WO2001012270A1 - Method for determining exercise strength and device using the same - Google Patents

Abstract

An exercise machine detects an electrocardiac signal at the start of exercise (ST1), starts the control of the exercise load (ST4), and calculates the power values of the fluctuations of the cardiac rate and cardiac interval (ST5, ST6). After two minutes from the start of the warm-up (ST7), the machine automatically controls the Ramp load (ST9). The processing at ST9 includes presetting of the rate of variation of the Ramp load after three, four, fiv e, and six, from the start of the warm-up. As a result, the machine enables the user to exercise under a load most suitable individually.

Description

 Description Method for determining exercise intensity and apparatus using the same

 The present invention relates to a method for determining an exercise intensity that is optimal for an individual, and an exercise device and an exercise intensity determination device capable of exercising with the exercise load. Background art

 When performing a simple physical strength measurement using an exercise load device such as an ergobike, for example, the heart rate is measured during a gradually increasing load (hereinafter referred to as a “ramp load”), and the physical strength level is determined from the relationship between the load and the heart rate. Perform an evaluation. At that time, it is necessary to give an appropriate load fluctuation rate of the ramp load (here, it indicates the gradually increasing load) according to the physical strength level.

 Conventionally, personal data such as age, gender, and weight are input, and the load fluctuation rate of the Ramp load that matches the standard physical strength in the input personal data is provided. FIG. 65 shows an example of a flowchart for determining the load fluctuation rate of the Ramp load in this case. In the flow chart of FIG. 65, it is determined whether or not the age is 60 or more (ST91), further, whether or not the weight is 4 O kg or less (ST92), and whether or not the weight is 80 kg or more (ST93). After each judgment result, it is judged whether it is male or female (ST 94, ST 96, ST 98), and the load fluctuation rate of the Ramp load is set to 5, 10, 15, 20 [W / min] based on the result. Decided (ST95, ST97, ST99, ST100).

 Also, in addition to the personal information, input the presence or absence of physical strength (high physical strength person, general person, low physical strength person, etc.), or determine the load fluctuation rate of the Ramp load using the result of the previous physical strength measurement. I have. An example of inputting the presence or absence of physical strength in this case is as follows. Man woman

 For general users: 1 5 10

 For people with low fitness: 8 5

 For high fitness people: 25 1 5

[Unit: W / min] Before exercise, if you select the above-mentioned physical fitness level to which you belong, together with personal information such as age and '14, a preset load fluctuation rate of the above-mentioned Ramp load is provided.

 However, when determining the load fluctuation rate of the Ramp load according to the input personal data, there is, of course, a possibility that an excessive load may be applied to a subject having a lower than standard physical strength. On the other hand, for subjects who are superior to the standard physical fitness, there is a problem that the physical fitness measurement takes too much time, and an accurate evaluation of the physical fitness level cannot be performed. In addition, it is necessary to input personal data such as age, gender, weight, etc. in advance, which is not only complicated and cumbersome, but also for those who do not want to disclose personal information to others because they input personal information. There are.

 In addition, when determining the load fluctuation rate of Ra immediate load using the presence or absence of physical strength in addition to personal information, if the user does not know which physical strength level to apply to, the user must input the physical strength level. There are mistakes. Also, when determining the load fluctuation rate of the Ramp load using the results of the physical fitness measurement, the physical fitness measurement must be performed in advance, which is troublesome.

 On the other hand, a device for knowing an appropriate exercise level is also disclosed. For example, a prior art for determining exercise intensity (exercise level) according to the level of heartbeat fluctuation is disclosed in, for example, Japanese Patent Application Laid-Open No. Hei 9-509877. According to the publication, a method of determining exercise intensity of an exerciser based on a heart rate fluctuation value during exercise is disclosed. According to this technique, the exercise intensity is determined based on the heart rate fluctuation value during exercise, because the heart rate fluctuation value monotonously decreases as the exercise intensity increases.

 In addition, the heart rate variability tends to decrease as the exercise intensity increases, or the spectrum power derived from the heart rate, HI (0-0.15 HZ) and LO (0.15-5-1). 0 HZ) also tends to decrease as exercise intensity increases.

On the other hand, it has been reported that the absolute change in heart rate variability during exercise is significantly smaller in diabetic patients than in healthy subjects. Autonomic Nervous Activity ”Toshio Moriya, Graduate School of Human and Environmental Studies, Kyoto University, 6 others]. However, in the above-described conventional exercise intensity determination method, the exercise intensity is not particularly problematic in a healthy person.As reported in the above-mentioned literature, the exercise intensity is determined in a subject such as a diabetic patient who has a monotonous fluctuation pattern. However, the range in which the exercise intensity determination method can be applied is limited.

 In addition, in the above-described conventional exercise intensity determination method, the exercise intensity is determined only by the heartbeat fluctuation, and the subject's health is determined by the fluctuation fluctuation pattern during exercise, focusing on the difference in the fluctuation fluctuation pattern due to a disease state such as diabetes. The fact is that no method has been devised for detecting the state.

 The present invention has been made in view of the above-mentioned problems, and is an exercise machine capable of exercising with an optimal exercise load for an individual; a physical fitness level evaluation device for accurately evaluating an individual's physical fitness level; The purpose of the present invention is to provide an exercise intensity determination device that determines the optimal exercise intensity for an individual.

 Another object of the present invention is to provide a method for determining exercise intensity capable of determining an optimal exercise intensity for an individual irrespective of a healthy person or a diseased person such as diabetes, a device for assisting in discriminating a health condition from exercise, a health condition It is an object of the present invention to provide a device for measuring the weight of an animal, and an exercise machine having those functions. Disclosure of the invention

 An exercise machine according to the present invention includes: an exercise load section having a variable exercise load; a physiological signal measuring section for non-invasively measuring a physiological signal during exercise by the exercise load section; and a physiological signal obtained during exercise. A load determining unit that determines a load change rate of the gradually increasing load or the gradually decreasing load, wherein the exercise load unit changes the exercise load based on the load change rate of the gradually increasing load or the gradually decreasing load determined by the load determining unit.

In this exercise equipment, the load fluctuation rate of the gradually increasing load or the gradually decreasing load that is appropriate for the individual is determined, and the exercise load changes according to the determined load fluctuation rate of the load. For this reason, there is no need to enter personal information such as age, gender, weight, etc., nor to measure physical fitness in advance, and even if you do not know your physical fitness level, the optimal load for each individual You can exercise with no overload and, on the other hand, do not overload. In another aspect of the present invention, the physical strength level evaluation device includes a physiological signal measuring unit that non-invasively measures a physiological signal during exercise, and a load of a gradually increasing load or a gradually decreasing load based on the physiological signal obtained during the exercise. A load determining unit that determines the rate of change, and an evaluation of the physical fitness level based on the relationship between the load value and the heart rate during the gradually increasing or decreasing load exercise according to the load variation rate of the gradually increasing or gradually decreasing load determined by the load determining unit. And a physical fitness level evaluation unit that performs

 With this physical fitness level evaluation device, the physical fitness level of each person can be accurately evaluated by performing exercise.

 In still another aspect of the present invention, the exercise intensity determining device includes a physiological signal measuring unit that non-invasively measures a physiological signal during exercise, and a gradually increasing or decreasing load based on the physiological signal obtained during the exercise. Optimum from the relationship between the exercise load and the fluctuation of the heartbeat interval during the gradual or declining load exercise according to the load deciding unit that determines the load fluctuation rate of the gradual load or the gradual load determined by the load deciding unit. And an exercise intensity determination unit that determines an appropriate exercise intensity.

