JP4832914B2 - Muscle fatigue evaluation device - Google Patents

Description

  The present invention relates to a muscular fatigue evaluation apparatus, and is particularly suitable for evaluating muscular fatigue of cumulative work performed for a relatively long time in almost the same posture such as sitting posture, such as driving an automobile or office work. The present invention relates to a muscle fatigue evaluation apparatus.

  Patent Document 1 discloses an apparatus that evaluates the comfort level in driving operation of an automobile using myoelectric potential. This apparatus measures a myoelectric potential of a pair of muscles provided symmetrically, in particular, the deltoid muscle of the shoulder, obtains a synchronous contraction waveform from a time series waveform of myoelectric potential, and further, at predetermined time intervals, A technique is disclosed in which an integrated value of a waveform or an integrated value of an envelope of the waveform is obtained and the comfort level is evaluated using the value. Patent Document 2 discloses an apparatus for measuring the myoelectric potential of the left and right masseters that opens and closes the jaws to obtain the intensity information of the myoelectric potential of the masseter muscles from the time-series waveform and evaluate the stress in driving work and the like. It is disclosed.

  By the way, since the electromyogram waveform is a biological signal obtained by contracting the muscle, in order to obtain the electromyogram waveform as described above, the myoelectric potential is measured for the muscle at the place where a certain exercise is performed. There is a need. In general, vehicle driving is a work with a small amount of exercise, although it depends on driving conditions. Therefore, in Patent Document 1, as a specific example, application to evaluation of the comfort level during steering with a relatively large amount of exercise is merely mentioned. In the technique of Patent Document 2, a detection sensor must be attached to the masseter muscle located on the side of the face, and it is not practical to attach this sensor during normal driving.

On the other hand, in Patent Document 3, muscle fatigue can be determined even if the subject does not actively exercise, and muscle fatigue determination that can be attached to a position that does not hinder driving even during driving. An apparatus is disclosed. That is, this is a device for determining muscle fatigue by attaching a detection sensor to the standing muscle of the spine.
JP 2004-49622 A JP 2005-87486 A JP 2000-232A

  Low back pain caused by vibrations input from the vehicle or the road surface is likely to occur in the muscles of the back of the back responsible for maintaining posture. The muscles of the back of the lumbar region are rich in the types of muscle fibers involved in sustained tension generation, particularly slow muscle fibers. Since this muscle has little change in its own movement, it is difficult to detect the change in the movement of the muscle only by looking at the waveform of the electromyogram normally detected. For this reason, when detecting fatigue in a sitting posture, rather than directly evaluating muscle activity to maintain such a sitting posture, it is evaluated using indirect parameters such as dynamic body pressure distribution and vibration transfer characteristics. It is common. Therefore, development of a technique for directly evaluating muscle fatigue in a sitting posture is desired. Muscle fatigue assessment in long-term muscle activity at low strength such as posture maintenance is performed by aerobic energy supply, such as the muscles of the back of the lumbar region including the spine upright muscle, and the glycolytic ability is low, and the muscle itself It is desirable to target muscles with little change in movement. It can be said that the technique disclosed in Patent Document 3 is more suitable for fatigue evaluation of cumulative work such as driving than the techniques disclosed in Patent Documents 1 and 2 in this respect.

  However, simply attaching a detection sensor to muscles with little glycolytic ability and little change in the movement of the muscles themselves cannot obtain myoelectric potential data that has a significant difference between muscle fatigue and other cases. . For this reason, Patent Document 3 employs a configuration in which stimulation applying means for applying electrical stimulation is applied to the erected spine muscle and myoelectric potential as a response to the electrical stimulation is detected. In other words, a pulse voltage is applied from the electrical stimulation electrode to the standing muscle of the spine, the average value of the evoked myoelectric potential and the power spectrum distribution of the evoked myoelectric potential are obtained by frequency analysis, the center frequency is calculated, and the average value of the amplitude In addition, the occurrence of muscle fatigue is detected by comparing changes in the center frequency every predetermined time, but there is a problem that the apparatus configuration is complicated, such as requiring an electrical stimulation electrode.

  On the other hand, the present applicant acquires biological signals such as pulse waves, heartbeats, and breaths in WO2005 / 039415A1, obtains an average rate of change in a predetermined time range of the time-series data, performs slide calculation using the rate of change, The data processing means which determines a person's state (whether it is an awake state, a sleep state, a fatigue state, etc.) from the obtained result is proposed. According to this data processing means, minute and minute changes such as pulse waves can be emphasized and expanded, and can be perceived as fluctuations consisting of a more global time-series waveform. Can clarify the change of time series. That is, in the conventional technique, the myoelectric potential of a muscle having a large change in the movement of the muscle itself, or the evoked myoelectric potential by applying a stimulus, is measured. When viewed as fluctuations consisting of various time-series waveforms, it is a muscle with a large change in the movement of the muscle itself (a muscle with a high glycolysis ability and a relatively high fast muscle fiber ratio), or a change in the movement of the muscle itself. Regardless of whether the muscle is a small muscle (a muscle having a high glycolytic ability and a relatively high ratio of slow muscle fibers), the biological state can be evaluated based on the myoelectric potential of the muscle to be measured.

