMG signals, signal processing and analysis were performed using the IEMG and FFT methods in the time domain and the frequency domain, respectively, from the stored signals.

In order to trace back the exact physical properties of the wearer, we compare the muscle force measured by the load cell with the simulation results and readjust the physical properties of the muscles required for the analysis. Then, the human body model based on the Hill base muscle And the strength and joint moment of the biceps brachii were obtained. The power of the joint moment by the simulation analysis was compared with the case in which the muscle fatigue was not accumulated and the case where the muscle fatigue accumulated and could no longer be moved. As a result, the power decreased by about 30%.

Example  2

One 25-year-old male was measured the electromyogram signal of activation of the deltoid of the subject according to the cyclic load during the side lateral operation. Using the dumbbell of 2kg, 3.5kg, 5kg, 7kg, The size of the load was adjusted.

Electromyography was performed using a Noraxon dual electrode (Noraxon, USA). The distance between the electrodes was 2 cm in the direction of the myofiber, and the ground electrode was placed on the outside or inside.

When measuring the EMG, the distance between the electrodes was set to 2 cm in the direction of the muscle fiber, and the ground electrode was placed outside or inside.

Since the electrode may be detached depending on the position of the muscle due to muscle contraction, the electrode is attached to the lower third of the deltoid muscle, which is promoted when the muscle is contracted to the maximum. It was sterilized with alcohol cotton before attaching the electrode.

The EMG sensor was connected to the EMG module, and one channel EMG signal was collected at a sampling rate of 1500 Hz, a low pass filter of 500 Hz, and a high pass filter of 10 Hz.

The wearer performed side lateral motion with only his right arm while keeping his legs open and standing in shoulder width. The dumbbell was lifted at a constant speed for each load and the EMG signal was measured to the extent that the wearer could no longer hold the dumbbell.

After storing the measured EMG signals, signal processing and analysis were performed using the IEMG and FFT methods in the time domain and the frequency domain, respectively, from the stored signals.

In order to trace back the exact physical properties of the wearer, we compare the muscle force measured by the load cell with the simulation results and readjust the physical properties of the muscles required for the analysis. Then, the human body model based on the Hill base muscle And the strength and joint moment of the biceps brachii were obtained. The power of the joint moment by the simulation analysis was compared with the case in which the muscle fatigue was not accumulated and the case where the muscle fatigue accumulated and could no longer be moved. As a result, the power decreased by about 30%.

Example  3

One 25-year-old male measured EMG signal, which is the activation level of the rectus femoris, in the pelvic limb during repetitive load operation. Using 3 kg, 5 kg, 7 kg, and 9 kg sand bags, And the magnitude of the load was adjusted.

When measuring the EMG, the distance between the electrodes was set to 2 cm in the direction of the muscle fiber, and the ground electrode was placed outside or inside.

The electrodes were attached to the lower third of the rectus femoris muscle, which is promoted when the muscle is contracted to the maximal extent, because the electrode may be detached depending on the position of the muscle contraction due to muscle contraction. Before the electrode was attached to remove, it was sterilized with alcohol cotton.

The EMG sensor was connected to the EMG module, and one channel EMG signal was collected at a sampling rate of 1500 Hz, a low pass filter of 500 Hz, and a high pass filter of 10 Hz.

After storing the measured EMG signals, signal processing and analysis were performed using the IEMG and FFT methods in the time domain and the frequency domain, respectively, from the stored signals.

In order to trace back the exact physical properties of the wearer, we compare the muscle force measured by the load cell with the simulation results and readjust the physical properties of the muscles required for the analysis. Then, the human body model based on the Hill base muscle And the strength of the lower limb and the joint moment were obtained. The power of the joint moment by the simulation analysis was compared with the case in which the muscle fatigue was not accumulated and the case where the muscle fatigue accumulated and could no longer be moved. As a result, the power decreased by about 30%.

The above-described fatigue prediction method and fatigue prediction system are not limited to the configuration and method of the embodiments described above, but the embodiments may be applied to all or some of the embodiments so that various modifications can be made. May be selectively combined.

Claims (8)

Collecting information about an operation of the wearer and an electromyogram through the attached sensor;
Outputting the muscle power of the used muscle or the moment power of the used joint to the first data based on the collected information; And
Comparing the first data with second data including stored values relating to the number of times of muscle use and muscle strength so as to form a FN (Fatigue-Numbers of times) curve, and setting the first data in the second data And a fatigue accumulation state, when the vehicle is in a certain range,
Measuring the EMG of the wearer and extracting muscle activation information and motion data by the EMG;
Outputting a joint moment using the degree of activation of the muscle, motion data, and a human body model;
Deriving an index for predicting fatigue based on the joint moment, the FN curve, and the reduction rate of the joint moment power according to the accumulation of muscle fiber; And
And predicting muscle fatigue through the index. The method according to claim 1,
Wherein the data is information about a muscle power of each muscle used or a moment power of joints used when the wearer performs a specific operation. delete 3. The method of claim 2,
The second data is the moment power of each joint,
Wherein the step of determining the fatigue accumulation state is a step of defining the fatigue accumulation state when the first data including the moment power of the joint is reduced to a predetermined value or more as compared with the second data, Fatigue prediction method. 3. The method of claim 2,
The step of outputting as the first data
The information received from the sensor is input to the simulation program to analyze the operation of the wearer,
And inputting the electromyogram information received from the sensor into the simulation program and outputting the muscle power of the muscle used or the moment power of the joint used in relation to the operation as the first data. Sensors attached to the body to detect movement of the wearer and EMG information; And
Using the information collected from the sensors, it is possible to output the muscle power of the used muscle or the moment power of the joints used as the first data, and form the first data and the FV (Fatigue-Numbers of times) curve And comparing the second data including stored values relating to the number of times of muscle use and muscle strength so as to define a fatigue accumulation state when the first data is within a predetermined range set in the second data,
The control unit measures the wearer's EMG and extracts activation information and motion data of the muscles according to the EMG, outputs the joint moment using the muscle activation information, the motion data, and the human body model, and calculates the joint moment, the FN Wherein an index for predicting fatigue is derived based on a reduction rate of joint moment power according to a curve and a muscle fatigue scale, and the fatigue prediction is predicted through the index. The method according to claim 6,
Further comprising an output unit for outputting information on whether or not fatigue is to be recognized by the wearer. The method according to claim 6,
Further comprising a wireless transceiver configured to transmit information on fatigue of the wearer to a central control center and receive an instruction of the central control center according to the information.

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