 In still another aspect of the present invention, the exercise intensity determining device includes a physiological signal measuring unit that non-invasively measures a physiological signal during exercise, and a gradually increasing or decreasing load based on the physiological signal obtained during the exercise. A load determining unit that determines the load fluctuation rate of the exercise load and the power of the heart rate variability spectrum during the exercise of the increasing or decreasing load according to the load variation rate of the increasing load or the decreasing load determined by the load determining unit. An exercise intensity determination unit that determines an optimal exercise intensity from the relationship.

 The exercise intensity determination device according to the present invention can determine the optimal exercise intensity for each person by exercise.

 Preferably, the above exercise device further includes an exercise load section that can change the exercise load, and the exercise load section is based on the physical strength level obtained by the physical strength level evaluation device or the exercise intensity obtained by the exercise intensity determination device. Change exercise load.

With this exercise equipment, it is possible to exercise with an optimal exercise load according to each person. In still another aspect of the present invention, a method of determining exercise intensity includes measuring a physiological signal non-invasively during an exercise load, and detecting a change in the physiological signal during the exercise load based on the obtained physiological signal corresponding to the change in the exercise load. The pattern is determined and the fluctuation pattern determined Determine the appropriate exercise intensity.

 According to this determination method, a fluctuation pattern of a physiological signal at the time of exercise load is determined, and an appropriate exercise intensity is determined according to the determined fluctuation pattern. Therefore, not only healthy persons but also persons with diseases such as diabetes and hypertension are determined. It is possible to accurately determine the appropriate exercise intensity. Here, the discrimination of the fluctuation pattern of the physiological signal at the time of the exercise load is performed based on, for example, a warm-up time and a predetermined time interval accompanying the increase of the exercise load, or a change rate of the physiological signal at each exercise load value interval. To determine the pattern. In still another aspect of the present invention, an exercise machine includes a load device having a variable load, a physiological signal measuring unit for measuring a physiological signal non-invasively with time, and an exercise load change obtained by the physiological signal measuring unit. An exercise intensity determining unit that determines a variation pattern of the physiological signal at the time of exercise load based on the physiological signal corresponding to the exercise load, and determines an appropriate exercise intensity according to the determined variation pattern. The load is set to a load corresponding to the exercise intensity determined by the exercise intensity determining unit.

 With this exercise equipment, it is possible to exercise with optimal exercise intensity for individuals regardless of healthy persons or persons with diseases such as diabetes and hypertension.

 In still another aspect of the present invention, an exercise machine includes a load device having a variable load, a physiological signal measuring unit for measuring a physiological signal non-invasively with time, and an exercise load change obtained by the physiological signal measuring unit. And a health condition discriminator for discriminating a fluctuation pattern of the physiological signal at the time of exercise load based on the physiological signal corresponding to the exercise condition, and discriminating a health condition according to the discriminated fluctuation pattern.

 This exercise equipment can check your health by exercising.

 In still another aspect of the present invention, a health condition determination support device includes a physiological signal measuring unit that measures a physiological signal non-invasively with time, and a change in exercise load obtained by the physiological signal measuring unit during exercise load. A fluctuation pattern discriminating unit for discriminating a fluctuation pattern of a physiological signal at the time of exercise load based on a physiological signal, and an output unit for outputting the fluctuation pattern determined by the fluctuation pattern determining unit.

Since this support device determines the fluctuation pattern of the physiological signal during exercise load and outputs the determined fluctuation pattern, whether it is a healthy person or whether there is an abnormality in the autonomic nerves due to a disease such as diabetes or hypertension. To know the output fluctuation pattern Can be determined.

 In still another aspect of the present invention, a measurement device includes: a physiological signal measuring unit that measures a physiological signal over time in a non-invasive manner; and a physiological signal corresponding to a change in exercise load obtained by the physiological signal measuring unit. A health condition discriminating unit for discriminating a fluctuation pattern of a physiological signal during exercise load, and discriminating a health condition according to the discriminated fluctuation pattern; and an output unit for outputting the health condition discriminated by the health condition discrimination unit.

 If this measuring device is incorporated in a bicycle ergometer, for example, it will be possible to know its own health by exercising. BRIEF DESCRIPTION OF THE FIGURES

 FIG. 1 is a block diagram showing a circuit configuration of a bicycle ergometer according to an embodiment of the exercise equipment of the present invention.

 FIG. 2 is an external perspective view of the bicycle ergometer.

 FIG. 3 is a diagram showing a state in which another example of the electrocardiographic sensor used in the bicycle ergometer is worn on an exerciser.

 FIG. 4 is a diagram showing a state in which another example of an electrocardiographic sensor used in the bicycle ergometer is attached to an exerciser.

 FIG. 5 is a diagram showing a state in which a pulse sensor used in the bicycle ergometer is attached to an exerciser.

 FIGS. 6A and 6B are classification tables of the fluctuation peak values used for the automatic ramp load control in the flow chart of FIG.

 FIG. 7 is a front view showing an example of the operation of the bicycle ergometer.

 FIG. 8 is a flowchart following the flowchart of FIG.

 FIG. 9 is an explanatory diagram for obtaining the exercise load, which is the optimal exercise intensity, from the convergence point of the fluctuation power.

 FIG. 10 is a flowchart showing a process for determining a convergence point of fluctuation.

 FIG. 11 is a flowchart showing a process for determining the convergence point of the fluctuation, together with FIG.

FIGS. 12A and 12B show the relationship between the Ramp load and the fluctuation power. FIG. 13 is a flowchart showing the processing of the automatic ramp load control in the flowchart of FIG.

 FIGS. 14A to 14C are diagrams showing display examples of determining the optimal exercise intensity.

 FIGS. 15A to 15C are diagrams showing display examples of exercise intensity.

 FIGS. 16A to 16C are diagrams for explaining a state in which the training mode at the optimal exercise intensity is entered.

 FIGS. 17A to 17C are explanatory diagrams for obtaining a physical strength level from the relationship between exercise load and heart rate.

 FIGS. 18A and 18B are diagrams showing display examples when the physical strength level is determined.

 FIG. 19 is a flowchart showing the process of S Ta in the flowchart of FIG.

 FIG. 20 is a flowchart showing the process of STb in the flowchart of FIG.

 FIGS. 21 and 22 are flowcharts showing the processing of STc in the flowchart of FIG. FIG. 23 is a flowchart showing the process of ST d in the flowchart of FIG.

 FIG. 24 shows 28-year-old male data for which the optimal exercise intensity was determined using the bicycle ergometer of the embodiment.

 FIG. 25 shows data of a 23-year-old woman whose optimal exercise intensity was determined using the bicycle ergometer of the embodiment.

 FIGS. 26A and 26B are diagrams showing patterns a and b of power fluctuation patterns of heartbeat interval fluctuations during exercise.

 FIGS. 27A and 27B show patterns c and d of the fluctuation pattern of the power of the fluctuation of the heartbeat interval during exercise.

 FIG. 28 is a flowchart showing an example of the operation of the bicycle ergometer.

 FIGS. 29A to 29C are diagrams showing display examples of determination of the optimal exercise intensity in the bicycle ergometer.

 FIGS. 30A to 30C are diagrams showing display examples of exercise intensity after the display of the optimal exercise intensity determination display.

 FIG. 31 is a diagram for explaining an example of executing an exercise program at an optimal exercise intensity.

FIG. 32 is a flowchart showing the pattern determination processing in the flowchart of FIG. Fig. 33 is a flowchart showing the processing for determining the exercise levels a and b in the flowchart of Fig. 32. FIG.

 FIGS. 34A to 34C are diagrams illustrating a method of determining exercise intensity from the convergence point of fluctuation power.

 FIG. 35 is a flowchart showing a process of determining the exercise level c in the flowchart of FIG.

 FIG. 36 is a view for explaining another example of determining exercise intensity from fluctuation power. FIG. 37 is a flowchart showing a process of determining the exercise level d in the flowchart of FIG.