  In other words, human functions include a central system represented by heartbeat and involuntary muscles and a peripheral system represented by fingertip volume pulse waves and skeletal muscles. The heartbeat, which is the central system, is insensitive. This is because if it reacts sensitively to changes in the outside world, it affects life support. Instead, the peripheral system moves well. It can be said that the peripheral system functions as a barrier that protects the central system from changes in the external world, and the peripheral system has a greater change and a faster response rate to external changes than the central system. For this reason, physiological phenomena such as pain, fatigue, and sleepiness are easy to understand when viewed by peripheral reactions, with large changes appearing and the reaction speed is fast. On the other hand, when a person takes a sitting posture, anti-gravity muscles are used to make the posture, and the burden on muscles that are not used when standing is increased. Anti-gravity muscles that are not used when standing and are used to create a sitting posture are muscles with small changes in movement of the muscles themselves (muscles with high glycolytic ability and relatively high ratio of slow muscle fibers. The percentage of muscle) increases. From these facts, it is appropriate to use the myoelectric potential of the muscle having a small change in the movement of the muscle itself in evaluating the muscle fatigue in the sitting posture.

  Therefore, the present invention makes it possible to evaluate myoelectric potentials of muscles with little change in their own movements such as the muscles of the lumbar region with a simple configuration by applying the above-described method of capturing the global fluctuation waveform. It is an object of the present invention to provide a muscle fatigue evaluation apparatus suitable for fatigue evaluation of cumulative work such as driving. Further, the present invention enables the evaluation of myoelectric potential of muscles with little change in the movement of itself such as the muscles of the back of the back, so that the myoelectric potential detection sensor can be appropriately used regardless of the type of muscle to be measured. It is an object of the present invention to provide a muscle fatigue evaluation apparatus that can be attached to the above-mentioned site and can identify a muscle in which fatigue occurs.

In order to solve the above-mentioned problem, the present invention according to claim 1 is provided with a myoelectric potential detection sensor and a data processing / determination means for processing signal data obtained from the myoelectric potential detection sensor, thereby preventing human muscle fatigue. A muscle fatigue evaluation device for evaluating,
The data processing / determination means includes
From the signal data of the myoelectric potential detection sensor, for each predetermined time range, a maximum value and a minimum value are obtained by a smoothing differential method, and a power value calculating means for obtaining a difference between both as a power value;
Power value slope calculating means for sequentially obtaining the slope of the power value with respect to the time axis in a predetermined time range at a predetermined overlap time;
Creating a time series waveform of the power value slope obtained by the power value slope calculating means, and outputting the time series waveform power value slope time series waveform creating means;
Frequency analysis means for frequency analysis of the slope of the power value obtained by the power value slope calculation means;
Have
The power spectrum obtained by the frequency analysis of the power value slope frequency analysis of the time series waveform of the power value slope is borne by the muscle that is the object of myoelectric potential measurement for the total weight. A correction means for correcting to a value proportional to the mass ratio is provided, and among the power spectra obtained for each muscle to be measured, a muscle having a larger power spectrum value obtained by the correction means has a higher degree of muscle fatigue. providing muscle fatigue evaluation device, characterized in that the determined configuration with.
In the second aspect of the present invention, the data processing / determination means further includes maximum Lyapunov exponent calculation means for calculating a maximum Lyapunov exponent by performing chaos analysis on the signal data of the myoelectric potential detection sensor,
A maximum Lyapunov exponent slope calculating means for sequentially obtaining a slope of the maximum Lyapunov exponent with respect to a time axis in a predetermined time range at a predetermined overlap time;
Claim 1, characterized in that said maximum to create a time-series waveform of the maximum Lyapunov exponent slope determined by the Lyapunov exponent inclination calculation means, and a maximum Lyapunov exponent gradient time series waveform generating means for outputting a time series waveform A muscle fatigue evaluation apparatus as described is provided.
According to a third aspect of the present invention, the data processing / judgment means further includes frequency analysis means for performing frequency analysis on the maximum Lyapunov exponent inclination obtained by the maximum Lyapunov exponent slope calculation means. The muscle fatigue evaluation apparatus according to claim 2 is provided.
According to the fourth aspect of the present invention, a fingertip volume pulse wave measuring unit is further provided, and the data processing / determining unit is configured to output the fingertip volume pulse wave signal data obtained from the fingertip volume pulse wave measuring unit. Means to perform power value gradient and frequency analysis,
The power spectrum of the fingertip volume pulse wave is compared with the power spectrum of corrected myoelectric potential obtained for each muscle to be measured. The muscle fatigue evaluation apparatus according to any one of claims 1 to 3, wherein it is determined that the degree of muscle fatigue is larger as the spectrum and the tendency are closer.
In the present invention according to claim 5, the data processing / determination means further includes a power value inclination obtained by the power value inclination calculating means or a maximum Lyapunov exponent inclination obtained by a maximum Lyapunov exponent inclination calculating means. 5. A muscle fatigue evaluation device according to any one of claims 2 to 4 , wherein a fatigue level calculating means for calculating an integral value by performing absolute value processing is set.