 FIG. 38 is a view for explaining still another example of determining exercise intensity from fluctuation power.

 FIG. 39 is a flowchart showing another example of the determination process of the exercise level c in the flowchart of FIG.

 FIG. 40 is a plan view showing the display unit of the display unit in the operation unit of the bicycle ergometer.

 FIGS. 41A and 41B are plan views showing specific display examples by the display unit of FIG. FIGS. 42A and 42B are diagrams illustrating display examples of fluctuation patterns of fluctuation power. FIG. 43 is a flowchart showing another example of the operation of the bicycle ergometer.

 FIG. 44 is a flowchart following the flowchart of FIG.

 FIG. 45 is a diagram showing a fluctuation pattern of fluctuation power with respect to exercise load. FIG. 46 is a classification table used for pattern discrimination of a variation pattern in the flowcharts of FIGS.

 FIG. 47 is a diagram showing where classifications a to e in the table of FIG.

 FIG. 48 is a flowchart showing an example of the fluctuation power fluctuation pattern discrimination processing in detail.

 FIG. 49 is a flowchart showing a process 3 in the flowchart of FIG.

 FIG. 50 is a flowchart showing a process 4 in the flowchart of FIGS. 50 and 48.

 FIG. 51 is a flow chart following the branch B in the flowchart of FIG.

FIG. 52 is a flow chart following the branch D in the flow chart of FIG. FIG. 53 is a flow chart following the branch E in the flow chart of FIG.

 FIG. 54 is a flow chart following the branch C in the flow chart of FIG.

 FIG. 55 is a flowchart showing a process 2 in the flowchart of FIG.

 FIG. 56 is a flow chart following the branch G in the flow chart of FIG.

 FIG. 57 is a flow chart following the branch F in the flow chart of FIG.

 FIG. 58 is a flow chart following the branch I in the flow chart of FIG.

 FIG. 59 is a flowchart following the branch H in the flowchart of FIG.

 FIGS. 6OA to 63 show patterns a to j in the flowcharts of FIGS. 48 to 59. FIG.

 FIG. 64A is a graph showing the relationship between time and entropy, and FIG. 64B is a graph showing the relationship between time and load.

 FIG. 65 is a flowchart showing a conventional example in which personal information such as age, gender, and weight is input to determine the load fluctuation rate of the Ramp load. BEST MODE FOR CARRYING OUT THE INVENTION

 (1) First embodiment

FIG. 1 is a block diagram showing a circuit configuration of a bicycle ergometer according to an embodiment of the exercise equipment of the present invention. The ergometer includes an electrocardiographic sensor 1 for detecting an electrocardiographic signal, a preamplifier 2 for amplifying the output signal, a filter 3 for removing noise, and an amplifier 4 for amplifying the electrocardiographic signal to an appropriate level. An AZD converter 5, a CPU 6 for executing various processes, a key input device 7, a display device 8 for displaying exercise intensity, body level, etc., and a load device 9 for changing a rotational load. Is provided. The CPU 6 has a load determination function for determining a load fluctuation rate of a gradually increasing load or a gradually decreasing load based on a physiological signal obtained during exercise, and exercise load during exercise according to the determined load fluctuation rate of the gradually increasing or decreasing load. Or the heart rate and the physical fitness level evaluation function that evaluates the physical fitness level, or the fluctuation of the exercise load and the heartbeat interval during the gradually increasing or decreasing load exercise according to the determined load fluctuation rate of the increasing or decreasing load It has a function to determine the optimal exercise intensity from the relationship between the exercise load and the exercise load and the power of the heart rate variability vector. FIG. 2 is an external perspective view of the bicycle ergometer. In FIG. 2, the enolegometer includes a saddle 11, a handle 12, an operation unit 13 having a key input device 7, a display 8, an alarm (not shown), a pedal 14, and a front leg. A frame 15 and a rear leg frame 16 are provided. The handle 12 is provided with a pair of electrodes (physiological signal measurement unit) 17 for detecting electrocardiograms. When the exerciser holds the electrode 17 of the handle 12 with both hands during exercise, both hands and the electrode 17 are connected. Contact is made, and the ECG signal is detected from the hand.

 In this ergometer, a subject (exercise person) exercises by sitting on a saddle 11, depressing a pedal 14, and rotating the pedal 14. Exercise load is applied to the pedal 14 so as to have a weight corresponding to the degree of exercise intensity. When the exercise load is large, a large amount of exercise is naturally required to rotate the pedal 14 a fixed number of times. However, this is known per se.

 In addition, in the embodiment of FIG. 2, the electrode 17 for detecting the electrocardiogram is provided on the handle 12, but various changes can be made. For example, in FIG. 3, a chest belt 41 equipped with a pair of electrodes and a transmitting unit is attached to the chest of the exerciser M, and a receiving unit 42 (corresponding to the operating unit 13 in FIG. 2) is provided on the handle 12. Have been. In this case, the electrocardiographic signal detected from the chest of the athlete M [is transmitted to the receiving unit 42 wirelessly and processed.

 In the example shown in FIG. 4, three electrodes 45, 46, and 47 of + (plus), one (minus), and G (ground) are attached to the chest of the exerciser M. It is a chest-leading type that is connected to the circuit inside the body and detects electrocardiographic signals.

 In the example shown in FIG. 5, the pulse sensor 49 is attached to the earlobe of the exerciser M instead of the electrocardiographic sensor, and the pulse is detected.

 Exercise equipment configured in this way, during exercise, based on physiological signals to the change in exercise load, such as the electrocardiogram (electrocardiogram signal) and pulse wave signal (pulsation signal) detected by the electrocardiogram sensor ゃ pulse sensor The load fluctuation rate of the gradually increasing load or the gradually decreasing load is determined based on the physiological signal obtained in step (1). Next, an example of a method for obtaining the load fluctuation rate of a gradually increasing load or a gradually decreasing load will be described. Here, the physiological signal is assumed to be the power of the fluctuation of the heart rate and the heartbeat interval, and a method of obtaining a gradually increasing load (hereinafter, referred to as a Ramp load) will be described.

First, the power of heart rate and heart rate fluctuations during exercise is detected. The heart rate is Is calculated as follows. During exercise, the peak of the ECG signal detected from the electrode 17 (Fig. 2) provided on the handle 12 of the ergometer is detected, and the RR interval data (one cycle of the heartbeat) is calculated. For example, the heart rate is calculated from the average value of five beats at that interval. Also, the fluctuation power (Power) is calculated by the following equation (1),

Power (n) [ms ² ] = {RR (n) one RR (n-1)} ² ··· (1) This is the square of the difference between the previous and current one cycle, and is referred to here as the power of the heartbeat fluctuation. In this Power data, for example, the average value for 30 seconds is calculated at 15 second intervals.

 Using the calculated heart rate and fluctuation power, for example, using the value of the Ramp load automatic control point shown in Fig. 6A, the load fluctuation rate of the Ramp load is determined. Here, it is divided into points a to e and thereafter. Specifically, the point a is defined as the value at the time of warming up (Wup), and the average value of one minute from the elapse of one minute out of two minutes of warming up is defined as Wup. Then, as point b, the value after 2 minutes from the end of warm-up (P 2min), as point, 3 minutes later (P 3 min), as point d, 4 minutes later (P4min), as point e The value after 5 minutes (P 5 min) is used, and similarly, every 6 minutes after the end of warm-up, the value every minute is used. These points a to e show the typical (standard) fluctuation power during the Ramp-load motion of the ergometer, which shows an exponential decreasing trend, as shown in Fig. 6 (b). Corresponds to position.