  The present invention includes data processing / determination means for processing signal data obtained from the myoelectric potential detection sensor and generating a fluctuation waveform of the signal data. Although it is difficult to obtain myoelectric potential data having a significant difference between muscle fatigue and when it is not, it is difficult to obtain from the muscles that contain many muscle fibers involved in sustained tension generation and have little movement, but the data processing used in the present invention -Since the judgment means generates a fluctuation waveform that emphasizes the obtained signal data, it detects the occurrence of muscle fatigue in muscles with little movement that contain many muscle fibers involved in sustained tension generation. can do. As a result, the muscle fatigue evaluation apparatus of the present invention is suitable for fatigue evaluation of cumulative work such as driving.

  Further, by analyzing the frequency of the fluctuation waveform obtained by the data processing / determination means, and further correcting the result in proportion to the proportion of the mass borne by the muscle to be measured with respect to the total weight, muscle fatigue It is possible to easily identify the site where the occurrence of the above.

  Hereinafter, the present invention will be described in more detail based on embodiments shown in the drawings. FIGS. 1-2 is a conceptual diagram of the state which attached the muscle fatigue evaluation apparatus 1 which concerns on one Embodiment of this invention to the sheet | seat 100 for vehicles, such as a motor vehicle. The muscle fatigue evaluation apparatus 1 includes a myoelectric potential detection sensor 10 and data processing / determination means 20.

  The myoelectric potential detection sensor 10 is provided with a myoelectric electrode 11 and is attached so as to be in contact with the skin of the subject. As long as the myoelectric potential can be measured, the method of attaching the myoelectric electrode 11 to the skin is arbitrary. For example, the myoelectric electrode 11 can be attached to the skin or embedded in a predetermined position of a jacket that can be worn by the subject. It is good also as a structure.

  When evaluating muscle fatigue in a sitting posture, muscles to be measured include muscles that contain a large amount of muscle fibers involved in continuous tension generation and that do not move much. As described above, driving with a sitting posture is a work that continues muscle activity for a long time with low strength, so muscle fatigue evaluation in that state is the ability to glycolytically act by supplying aerobic energy This is because it is desirable to target a muscle with a small amount of movement, which contains many muscle fibers involved in continuous tension generation, which is a muscle with a small amount of movement. Examples of such muscles include lumbar and back muscles such as the lumbo-gastrocnemius and spinal column standing muscles, and the flat muscles of the lower leg. Considering the ease of measurement in the driving state, it is preferable to target the muscles of the back of the waist because measurement can be performed while wearing a jacket as described above. In addition, according to the present embodiment, muscle fatigue of a muscle having a small change in movement of the muscle itself (a muscle having a high glycolytic ability and a relatively high slow muscle fiber ratio) can be evaluated. It is also possible to evaluate muscle fatigue of muscles with large changes in the movement of the muscles themselves (muscles with high glycolytic ability and muscles with a relatively high fast muscle fiber ratio). That is, in this embodiment, muscle fatigue can be evaluated for various muscles without selecting a muscle for which myoelectric potential is to be measured. Therefore, it is possible to easily identify which muscle is fatigued by attaching the myoelectric potential detection sensor 10 to a plurality of parts in any posture as well as the sitting posture. .

  The data processing / determination unit 20 is connected to the above-described myoelectric potential detection sensor 10 via a wireless or signal cable, and a program as disclosed in WO2005 / 039415A1 is set. That is, as shown in FIG. 2, the program includes a power value calculating unit 21, a power value gradient calculating unit 22, a power value gradient time series waveform creating unit 23, a maximum Lyapunov exponent calculating unit 24, and a maximum Lyapunov unit. Exponential slope calculating means 25 and maximum Lyapunov exponent slope time series waveform creating means 26 are provided.

  First, the power value calculating means 21 smoothes and differentiates the signal data obtained from the myoelectric potential detection sensor 10, and sets a predetermined threshold for the waveform fluctuation width, preferably 70% of the waveform fluctuation width. Is detected, and the maximum value and minimum value of each period of the original waveform are obtained, and then divided into predetermined time ranges set in advance, for example, every 5 seconds (s), and the maximum value in the time range is determined. And the average of the minimum values, and the difference between them is determined as the power value. However, in order to emphasize the amount of change, in this embodiment, it is preferable to square the difference between the average value of the maximum value and the average value of the minimum value in the predetermined time range to obtain the power value.

  The power value inclination calculation means 22 obtains the inclination of the power value obtained by the power value calculation means 21 with respect to the time axis in a predetermined time range by performing slide calculation a predetermined number of times at a predetermined lap rate with respect to the predetermined time. The slide calculation is performed as follows.

For example, when the inclination during T seconds (s) is obtained at a slide lap ratio of 90%, first, the peak value of the maximum Lyapunov exponent between 0 (s) and T (s) and the time axis of the power value are plotted. The slope is obtained by least square approximation. Then
Slide calculation (1): Between T / 10 (s) and T + T / 10 (s),
Slide calculation (2): Between 2 × T / 10 (s) and T + 2 × T / 10 (s),
Slide calculation (n): Each slope between n × T / 10 (s) and T + n × T / 10 (s) is obtained by least square approximation. In order to globally grasp the characteristics of the power value in the time domain, the sampling time interval (T seconds) when performing the slide calculation is optimally 180 seconds, and the slide lap ratio is optimally 90%. . The reason for this is disclosed in WO2005 / 039415A1, so although details are omitted, it was obtained from a sleep experiment conducted on several subjects, and the time interval for calculating the slope was set to 180 seconds. When the slide wrap rate is set to 90%, the characteristic (fluctuation) that fluctuates peculiar to the biological displacement signal can be extracted significantly.

  Each slope calculated by the power value slope calculating means 22 is plotted on the time axis by the power value slope time series waveform creating means 23 to create a time series waveform (fluctuation waveform).

  On the other hand, the maximum Lyapunov exponent is one of the chaos indicators, and is a numerical value indicating the degree of dependence of the chaos on the initial value as an exponent, and the distance between two adjacent trajectories drawn by the chaos attractor is It is an amount indicating the degree of separation with time. Specifically, the myoelectric signal data collected by the myoelectric potential detection sensor 10 is obtained by first reconstructing the myoelectric time-series signal into the state space by the maximum Lyapunov exponent calculation means 24 by the time delay method. Slide calculation is performed using a sliding window of 30 seconds for the continuous data calculation value, the Lyapunov exponent is digitized, the maximum Lyapunov exponent value is plotted every second, and the time series data of the maximum Lyapunov exponent is obtained. obtain. Next, the maximum value and the minimum value of each period of the time series change waveform of the maximum Lyapunov exponent calculated as described above are detected. This method is the same as in the case of the power value, and the maximum Lyapunov exponent is obtained by smoothing differentiation.

  The maximum Lyapunov exponent slope calculating means 25 calculates the slope of the maximum Lyapunov exponent obtained above in the predetermined time range with respect to the time axis in the predetermined time range, as in the case of the power value. It is calculated by sliding a predetermined number of times at a predetermined lap rate. Each slope calculated by the maximum Lyapunov exponent slope calculating means 25 is plotted on the time axis by the maximum Lyapunov exponent slope time series waveform creating means 26 to create a time series waveform (fluctuation waveform).

  The data processing / determination means 20 analyzes each time series waveform (fluctuation waveform) created by the power value slope time series waveform creation means 23 and the maximum Lyapunov exponent slope time series waveform creation means 26, and accumulates fatigue. Fatigue determination means 27 is set as a program for determining the state.

  The fatigue determination unit 27 compares the time series waveforms of the power value inclination and the maximum Lyapunov exponent inclination to determine the appearance of the fatigue signal. Whether or not it is a fatigue signal, as disclosed in WO2005 / 039415A1, a characteristic signal group that stably shows a phase difference (opposite phase) of about 180 degrees in both time series waveforms appears. Judgment is made at a certain time.

  The data processing / determination unit 20 preferably further includes a frequency analysis unit 28. This is a frequency analysis of the change in the slope of the power value appearing in time series and the slope of the maximum Lyapunov exponent. The power value slope frequency analysis means bears the power spectrum obtained by frequency analysis of the time-series waveform of the power value slope, and the muscle that is the object of myoelectric potential measurement bears the total weight. It is preferable that a correction unit that corrects the value to be proportional to the mass ratio is provided. Since the power spectrum obtained through such correction means has a value corresponding to the burden mass, it is possible to directly compare those measured for a plurality of muscles. Therefore, it is possible to determine that the muscle having a larger absolute value of the power spectrum obtained by the correcting means has a higher degree of muscle fatigue (or muscle fatigue has occurred).