 Next, FIGS. 7 and 8 show an example of a flow chart for determining the optimal exercise intensity using the automatic control of the Ramp load in the above exercise equipment (ergometer). However, the optimal exercise intensity here is, for example, the convergence point of fluctuation power during exercise load, and the optimal exercise intensity is the exercise load at the time of convergence point display.

In FIG. 7, when the measurement start key of the key input device 7 in FIG. 1 is pressed, the measurement is started. First, in step (hereinafter abbreviated as ST) In ST1, an electrocardiographic signal is detected by the electrocardiographic sensor 1, and a calibration operation is performed so that the signal from the electrocardiographic sensor 1 becomes a certain level (ST2). . This calibration operation is performed by adjusting the gain in the amplifier 4 based on a signal from the CPU 6. After the calibration is completed, "Start measurement" is displayed on the display 8 (ST3), and the control of the load device 9 is started. Start (ST 4). As this control, for example, after forming up for 2 minutes at an initial load value of 15 [w], a ramp load of 10 [WZmin] is initially applied.

 Next, the peak value of the electrocardiographic signal is detected, and the heart rate and the fluctuation are calculated as described above (ST5, ST6). At this time, the average heart rate and fluctuation power during warm-up are calculated. For example, in a 2-minute warm-up, an average value (HR of Wup) for 15 seconds after a lapse of 1 minute and 30 seconds is calculated as a heart rate, and the Wup value is used as the power of fluctuation (see Fig. 6A). ] Is calculated. The calculation of the heart rate and fluctuation power is continued until two minutes have elapsed since the start of warm-up (ST 7). After 2 minutes, it is determined whether it is the Ramp load control timing (ST8). If Yes, an appropriate Ramp according to the physical strength level of each individual is used at any time based on the measured heart rate and fluctuation power. The load fluctuation rate of the load is set (ST 9).

 The automatic control process of the ramp load is performed as shown in Fig.13. That is, if three minutes have passed since the start of warming-up, the process proceeds to step S (a) (ST81). If four minutes have elapsed, the process proceeds to STb (ST82). If five minutes have passed, the process proceeds to STc (ST83). After that, the process proceeds to STd (ST84). Each processing will be described later.

 The convergence determination is performed following the processing of S7 in FIG. 7 (ST10). One

 When the following conditions (a) and (b) are satisfied in the fluctuation characteristics of the fluctuation power, the convergence judgment Y e s is determined and the optimal exercise intensity is determined.

 (a) The fluctuation power falls below a predetermined power base value (baseline).

(b) The difference from the power of the previous fluctuation [Power {T (n-1)}-Power {T (η)}: the gradient of the fluctuation curve of the fluctuation power] is equal to or less than the specified gradient. Note that the optimal exercise intensity may indicate a heartbeat value or an exercise load at the convergence point shown in FIG.

Fig. 9 shows an example of determining the optimal exercise intensity. In Fig. 9, the exercise load at the intersection of the time-exercise load characteristics from the convergence point of the fluctuation is determined as the optimal exercise intensity. The method of determining the convergence point will be described with reference to the flowcharts of FIGS. 10 and 11. This flow diagram is a standard 9 shows a method of determining a convergence point used for determining the exercise intensity of a simple pattern. The processing from ST61 to ST65 is the same as ST1 to ST5 in FIG. That is, when the measurement start key of the key input device 7 in FIG. 1 is pressed, the measurement is started. First, an electrocardiographic signal is detected by the electrocardiographic sensor 1 (ST61), and a calibration operation is performed so that the signal from the electrocardiographic sensor 1 becomes a certain constant level (ST62). This calibration operation is performed by adjusting the gain in the amplifier 4 based on a signal from the CPU 6. After the calibration is completed, “Start measurement” is displayed on display 8.

(ST63), the control of the load device 9 is started (ST64). As this control, for example, after performing a warm-up for 2 minutes at an initial load value of 20 [w], a lamp load of 15 [w] per minute is applied.

Next, the peak value of the electrocardiogram signal is detected, and the fluctuation power is calculated from the calculation formula (1) (ST65). After the calculation, it is determined whether or not 2 minutes have passed during warm-up (ST66), and if not, the process returns to ST65. After lapse of 2 minutes after completion of the warm-up period, ST 67 is YE S, and the power base value of 25 [ms ^2], and the inclination ^{6 [ms 2] (ST 70} , 71).

 Subsequently, convergence determination (ST68) is performed. This is because in the fluctuation characteristics of the fluctuation power shown in Fig. 12 (change of the fluctuation power and the exercise load with time), the fluctuation power decreases and converges as the exercise load increases. The convergence point of the fluctuation curve of the power of this fluctuation is the AT point. Here, as the determination of the convergence point corresponding to this AT point, the power of the fluctuation falls below a predetermined reference value and the difference from the previous power value [Power {T (n-1)} -Power {T ( η)}: Slope of the fluctuation curve of the fluctuation power) reaches a predetermined reference value (power base value) or less. That is, when it cannot be determined that the convergence point is obtained, the exercise load is gradually increased with the determination NO (ST69), and the processing of ST66 to ST68 is repeated. If the convergence point is determined, the exercise intensity corresponding to the calculated load value is displayed on the display 8 as a result (ST72). In these flowcharts, the fluctuation power used for pattern classification is as shown in FIG. 6A, and classifications a to e correspond to the positions shown in FIG. 6B in fluctuation power.

By the way, in ST 10 of FIG. 8, when it cannot be determined that the convergence point exists, If the degree cannot be determined, the judgment is No, the load is gradually increased according to the set Ramp load (ST11), and the process returns to ST5 to continue the heart rate calculation, fluctuation power calculation and convergence judgment.

 On the other hand, when the optimal exercise intensity is determined, a load value one minute before the determination of the optimal exercise intensity is calculated, and the exercise load corresponding to the calculated optimal exercise intensity is displayed on the display 8 as a result (ST12). As shown in Fig. 14A → 14B → 14C, the display example shows “Optimal luck”, “Dynamic strength” and “Determination” while scrolling horizontally. Exercise intensity may be displayed as shown in B, 15C. In this example, the point at which multiple levels of “level 5” are displayed as the load level is set as the best mode (Fig. 15A), and the other display examples are “heart rate (beat Z)” [Fig. 15 B], and “exercise load” W [Fig. 15C]. After displaying the results, the exercise load is reduced and the exerciser is allowed to cool down for a certain period of time (for example, 1 minute) (ST13). Thereafter, the exercise load control ends (ST14).

 In the flowcharts in Figs. 7 and 8 above, after the optimal exercise intensity is determined, the exercise is terminated after the cool-down has been performed.However, without ending the exercise, the load is temporarily reduced to 1Z2 of the optimal exercise intensity. It is also possible to have a training program in which the exercise is performed for approximately one minute after the exercise is reduced to a certain degree and then increased again to the optimal exercise intensity to enter the training mode at the optimal exercise intensity.

 As a specific example of a training program that enters the training mode with optimal exercise intensity, as shown in Figs. 16A to 16C, after deciding the optimal exercise intensity (a in Fig. 16A), the exercise load is optimized once. The exercise intensity is reduced to about 1Z2 (Fig. 16A, b), and the exercise with the exercise load is performed for about 1 minute, and then increased again to the determined optimal exercise intensity (Fig. 16A, c), and the optimal exercise is performed. Run an intensity-controlled exercise program.