  On the other hand, time series signals of power value inclination and maximum Lyapunov exponent inclination obtained by analyzing signal data of fingertip volume pulse wave in the same manner as the above myoelectric potential, and frequency analysis results thereof are disclosed in WO2005 / 039415A1. As described above, a biological condition such as fatigue of the entire body of the subject is determined. Therefore, the power spectrum of the frequency analysis obtained by measuring the myoelectric potential at a plurality of sites is almost the same as the power spectrum of the fingertip volume pulse wave. Then, when compared by region, it can be determined that the degree of muscle fatigue is greater (or muscle fatigue has occurred) as the power spectrum and tendency of the fingertip volume pulse wave approximate.

  Furthermore, it is preferable to set a fatigue degree calculating means 29 in the data processing / determination means 20. This is because the time series signal of the power value slope obtained by the power value slope calculating means 22 and the time series signal of the maximum Lyapunov exponent slope obtained by the maximum Lyapunov exponent slope calculating means 25 are subjected to absolute value processing and integrated. By calculating the value, the amount of energy metabolism is estimated, and the integrated value is calculated as the degree of fatigue (the degree of progress of fatigue). This is because energy metabolism and fatigue are linked.

(Test Example 1)
A subject (a healthy Japanese man in his thirties) was seated on an automobile seat for 90 minutes with his eyes open, and fatigue was verified.

  The myoelectric electrode 11 was affixed to the skin of the surface of the left lumbar gluteal and right gluteal gluteal muscles, which are muscles with low glycolytic ability, to obtain myoelectric signal data of each muscle. Moreover, the myoelectric electrode 11 was affixed on the skin of the surface of the left foot gastrocnemius muscle and the right foot gastrocnemius muscle which is a muscle with a high glycolytic ability and a high ratio of fast muscle fibers, and electromyographic signal data of each muscle was obtained. Then, each signal data is set in the data processing / determination means 20 by a power value slope calculating means 22, a power value slope time series waveform creating means 23, a maximum Lyapunov exponent calculating means 24, a maximum Lyapunov exponent slope calculating means 25, a maximum Lyapunov. Processing was performed by the exponential slope time series waveform creating means 26. The obtained data are FIG. 3 and FIG. FIG. 3A shows data of the left lumbar gastrocnemius, FIG. 3B shows data of the right lumbar gastrocnemius, and FIG. 4A shows data of the left leg gastrocnemius. b) shows the data of the right foot gastrocnemius, respectively.

  The fatigue determination means 27 analyzes the waveforms shown in FIGS. 3 and 4 to detect the occurrence of a fatigue signal. The fatigue determination means 27 is a point in time when a characteristic signal group that stably shows a phase difference (opposite phase) of approximately 180 degrees appears in the time series waveform of the power value inclination and the maximum Lyapunov exponent inclination. Is determined to be the appearance of a fatigue signal causing fatigue of a predetermined level or more. From FIGS. 3A and 3B, a range of about 5 minutes before and after 63 to 68 minutes (shown by A in the figure). It can be seen that the fatigue signal appears in the range. In addition, since the amplitude of the inclination is larger in FIG. 3B than in FIG. 3A, the accumulation of fatigue is greater in the right lumbar gluteal muscle than in the left lumbar gluteal muscle in this subject. Can be determined to be large. Thus, the determination of the fatigue state using the myoelectric signal data can be determined by specifying the part where the muscle fatigue occurs.

  On the other hand, regarding the left and right gastrocnemius muscles of FIGS. 4 (a) and 4 (b) with high glycolytic ability, the range of about 5 minutes before and after 63-68 minutes is centered on the relationship between the slope of the power value and the slope of the maximum Lyapunov exponent. In, no significant phase difference is observed. That is, fatigue does not occur in the gastrocnemius during this time period.

  That is, for gastrocnemius muscles with high glycolytic ability, fatigue occurs when frequent accelerator and brake operations are performed, and after that, if the stable running state continues, the fatigue recovers and the sitting state is prolonged. But gastrocnemius muscles are less fatigued. On the other hand, in the case of lumbar gluteal muscles with low glycolytic ability, fatigue occurs over a long period of time even in a stable sitting state. As described above, even if such myoelectric signal data of the lumbar gluteal muscle is detected, it has been difficult to collect the data as having a significant difference between the fatigued state and the other state. In the invention, by performing the above-described data processing, the myoelectric signal data of the lumbar gluteal muscle is amplified so to speak and can be viewed as a global fluctuation waveform, so that muscle fatigue can be reliably captured.

  FIG. 5 shows the data processing / means of the above embodiment based on the original waveform data obtained by measuring the fingertip volume pulse wave by wearing an optical fingertip volume pulse wave meter on the subject's finger during the test. Is a time series waveform of the power value gradient and the maximum Lyapunov exponent gradient created by the same means. The fingertip plethysmogram is effective to see the change in the physical condition of the whole body, and the power value gradient and the maximum Lyapunov exponent gradient are prominent in the range of 45 to 60 minutes (the range indicated by B in the figure). A negative phase is observed, and a fatigue signal is generated at this point. However, a remarkable 180-degree phase difference is not observed in the range of about 5 minutes before and after 63 to 68 minutes. However, in the time series waveform of the maximum Lyapunov exponent slope, an extreme peak waveform is generated in the same time zone, which indicates that the subject had body movement. In other words, when fingertip plethysmogram is used, it is possible to detect the fatigue signal of the whole body, which can be said to be a comprehensive index that is the result of using all muscles, regardless of the level of glycolysis, as well as body movement etc. It can also be determined whether there is a movement that temporarily eliminates muscular fatigue, but it cannot be determined whether fatigue has accumulated in muscles with low glycolytic ability, such as the lumbar gluteal muscle. Therefore, for the fatigue evaluation of accumulated work in a seated state such as driving, or the direct evaluation of whether or not the seat has a low accumulation of fatigue even if seated for a long time, the method according to the present invention is used, It is preferable to target muscles with low glycolytic ability, that is, muscles with little movement including many muscle fibers involved in sustained tension generation.

  6 to 11 show frequency analysis results regarding the power value gradients and the maximum Lyapunov exponent gradients shown in FIGS. FIG. 6A is a frequency analysis of power value gradients for the left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, right gastrocnemius, and fingertip plethysmogram, and the power for the measurement time of 0 to 90 minutes It is the figure which showed the spectrum collectively. However, in FIG. 6 (a), the power spectrum of the left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, and right gastrocnemius other than the fingertip volume pulse wave is applied to the total weight by the correction means described above. It is shown as a value multiplied by the proportion of the mass borne by each muscle. Here, since the weight of the subject was 60 kg, each of the left and right lumbar gastrocnemius was multiplied by 40/60, and each of the left and right gastrocnemius was multiplied by 5/60. FIG. 6 (b) is a frequency analysis of the maximum Lyapunov exponent slope for the left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, right gastrocnemius, and fingertip plethysmogram, with a measurement time of 0-90 minutes. It is the figure which showed the power spectrum collectively.

  From FIG. 6A, it can be clearly determined that the power spectrum of the right lumbar gluteal muscle approximates the power spectrum of the fingertip volume pulse wave, which is an index of the fatigue level of the entire body.

  The result of the frequency analysis of the power value gradient in FIG. 6A is divided into 0 to 60 minutes and 60 to 90 minutes of the measurement time, and the left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, right gastrocnemius, finger FIGS. 7A, 8A, 9A, 10A, and 11A are diagrams shown for each plethysmogram. From these figures, it is obvious that the absolute value of the peak value of the power spectrum is the largest in the right lumbar gastrocnemius in FIG. 8 (a), and the degree of muscle fatigue is large in the right lumbar gastrocnemius. I understand. Moreover, when this was compared with the frequency analysis of the finger plethysmogram in FIG. 11A, the whole body felt fatigue between 0 and 60 minutes, but between 60 and 90 minutes, It can be seen that fatigue has accumulated in the right lumbar gastrocnemius muscle, which is a less responsive muscle.

  On the other hand, the result of frequency analysis of the maximum Lyapunov exponent slope in FIG. 6B is divided into 0 to 60 minutes and 60 to 90 minutes of measurement time, and left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, right FIGS. 7B, 8B, 9B, 10B, and 11B show the gastrocnemius and fingertip volume pulse waves. In the frequency analysis of the maximum Lyapunov exponent slope, the peak value of the power spectrum shifts to the low frequency region with the passage of time, and when the peak value becomes small, an indicator of whether fatigue has increased and functional deterioration has occurred. Become. The frequency of the peak value of the left lumbar gluteal muscle in FIG. 7B is almost the same, but the value is smaller in 60 to 90 minutes than in 0 to 60 minutes. Therefore, it can be determined that the left lumbar gluteal muscle is at a stage where a decline in function has started. In the right lumbar gluteal muscle of FIG. 8B, the peak value shifts to the low frequency region and the value is smaller at 60 to 90 minutes than at 0 to 60 minutes. Therefore, it can be seen that the functional deterioration of the right lumbar gluteal muscles occurred in 60 to 90 minutes. In the left and right gastrocnemius muscles in FIGS. 9B and 10B, the peak value slightly shifts to the low frequency region, the value is almost the same or slightly smaller, and the function starts to deteriorate. The same applies to the finger plethysmogram in FIG. 11 (b), and it can be seen that functional degradation has started to occur throughout the body. Thus, by performing the frequency analysis, it is possible to more reliably determine a part where fatigue has occurred, a time zone in which fatigue has started to occur, and the like.