Also, in the process of evaluating the physical fitness level, the automatic control of the Ramp load is used in the same manner as described above, and the physical fitness level can be determined from the relationship between the heart rate and the load value during the Ramp exercise. A specific example of determining the physical strength level will be described with reference to FIGS. 17 to 18B. First, the heart rate and exercise obtained during Ramp exercise using automatic Ramp exercise control (from the end of warming up to 75% HRamx, for example). Load relationship [Fig. 17B] From the force, the maximum predicted exercise load (Wmax [w]) corresponding to the maximum predicted heart rate (HRmax [bpm]) is calculated [Fig. 17C]. In the example of 75 <% HRma _X , the age is input from the key input section 7, HI max = 2 20—age is calculated internally, and the ramp rate exercise with the heart rate as the value of 5% is calculated. Calculate Wmax [w] from the time and slope. In other words, as shown in FIG. 17C, an approximate straight line is obtained from the relationship between the exercise load and the heart rate after the end of the warm-up, and the exercise load (Wmax [w]) on HRtnax is estimated. Using the calculated Wmax, the maximum oxygen uptake [l / min] is estimated using a known formula, and the result is divided by the body weight to obtain the maximum oxygen uptake per 1 kg of body weight [ml / KgZmin]. Age and gender are used to determine the maximum oxygen uptake, and the estimated maximum oxygen uptake is used to determine the physical fitness level, for example, physical fitness level 1 (poor) to physical fitness level 6 (very good). The determined health level is displayed as shown in Figs. 18A and 18B.

 FIGS. 19 to 23 show specific examples of the flowchart for determining the load fluctuation rate of the ramp load by the automatic ramp load control process (ST 9) in the flowchart of FIG. The symbols for each parameter in these flowcharts are as shown in Figure 6A. Here, the linear pattern of the heart rate during the ramp exercise of the ergometer and the exponential decrease of the power of the fluctuation of the heartbeat interval are used as standard patterns.

Processing of STa shown in Fig. 19 [STa, STb, STc, STd, ... in Figs. 19 to 23 below are 1 minute, 2 minutes, 3 minutes, ... after the warm-up time has elapsed. ST1, ST2, ST3, ST4,... In order to distinguish them from ST1, ST2, ST3,. d, ……] is the case where three minutes have passed since the start of warm-up, as shown in the flowchart of FIG. Calculate the heart rate (HR of ST a) one minute after the end of warm-up. Here, the heart rate (HR of Wup) determined in ST5 of the flow chart of FIG. 7 is compared with the HR of STa (ST15). If the heart rate rise is remarkable, load fluctuation is considered. Reduce the rate. For example, if the increase in heart rate [AHR 1 {(HR of ST a) — (HR of Wup)}] is greater than 15 [bpm], the load variation rate of the current Ramp load will be 5 (W / min). ] To 5 [W / min] (ST 16). If the AHR l is less than 15 [bpm], change the load regulation to 10 [W / min].

 The process of STb shown in FIG. 20 is a case where four minutes have elapsed from the start of warm-up. Calculate the heart rate (HR of STb) and fluctuation power (P2min) two minutes after the end of warm-up (ST21). Here, the HR of Wup and the HR of STb are compared (ST22), and if the heart rate rises remarkably, the load fluctuation rate is reduced. If the rise in heart rate is small and the rate of decrease in fluctuation power is small compared to Wup, increase the load fluctuation rate. For example, if the increase in heart rate [ΔΗ R 2 {(HR of ST b) — (HR of Wup)}] is greater than 20 [bpm], the load fluctuation rate of the current Ramp load is 5 [W / min] to 5 [W / min] (ST 24). However, if the current load change rate is 5 [W / min] or less (ST23), the current load change rate is kept.

 On the other hand, if ΔΗΙ¾ 2 is less than 5 [bpm] (ST 25) and the fluctuation power is larger than the Wup power 2Z3 (ST 26), it is considered that the fluctuation power reduction rate is small, and The load change rate of the load is increased by 5 [W / min]. When the current load change rate is 5 [W / min], it is set to 10 [W / min], and when the current load change rate is 10 [WZmin], it is set to 15 [W / min]. [W / min] (ST 27). If both ST22 and ST25 are NO, the current load regulation is maintained.

The processing of STc shown in FIGS. 21 and 22 is a case where 5 minutes have passed from the start of warming-up. The heart rate (HR of STc) and the power of fluctuation (P3min) 3 minutes after the end of the warm-up are obtained (ST31). Here, the HR of STb and the HR of STc one minute before are compared (ST32), and if the increase in heart rate is remarkable, the load fluctuation rate is relaxed. In addition, when the increase in the heart rate is small and the rate of decrease in fluctuation power is smaller than Wup, the load fluctuation rate is increased. For example, if the increase in heart rate [AHR 3 {(HR of ST c) — (HR of ST b)}] is greater than 15 [b pm], then the load change rate of the current Ramp load will be 5 (W / min) to decrease. For example, when the current load fluctuation rate is 15 [W / min], it is 10 [W / min], and when it is 10 [WZmin], it is 5 [W / min] (ST34). However, if the current load change rate is less than 10 [WZmin] (ST33), the current load change rate is kept. On the other hand, when the absolute value of the fluctuation power is as large as 500 [ms2] or more (ST35), or when the AHR3 is less than 5 [bpm] (ST38), the fluctuation power is larger than the Wup power 1Z2. If it is large (ST39), it is considered that the fluctuation power reduction rate is small, and in all cases, the current load fluctuation rate is increased by 5 [W / rn in], and when the current load fluctuation rate is 5 [WZmin], Set to 10 [WZmin], and when set to 10 [W / min], set to 15 [W / min] (ST37, ST41). However, in the former and latter cases, if the current load fluctuation rate is 15 [W / min] or more (ST 36, ST40), the current load fluctuation rate remains the same. Also, when ST32, ST35, and ST38 are all NO, the current load fluctuation rate is maintained.

 The processing of ST d shown in FIG. 23 is a case after a lapse of 6 minutes from the start of warm-up. For example, heart rate after 4 minutes from the end of warm-up (HR of ST4) (ST "*" in the drawing applies the fractional value according to the elapsed time from the end of warm-up. Here, 4 minutes from the end of warm-up ST "d" [d = 4] because it is an example of progress, and the power of fluctuation (P 4min) (P "*" min in the drawing for the same reason, where * = 4 ) (ST 51). Here, when the absolute value of the fluctuation power is as large as 500 [ms2] or more (ST52), it is considered that the fluctuation rate of the fluctuation power is small, and the current load fluctuation rate is increased by 5 [W / min]. If the current load fluctuation rate is 5. [WZmin], set it to 10 [WZmin], and if it is 10 [WZmin], set it to 15 [W / min] (ST55). However, when the current load change rate is 20 [W / min] or more (ST54), the current load change rate is kept.

On the other hand, the HR of STc and the HR of STd one minute before are compared (ST53), and if the heart rate is significantly increased (NO), the load fluctuation rate is kept as it is. For example, if the increase in the heart rate [AHR4 {(HR of ST d) — (HR of ST c)]] is 5 [bpm] or more, the current load fluctuation rate is maintained. If the determination of ST53 is Yes, and the absolute value of the fluctuation power is larger than the preset power base value (Pbase) (ST56), the current load fluctuation rate is increased by 5 (W / min). When the current load fluctuation rate is 5 [W / min], it is set to 10 [WZmin], and 10 [WZm 1 [n], it is set to 15 [W / min] (ST 58). However, when the current load regulation is 20 [W / min] or more (ST57), the current load regulation is maintained. If the determinations in ST52 and ST53 are both No, the current load fluctuation rate is maintained.

 In this way, after 4 minutes have passed since the end of warm-up, the power of the fluctuation of the heart rate and the heart rate interval is calculated every minute in the same manner, and the heart rate rises from the value one minute ago. The power of the fluctuation of the heartbeat interval is judged from the magnitude of the absolute value, and the load fluctuation rate of the Ramp load is changed based on those results. However, here, the lower limit of the Ramp load is 5 [W / min] and the upper limit is 20 [WZ min], so that it is not set smaller than 5 [W / min] and larger than 20 [W / min]. ing.

As described above, an accurate physical strength level and an optimal exercise intensity can be determined for each person, and an appropriate R _amp load according to each individual's physical strength can be provided. In addition, there is no need to input personal information such as age, gender, and weight before starting exercise, and there is no need to input personal information, which improves usability.

 Actually, using the exercise equipment (ergometer) as described above, the Ramp load was automatically controlled, and the data for determining the optimal exercise intensity are shown in Fig. 24 and Fig. 25. Figure 24 shows

28 Shows data for an 8-year-old male with a higher level of physical fitness. In this case, the load fluctuation rate of the Ramp load is fixed at 10 [WZmin] at the beginning after the warm-up ends, but becomes 15 [WZmin] on the way and further 20 [W / min] Has been changed to The convergence point of the fluctuation power is determined at 8.75 min.

—Figure 25 shows data for a 23-year-old woman, whose physical strength level is that of a normal subject. The load fluctuation rate of the Ramp load has been set to 10 [W / in] from the beginning after the end of warm-up, and has not been changed since then. The convergence point of the fluctuation power is determined at 6.75 min. In Figs. 24 and 25, an auxiliary line is drawn in the vertical direction by a dotted line from the position of the convergence point of the fluctuation, and the relationship between the auxiliary line and the heart rate (bpm) curve (polyline) is shown. The intersection indicates the value of the optimal exercise intensity indicated by the heart rate, and the intersection of the auxiliary line and the curve (line) of the exercise load [w] is indicated by the exercise load. The value of the optimal exercise intensity.

 In the flow chart according to the above embodiment, the power of the fluctuation of the heart rate and the heartbeat interval is used as the physiological signal, but the pulse rate obtained by the pulsation signal may be used instead of the heart rate. In addition, instead of the power of the fluctuation of the heartbeat interval, an event of fluctuation of the heartbeat interval may be used. Alternatively, the power of the heart rate variability spectrum may be used as the physiological signal. Furthermore, the above flow chart is for obtaining a gradually increasing load, but the same applies to a case of obtaining a gradually decreasing load.

 (2) Second embodiment

 Hereinafter, the present invention will be described with reference to a second embodiment.

 In the second embodiment as well, the external view of the bicycle ergometer, the circuit configuration, the method of detecting an electrocardiographic signal, and the like are the same as those in the first embodiment, and a description thereof will be omitted.

 In the second embodiment, a variation pattern of a physiological signal during an exercise load is determined based on a physiological signal corresponding to an exercise load change detected by an electrocardiographic sensor and a pulse sensor, and an appropriate exercise intensity in accordance with the determined variation pattern. Is determined, and the ergometer is controlled so that the intensity of paddling 14 changes according to the determined exercise intensity.

 Next, a method of determining a fluctuation pattern of a physiological signal will be specifically described. Here, the fluctuation power described in the first embodiment is used as a physiological signal. This Power data is averaged for 30 seconds at intervals of 15 seconds as in the first embodiment, and the fluctuation characteristics of fluctuation power with respect to an increase in exercise load are obtained. FIGS. 26A and 26B and FIGS. 27 and 27B show the fluctuation characteristics of the fluctuation. Figure 26A shows the standard pattern (pattern a) found in healthy subjects. According to this, it can be seen that in a healthy person, the fluctuation power drops exponentially when a certain load intensity is exceeded.

On the other hand, FIG. 26B and FIGS. 27A and 27B show examples in which the power fluctuation pattern of the fluctuation of the heartbeat interval during exercise is different from the standard pattern a observed in healthy subjects. The pattern (pattern!) In Fig. 26B is the case where the absolute value of the fluctuation power is relatively small compared to pattern a. Pattern b is also seen in diabetic and obese individuals, but also in healthy individuals. Fig. 27 The pattern of pattern 7A (pattern c) has a significantly smaller absolute value of fluctuation power than pattern a, and an increase in exercise intensity. This is a case where the fluctuation of the fluctuation power with respect to is hardly obtained. Pattern c is a pattern found in diabetic patients and is also common in obese people. In the pattern (pattern d) in Fig. 27B, the power of the fluctuation is drastically reduced at a certain exercise intensity, and the power of the fluctuation tends to decrease exponentially as the exercise intensity increases, as in pattern a. Different from Pattern d is a pattern seen in hypertensives.

 As described above, the fluctuation pattern of the fluctuation power greatly differs between a healthy person and a non-healthy person. Conventionally, for example, the exercise intensity of a weight loss program is fixed at about 65% of the predicted maximum heart rate.However, in patients with diabetes or hypertension, exercise intensity is lower than that of healthy subjects. Is reported to be desirable. Thus, by classifying the above fluctuation patterns, the standard pattern a seen in healthy subjects determines the exercise intensity to be 65% of the predicted maximum heart rate, but the pattern c seen in diabetic patients c Therefore, it is preferable to determine the exercise intensity to be lower than 65% of the predicted maximum heart rate.

 By classifying the above fluctuation patterns, the exercise intensity determined by the above method is determined for the standard patterns a and b observed in healthy subjects. Since the healthy person is converging from the time of warm-up), the lightest exercise intensity, for example, the intensity of warm-up exercise, is determined. Further, for pattern d, the fluctuation power is drastically reduced at a certain exercise intensity, and for example, the exercise intensity immediately before the drastic decrease is determined as the exercise intensity.

 Next, an example of specific processing for determining the exercise intensity is shown in the flowcharts of FIGS. 28 to 33. That is, the flow charts of FIGS. 28 to 33 show an example of a process of calculating the fluctuation power, determining the fluctuation pattern, and determining the exercise intensity according to the determined pattern.

28, when the measurement start key of the key input device 7 of FIG. 1 of the first embodiment is pressed, the measurement is started, and the same processing as that of FIG. 7 of the first embodiment is started. First, in step ST101, an electrocardiographic signal is detected by the electrocardiographic sensor 1, and a calibration operation is performed so that the signal from the electrocardiographic sensor 1 becomes a certain constant level (ST102). This calibration operation is performed by adjusting the gain in the amplifier 4 based on a signal from the CPU 6. After calibration is completed, "Start measurement" appears on display 8. Is displayed (ST 103), and exercise load control of the load device 9 is started (ST 104). As this control, for example, after performing a warm-up for 2 minutes at an initial load value of 20 [w], a ramp load of 15 [w] per minute is applied.

 Next, the peak value of the electrocardiogram signal is detected, and the fluctuation power is calculated from the above-mentioned calculation formula (1) (ST 105). Pattern determination is performed based on the calculated fluctuation power (ST 106).

 The pattern determination process is performed as shown in the flowchart of FIG. In other words, patterns a to d are determined using the magnitude of the absolute value of the fluctuation power at the time of up-timing and the rate of decrease of the fluctuation power with respect to the increase in exercise load. While the pattern judgment and the convergence point of the fluctuation power cannot be determined, the judgment of ST 107 becomes No, the exercise load is gradually increased (ST 108), and the processing of ST 105 to ST 107 is repeated. When the pattern is determined, the exercise intensity according to the pattern is determined. That is, exercise intensity a, b is determined for pattern a or pattern b, exercise intensity c is determined for pattern c, and exercise intensity d is determined for pattern d. When the exercise intensity is determined in ST107, the result is displayed on the display 8 (ST109). The displayed contents include a heart rate [bpm] at an exercise intensity corresponding to the pattern, an exercise load [W], an intensity display for the exercise load, and the like. Examples of the display are shown in Figures 29A, 29B and 29. As shown in the above, “optimal luck”, “dynamic strength”, and “determination” are displayed on the LCD of the display while horizontally scrolling through the screen. After that, as shown in Fig. 3 OA, the exercise intensity is displayed by heart rate display, so that the optimal exercise intensity of the subject at that time can be notified. In addition to the exercise intensity based on the heart rate of OA in Fig. 3, the exercise load [w] or the level of the exercise intensity with respect to the exercise load depends on which of the multiple stages, as shown in Figs. 30B and 30C. , Can be informed. After displaying the results, the exercise load is reduced, the exerciser is allowed to cool down for a predetermined time (for example, 1 minute) (ST110), and the exercise load is terminated (ST111).