  FIG. 12A shows an integrated value calculated by performing absolute value processing on the time series signal of the power value inclination by the fatigue degree calculating means 29, and FIG. 12B shows the time series signal of the maximum Lyapunov exponent inclination. Indicates the integral value calculated by absolute value processing. FIG. 12 also shows data of left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, right gastrocnemius, and fingertip volume pulse wave.

  As is apparent from this figure, the gradient of the right lumbar gluteal muscle greatly changes around 65 minutes, and the muscle is rapidly used at that time, and the feeling of fatigue has increased after that time. As for the left lumbar gluteal muscle, it can be seen that the feeling of fatigue slightly increased during the same time period. In contrast, the left and right gastrocnemius muscles do not show a significant change from FIG. Further, the fingertip volume pulse wave shows a sudden change in the gradient around the first 10 minutes, but thereafter changes almost linearly at a constant rate. Therefore, it can be seen from this figure that although the fatigue of the whole body is gradually increasing, the feeling of fatigue is easily reduced by allowing an appropriate body movement, and it is not a sheet that feels a great acceleration of fatigue. However, it can be seen that at around 65 minutes, muscular fatigue of the lumbar gluteal muscles with low glycolytic ability is rapidly promoted due to sustained long-term muscle activity at low strength.

FIG. 1 is a diagram for explaining a measurement method using a muscle fatigue evaluation apparatus according to an embodiment of the present invention. FIG. 2 is a block diagram showing the configuration of the data processing / determination means of the above embodiment. FIG. 3 (a) is a diagram showing a time-series waveform of the power value slope and the maximum Lyapunov exponent slope of the left lumbar gluteal muscle, and FIG. 3 (b) is the power value slope and the maximum Lyapunov slope of the right lumbar gluteal muscle. It is a figure which shows the time series waveform of an exponential inclination. FIG. 4A is a diagram showing a time-series waveform of the power value inclination and the maximum Lyapunov exponent inclination of the left gastrocnemius (the gastrocnemius inside the left foot), and FIG. 4B shows the right gastrocnemius (the gastrocnemius inside the right foot). It is a figure which shows the time-sequential waveform of a power value inclination and the maximum Lyapunov exponent inclination. FIG. 5 is a diagram showing time-series waveforms of the power value gradient and the maximum Lyapunov exponent gradient of the fingertip volume pulse wave. FIG. 6A is a frequency analysis of power value gradients for the left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, right gastrocnemius, and fingertip plethysmogram, and the power for the measurement time of 0 to 90 minutes FIG. 6B is a frequency analysis of the maximum Lyapunov exponent slope for the left lumbar gastrocnemius, right lumbar gastrocnemius, left gastrocnemius, right gastrocnemius, and fingertip volume pulse waves. It is the figure which showed the power spectrum of 0 to 90 minutes of measurement time collectively. FIG. 7A is a diagram in which the frequency analysis result of the power value inclination of the left lumbar gluteal muscle of FIG. 6A is divided into measurement times of 0 to 60 minutes and 60 to 90 minutes. (B) is the figure which divided the result of the frequency analysis of the maximum Lyapunov exponent inclination of the left lumbar gluteal muscle of FIG.6 (b) into 0 to 60 minutes of measurement time, and 60 to 90 minutes. FIG. 8A is a diagram in which the frequency analysis result of the power value gradient of the right lumbar gluteal muscle in FIG. 6A is divided into measurement times of 0 to 60 minutes and 60 to 90 minutes. (B) is the figure which divided | segmented the result of the frequency analysis of the maximum Lyapunov exponent inclination of the right lumbar gluteal muscle of FIG.6 (b) into 0 to 60 minutes of measurement time, and 60 to 90 minutes. FIG. 9A is a diagram in which the result of frequency analysis of the power value inclination of the left gastrocnemius (gastrocnemius inside the left foot) in FIG. 6A is divided into measurement times of 0 to 60 minutes and 60 to 90 minutes. FIG. 9B is a diagram in which the result of frequency analysis of the maximum Lyapunov exponent inclination of the left gastrocnemius muscle (gastrocnemius of the left foot) in FIG. 6B is divided into measurement times of 0 to 60 minutes and 60 to 90 minutes. It is. FIG. 10A is a diagram in which the result of frequency analysis of the power value gradient of the right gastrocnemius (gastrocnemius inside the right foot) in FIG. 6A is divided into measurement times of 0 to 60 minutes and 60 to 90 minutes. FIG. 10B is a diagram in which the frequency analysis result of the maximum Lyapunov exponent inclination of the right gastrocnemius (gastrocnemius on the inner right foot) in FIG. 6B is divided into measurement times of 0 to 60 minutes and 60 to 90 minutes. It is. FIG. 11A is a diagram in which the frequency analysis result of the power value gradient of the fingertip volume pulse wave in FIG. 6A is divided into measurement times of 0 to 60 minutes and 60 to 90 minutes. (B) is the figure which divided | segmented the result of the frequency analysis of the maximum Lyapunov exponent inclination of the fingertip volume pulse wave of FIG.6 (b) into 0 to 60 minutes and 60 to 90 minutes of measurement time. FIG. 12 (a) is a diagram showing an integral value calculated by performing absolute value processing on a time series signal having a power value gradient, and FIG. 12 (b) is showing absolute value processing on a time series signal having a maximum Lyapunov exponent gradient. FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Muscle fatigue evaluation apparatus 10 Myoelectric potential detection sensor 11 Myoelectric electrode 20 Data processing / determination means 21 Power value calculation means 22 Power value inclination calculation means 23 Power value inclination time series waveform creation means 24 Maximum Lyapunov index calculation means 25 Maximum Lyapunov index Inclination calculation means 26 Maximum Lyapunov exponent inclination time series waveform creation means 27 Fatigue determination means 28 Fatigue state confirmation means 29 Fatigue degree calculation means

Claims (5)

A muscle fatigue evaluation apparatus comprising a myoelectric potential detection sensor and data processing / determination means for processing signal data obtained from the myoelectric potential detection sensor, and evaluating a person's muscle fatigue,
The data processing / determination means includes
From the signal data of the myoelectric potential detection sensor, for each predetermined time range, a maximum value and a minimum value are obtained by a smoothing differential method, and a power value calculating means for obtaining a difference between both as a power value;
Power value slope calculating means for sequentially obtaining the slope of the power value with respect to the time axis in a predetermined time range at a predetermined overlap time;
Creating a time series waveform of the power value slope obtained by the power value slope calculating means, and outputting the time series waveform power value slope time series waveform creating means;
Frequency analysis means for frequency analysis of the slope of the power value obtained by the power value slope calculation means;
Have
The power spectrum obtained by the frequency analysis of the power value slope frequency analysis of the time series waveform of the power value slope is borne by the muscle that is the object of myoelectric potential measurement for the total weight. A correction means for correcting to a value proportional to the mass ratio is provided, and among the power spectra obtained for each muscle to be measured, a muscle having a larger power spectrum value obtained by the correction means has a higher degree of muscle fatigue. The muscle fatigue evaluation apparatus characterized by the above-mentioned structure . The data processing / determination means further includes maximum Lyapunov exponent calculation means for calculating a maximum Lyapunov exponent by performing chaos analysis on the signal data of the myoelectric potential detection sensor,
A maximum Lyapunov exponent slope calculating means for sequentially obtaining a slope of the maximum Lyapunov exponent with respect to a time axis in a predetermined time range at a predetermined overlap time;
Claim 1, characterized in that said maximum to create a time-series waveform of the maximum Lyapunov exponent slope determined by the Lyapunov exponent inclination calculation means, and a maximum Lyapunov exponent gradient time series waveform generating means for outputting a time series waveform The muscle fatigue evaluation apparatus as described. 3. The muscle fatigue according to claim 2, wherein the data processing / judgment means further comprises frequency analysis means for frequency analysis of the maximum Lyapunov exponent inclination obtained by the maximum Lyapunov exponent slope calculation means. Evaluation device. Furthermore, a fingertip volume pulse wave measurement unit is provided, and the data processing / determination unit performs a power value inclination and frequency analysis of the fingertip volume pulse wave signal data obtained from the fingertip volume pulse measurement unit. Having means,
The power spectrum of the fingertip volume pulse wave is compared with the power spectrum of corrected myoelectric potential obtained for each muscle to be measured. The muscle fatigue evaluation apparatus according to any one of claims 1 to 3, wherein the degree of muscle fatigue is determined to be greater as the spectrum and the tendency are closer. The data processing / determination means further performs absolute value processing on the power value slope obtained by the power value slope computing means or the maximum Lyapunov exponent slope obtained by the maximum Lyapunov exponent slope computing means, and the integrated value The muscular fatigue evaluation apparatus according to any one of claims 2 to 4 , wherein a fatigue degree calculation means for calculating s is set.

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