 The determined exercise intensity is stored in the storage area in the CPU 6 as it is, and when the exercise is performed next using the load device, the exercise can be performed with the stored exercise intensity. .

After the exercise intensity is determined, the exercise program is continued with the determined exercise intensity. A specific example of this program is executed in the same manner as in FIGS. 16A to 16C of the first embodiment. As shown in Fig. 31A, once the optimal exercise intensity was determined, the exercise load was once reduced to the optimal exercise intensity of about 12 (Fig. 31b), and the exercise with that exercise load was performed for about 1 minute. After that, the exercise intensity is again raised to the determined optimal exercise intensity (FIG. 31c), and the exercise program controlled by the optimal exercise intensity is executed.

 In this example, the pattern d is used.However, for the other patterns a to c, the exercise program with the exercise intensity determined to be optimal for the subject is similarly executed. . In the above, after the exercise intensity was determined, the result was displayed on the display unit 8 to start the cool down. In addition, after the exercise intensity is determined, the result can be displayed on the display 8 and the exercise can be performed while controlling the exercise load as it is without entering the cool down step. . Alternatively, based on the determined exercise intensity, it becomes possible to execute various types of exercise programs such as a weight loss program, a physical strength increase program, and an exercise deficiency elimination program. The determined exercise intensity is the optimal exercise intensity according to the individual's physical condition and the state of the autonomic nervous system at the time of the determination, so that exercise with an appropriate exercise intensity can be performed.

 Exercise intensities a and b (ST 125) in the flow chart of FIG. 32 are determined as shown in FIG. First, it is determined whether or not the convergence point of the fluctuation power can be determined (ST 13 1). If it cannot be determined, the process returns. ST 1 32). In this case, as shown in FIG. 34C, the convergence point is determined, and the exercise load at the convergence point is referred to as shown in FIG. 34C (FIG. 34A), and this exercise load is determined as the exercise intensity of the person. The exercise intensity c (ST 26) is the exercise intensity during warm-up (here, the above-mentioned 20 [w]) as shown in FIG. 35 (ST 133). That is, in the case of the pattern c, as shown in FIG. 36, the exercise load at the end of the warm-up is defined as the exercise intensity of the person. The exercise intensity d (ST 127) is determined as shown in FIG. First, it is determined whether or not the convergence point of the fluctuation power can be determined (ST134). If it cannot be determined, the process returns. If it can be determined, as shown in FIG. 38, it corresponds to immediately before the fluctuation power drastically decreases. The exercise load is defined as the exercise intensity (ST 135).

Regarding pattern c, in the example of the above flow chart, Since the dynamic load was constant (20 [W]), the exercise intensity was determined to be cwo 20 [W]. If the exercise load during warm-up was changed according to personal information such as age, exercise was performed accordingly. The set value of the intensity c may be determined separately. Figure 39 shows a flow chart of one example. In the flow chart of FIG. 39, first, it is determined whether or not the age of the exerciser input through the key input device 7 is 60 years or older (ST 1336). It is determined whether the weight is 40 kg or less (ST137). If the weight is more than 40 kg, it is further determined whether it is 80 kg or less (ST138). If the weight is more than 80 kg, the exercise intensity is determined to be 20 [w] (ST 140).

 On the other hand, when the age is 60 years or older and the weight is 40 kg or less, the exercise intensity is determined to be 15 [w] (ST 141). If the weight is 80 kg or less in ST38, it is determined whether the subject is male or female (ST139), and the exercise intensity is set to 20 [w] for men and 15 [w] for women. ].

 The result display of ST 9 in the flow chart of FIG. 28 is performed on the display unit of the display 8 as shown in FIG. This display unit is composed of an LCD, and has a program display mark area 50, a data display area 51, a unit display area 52, and a program display mark area 53 in the upper part, and a graphic display area 54 in the lower part.

Specific display examples of this display unit are as shown in FIGS. 41A and 41B. In FIG. 41A, the pattern a is determined and the exercise intensity (exercise level) force is set to “5”. In FIG. 41B, the pattern b is determined and the exercise intensity is set to “2”. This is the case. In any case, a pattern of exercise intensity and fluctuation power is displayed in the lower graphic display area 54 while being scrolled horizontally leftward. In the above embodiment, the fluctuation pattern of the physiological signal (the physiological signal may be a fluctuation value of the power of the heart rate fluctuation vector in addition to the electrocardiogram signal or the pulsation signal) during the exercise load is determined. The present invention relates to a method of determining an appropriate exercise intensity according to the determined variation pattern. Similarly, by determining the variation pattern of the heartbeat fluctuation, it is possible to determine the health status of the exerciser. That is, by determining which of the patterns a to d the fluctuation pattern of the fluctuation power corresponds to, for example, it can be determined whether the exerciser is a healthy person or has a tendency to diabetes or hypertension. Obedience Therefore, if the fluctuation pattern is output and displayed as shown in FIGS. 42A and 42B, it is possible to know one's own health condition. In addition, instead of the power of the fluctuation of the heartbeat interval, the heartbeat of the fluctuation of the heartbeat interval may be used.

 An example of the processing for determining the health condition is shown in the flowcharts of FIGS. 43 and 44. This flowchart shows the method of determining the convergence point used to determine the motion level of the standard pattern. The processing from ST 51 to ST 55 is the same as ST 1 to ST 5 in FIG. That is, when the measurement start key of the key input device 7 in FIG. 1 is pressed, the measurement is started. First, an electrocardiographic signal is detected by the electrocardiographic sensor 1 (ST155), and a calibration operation is performed so that the signal from the electrocardiographic sensor 1 becomes a certain constant level (ST155). This calibration operation is performed by adjusting the gain in the amplifier 4 based on a signal from the CPU 6. After the calibration is completed, "Start measurement" is displayed on the display 8 (ST153), and the exercise load control of the load device 9 is started (ST154). As this exercise load control, for example, after performing a warm-up for 2 minutes with an initial exercise load of 20 [w], a ramp load of 15 [w] per minute is applied.

Next, the peak value of the electrocardiographic signal is detected, and the fluctuation fluctuation is calculated from the calculation formula (1) (ST155). After the calculation, it is determined whether or not two minutes have elapsed during warm-up (ST156), and if not, the process returns to ST155. Two minutes after the end of the warm-up, ST 157 becomes Yes, the power base value is 25 Cms ² ], and the slope is 6 [ms ² ] (ST60, 61).

Subsequently, convergence determination (ST 158) is performed. This is because in the fluctuation characteristics of the fluctuation power shown in Fig. 45 (changes with the time between the fluctuation power and the exercise load), the fluctuation power decreases and converges as the exercise load increases. The AT point is the convergence point of the fluctuation curve, which is the largest fluctuation curve. Here, as the convergence judgment corresponding to this AT point, the fluctuation power falls below a predetermined reference value and the difference from the previous power [Power {T (n-1)} -Power {T (η)} : The slope of the fluctuation curve of the power of fluctuations] reaches a predetermined reference value (power reference value) or less. That is, if it cannot be determined that the convergence point has been reached, the exercise load is gradually increased with a determination of NO (ST159), and the processing of ST155-ST158 is repeated. If you can determine the convergence point, The exercise intensity corresponding to the calculated load value is displayed on the display 8 as a result (ST162). After displaying the results, reduce the exercise load and give the exerciser a fixed time (for example, 1 minute).

—Let the machine down (ST 163). After that, the exercise load control ends (ST164).

Next, an example of the fluctuation power discrimination pattern discrimination processing is shown in detail in FIGS. Here, the pattern is determined using, for example, the average value of the fluctuation power at the time of warming-up, and the patterns at ₂ , ₃ , ₄ , and ₅ minutes after the end of the warming-up. In these flowcharts, the values of the fluctuation power used in the pattern classification are as shown in FIG. 46, and the classifications a to e correspond to the positions shown in FIG. 47 in the fluctuation power. Symbols related to patterns in the flow chart are shown in FIG. 26, and patterns a to j are the patterns shown in FIGS. 6OA to 63, respectively.

Claims

The scope of the claims 1. Exercise load means (9) with variable exercise load, physiological signal measuring means for non-invasively measuring physiological signals during exercise by the exercise load means (9), and physiological signal obtained during exercise. Load determining means (6) for determining the load fluctuation rate of the gradually increasing load or the gradually decreasing load, wherein the exercise load means (6) is a load of the gradually increasing load or the gradually decreasing load determined by the load determining means (6). Exercise equipment that changes exercise load based on the rate of change.  2. The exercise device according to claim 1, wherein the physiological signal is an electrocardiographic signal or a beat signal. 3. The exercise machine according to claim 1, wherein the physiological signal is a fluctuation of a heartbeat interval obtained by an electrocardiographic signal.  4. The exercise apparatus according to claim 3, wherein the fluctuation of the heartbeat interval is the power of the fluctuation of the heartbeat interval.  5. The exercise apparatus according to claim 3, wherein the fluctuation of the heartbeat interval is entropy of the fluctuation of the heartbeat interval. 6. The exercise device according to claim 1, wherein the physiological signal is power of a heart rate fluctuation spectrum.  7. The physiological signal is at least both a heart rate obtained by an electrocardiographic signal or a pulse rate obtained by a pulsatile signal and a fluctuation of a heartbeat interval obtained by an electrocardiographic signal. Exercise equipment as described. 8. The exercise apparatus according to claim 7, wherein the fluctuation of the heartbeat interval is the power of the fluctuation of the heartbeat interval.  9. The exercise apparatus according to claim 7, wherein the fluctuation of the heartbeat interval is entropy of the fluctuation of the heartbeat interval.  10. The physiological signal is at least both a heart rate obtained by an electrocardiographic signal or a pulse rate obtained by a pulsation signal, and a power of a heart rate fluctuation spectrum. Exercise equipment according to 1. 1 1. Physiological signal measuring means (1) for non-invasively measuring physiological signals during exercise, and load determining means for determining the load fluctuation rate of gradually increasing or decreasing load based on physiological signals obtained during exercise (6) and the gradually increasing load or gradually decreasing load determined by this load determining means (6) A body level evaluation device comprising: a physical strength level evaluation means (6) for evaluating a physical strength level from a relationship between an exercise load and a heart rate during a gradual increase or decrease load exercise according to a load variation rate of a load.  1 2. Physiological signal measuring means (1) for non-invasively measuring physiological signals during exercise, and load determining means for determining the load fluctuation rate of gradually increasing or decreasing load based on physiological signals obtained during exercise According to (6) and the load fluctuation rate of the gradually increasing or decreasing load determined by the load determining means (6), the optimal exercise intensity is obtained from the relationship between the exercise load and the fluctuation of the heartbeat interval during the gradually increasing or decreasing load exercise. An exercise intensity determining means (6) for determining the exercise intensity.  13. The exercise intensity determination device according to claim 12, wherein the fluctuation of the heartbeat interval is power of fluctuation of the heartbeat interval.  14. The exercise intensity determination device according to claim 12, wherein the fluctuation of the heartbeat interval is entropy of the fluctuation of the heartbeat interval.  1 5. Physiological signal measuring means (1) for non-invasively measuring physiological signals during exercise, and load determining means for determining the load fluctuation rate of gradually increasing or decreasing load based on physiological signals obtained during exercise (6) and the relationship between the exercise load and the power of the heart rate variability spectrum during the gradual or gradual load exercise according to the load fluctuation rate of the gradual load or the gradual load determined by the load determination means (6). Exercise intensity determining means (6) for determining the optimal exercise intensity from the exercise intensity.  1 6. An exercise load means (9) with a variable load value, and any one of the devices according to claim 11, 12, or 15, wherein the exercise load means (9) is Exercise equipment that changes the exercise load based on the physical strength level obtained by the bell evaluation device or the exercise intensity obtained by the exercise intensity determination device.  1 7. Non-invasive measurement of physiological signals during exercise load, and based on the obtained physiological signals corresponding to changes in exercise load, determine the fluctuation pattern of the physiological signal during exercise load and take appropriate action according to the determined fluctuation pattern. A method for determining exercise intensity that determines the appropriate exercise intensity. 18. The discrimination of the fluctuation pattern of the physiological signal at the time of the exercise load is performed at the time of warming-up and at a predetermined time interval associated with the increase of the exercise load, or based on the rate of change of the physiological signal at each exercise load value interval. The exercise intensity according to claim 1, wherein the pattern is determined. How to determine the degree.  19. The exercise intensity determination method according to claim 1, wherein the physiological signal is an electrocardiographic signal or a beat signal.  20. The exercise intensity determination method according to claim 17, wherein the physiological signal is a fluctuation of a heartbeat interval obtained from an electrocardiographic signal.  21. The method according to claim 4, wherein the fluctuation of the heartbeat interval is power of fluctuation of the heartbeat interval.  22. The exercise intensity determination method according to claim 17, claim 18, claim 19, or claim 20, wherein the determination of the appropriate exercise intensity according to the variation pattern uses a calculation method according to the variation pattern. .  23. Variable load device (9), non-invasive physiological signal measuring means (1) for measuring physiological signals over time, and physiological response to exercise load changes obtained by the physiological signal measuring means (1). An exercise intensity determining means (6) for determining a variation pattern of a physiological signal at the time of exercise load based on the signal, and determining an appropriate exercise intensity according to the determined variation pattern; The load is an exercise device set to a load corresponding to the exercise intensity determined by the exercise intensity determination means (6).  24. Variable load device (9), non-invasive physiological signal measuring means (1) for non-invasively measuring physiological signals, and physiological signal for exercise load change obtained by the physiological signal measuring means. An exercise machine comprising: health condition discriminating means (6) for discriminating a fluctuation pattern of a physiological signal during an exercise load and discriminating a health condition according to the discriminated fluctuation pattern.  25. The exercise apparatus according to claim 23 or claim 24, wherein the physiological signal is a fluctuation of a heartbeat interval obtained by an electrocardiographic signal.  26. Physiological signal measuring means (1) for measuring physiological signals over time in a non-invasive manner, and exercise based on physiological signals for exercise load changes obtained by the physiological signal measuring means (1) during exercise load. A health condition discriminator comprising a fluctuation pattern discriminating means (6) for discriminating a fluctuation pattern of a physiological signal under load, and an output means (6) for outputting the fluctuation pattern discriminated by the fluctuation pattern discriminating means (6). Support equipment. 27. The physiological signal is a fluctuation of a heartbeat interval obtained by an electrocardiographic signal. 26. The health condition determination support device described in 6.  2 8. Physiological signal measuring means (1) for measuring physiological signals over time in a non-invasive manner, and at the time of exercise load, based on the physiological signals for exercise load changes obtained by the physiological signal measuring means (1). Health condition discriminating means (6) for discriminating a fluctuation pattern of a physiological signal in the above, and discriminating a health condition according to the discriminated fluctuation pattern; and output means for outputting a health condition determined by the health condition discriminating device. A measuring device comprising:  29. The measuring apparatus according to claim 28, wherein the physiological signal is a fluctuation of a heartbeat interval obtained by an electrocardiographic signal.