APPARATUS FOR THE CONTROL OF A TRAINING DEVICE

Abstract

An apparatus for controlling of a training device including a training device configured to absorb a mechanical power applied by a person undertaking physical training, an assistance unit configured to assist the training and/or to make the training more difficult, and an exertion measuring apparatus configured to measure mechanical exertion data of an effort applied by the person during the training, a body sensor configured to measure physiological data of the body of the person, a computing unit configured, with an optimization algorithm, to adjust coefficients, a summand, and delays to prepare a prediction of the physiological data based on a model, and a control unit configured to take a predetermined reference variable for the physiological data, to take the prediction as a control variable, and to control an assistance of the assistance unit as a manipulated variable.

Claims

1. An apparatus for controlling a training device, the apparatus comprising: the training device configured to absorb a mechanical power applied by a person undertaking physical training, wherein the training device comprises an assistance unit configured to assist the training and/or to make the training more difficult, wherein the training device comprises an exertion measuring apparatus configured to measure mechanical exertion data BD(t) of an effort applied by the person during the training, wherein t is the time, a body sensor configured to measure physiological data PD(t) of the body of the person, a computing unit in which a mathematical model in the form mPD(t+T) is stored, wherein the computing unit (3) is configured, with an optimization algorithm, to adjust mPD(t+T) and the delay T individually for each person in such a way that mPD(t+T) approaches the measured physiological data PD(t+T), and to prepare a prediction mPD(t+T) of the physiological data PD(t+T) on the basis of the model, and a control unit (4) that is configured to provide a predetermined reference variable for the physiological data PD(t), to take the prediction mPD(t+T) as a control variable, and to control an assistance u(t) of the assistance unit (6) as a manipulated variable.

2. The apparatus according to claim 1, wherein $m\mathrm{PD}\left(t+T\right)=a_{10}+\underset{x}{.Math.}{B}_x\left(t\right)\mathrm{and}{B}_1\left(t\right)=\underset{i=1}{\overset{j}{.Math.}}{a}_{1i}*\left(\underset{d=0}{\overset{D_i}{.Math.}}\mathrm{BD}\left(t-{\tau }_{1i}-d*K_i\right)/\left(D_i+1\right)\right)\mathrm{and}{B}_2\left(t\right)=\underset{i=1}{\overset{k}{.Math.}}{a}_{2i}*\mathrm{PD}\left(t-{\tau }_{2i}\right)$ apply, wherein the computing unit is configured, with the optimization algorithm, to adjust the coefficients a.sub.xi, the summand a.sub.10 and the delays τ.sub.xi at least partially for each person individually in such a way that mPD(t+T) approaches the measured physiological data PD(t+T).

3. The apparatus according to claim 2, wherein the training device comprises an altimeter configured to measure the altitude h(t) of the training device, and $B_3\left(t\right)=\underset{i=1}{\overset{l}{.Math.}}{a}_{3i}*h\left(t-{\tau }_{3i}\right)$ in the model.

4. The apparatus according to claim 2, wherein the training device comprises a temperature sensor configured to measure the temperature Temp(t) in the surroundings of the training device (2), and $B_4\left(t\right)=\underset{i=1}{\overset{m}{.Math.}}{a}_{4i}*\mathrm{Temp}\left(t-{\tau }_{4i}\right)$ in the model.

5. The apparatus according to claim 2, wherein the training device comprises an inclinometer configured to measure an incline N(t) of the training device, and $B_5\left(t\right)=\underset{i=1}{\overset{n}{.Math.}}{a}_{5i}*N\left(t-{\tau }_{5i}\right)$ in the model.

6. The apparatus according to claim 1, wherein the computing unit is configured to prepare the prediction mPD(t+T) for the time T that lies at least T=5 s in the future.

7. The apparatus according to claim 2, wherein the computing unit is configured to adjust, based on the optimization algorithm, the coefficients a.sub.xi, the summand a.sub.10, the delays τ.sub.xi and the delay T after the training session, making use of the exertion data BD(t) ascertained in a plurality of training sessions and the physiological data PD(t) ascertained in the plurality of training sessions, as well as, optionally, of the altitude h(t) ascertained in the plurality of training sessions, the temperature Temp(t) ascertained in the plurality of training sessions and/or the incline N(t) ascertained in the plurality of training sessions, in order to take an underlying fitness of the person (8) into consideration.

8. The apparatus according to claim 7, wherein the computing unit is configured to adjust the coefficients a.sub.xi, the summand a.sub.10, the delays τ.sub.xi and the delay T after the training session with the optimization algorithm which comprises the steps of: a) specifying in each case a plurality of discrete values for each of the coefficients a.sub.xi, for the summand a.sub.10, for each of the delays τ.sub.xi, and for the delay T; b) setting a.sub.xi, a.sub.10, τ.sub.xi and T to one of the values; c) calculating mPD(t+T) based on the model; d) calculating a modelling error between the measured physiological data PD(t+T) and mPD(t+T) for a plurality of t; e) repeating steps b) to d) for all combinations of the values; and f) choosing those values for a.sub.xi, a.sub.10, τ.sub.xi and T, that result in the lowest modelling error.

9. The apparatus according to claim 8, wherein underestimation errors are weighted more strongly than overestimation errors in step d).

10. The apparatus according to claim 2, wherein the computing unit is configured to adjust, with an algorithm for adjusting a current fitness, the coefficients a.sub.xi and the summand a.sub.10 during a training session, making use of the exertion data BD(t) ascertained in the training session and the physiological data PD(t) ascertained in the training session, as well as, optionally, of the altitude h(t) ascertained in the training session, the temperature Temp(t) ascertained in the training session and/or the incline N(t) ascertained in the training session, in order to take the current fitness of the person into consideration.

11. The apparatus according to claim 10, wherein the computing unit is configured to determine, with the algorithm for adjusting the current fitness, a difference Diff(t)=mPD(t)−PD(t) between the prediction of the physiological data mPD(t) and the measured physiological data PD(t), and if the difference Diff(t) exceeds a threshold value Threshold1>0, to correct the coefficients a.sub.xi by adding a respective constant const1.sub.xi, as well as to correct the summand a.sub.10 by adding a constant const.sub.10 and, if the difference Diff(t) falls below a threshold value of ThresholdM<0 to correct the coefficients a.sub.xi by adding a respective constant constM.sub.xi, as well as to correct the summand a.sub.10 by adding a constant const.sub.M0.

12. The apparatus according to claim 1, wherein the control unit is a PID controller.

13. The apparatus according to claim 12, wherein the PID controller is configured to determine the assistance u(t) according to $u\left(t\right)=K_P*f_1\left(e\left(t\right)\right)+K_I*\underset{\tau =0}{\overset{\tau =t}{\int }}{f}_2\left(e\left(\tau \right)\right)d\tau +K_D*\frac{d{f}_3\left(e\left(t\right)\right)}{\mathrm{dt}}$ wherein KP, KI, and KD are control parameters, wherein e(t) is the control deviation at time t, wherein the functions f1(e), f2(e) and f3(e) are selected such that underestimation errors are weighted more strongly than overestimation errors.

14. The apparatus according to claim 13, wherein the computing unit is configured to carry out a calibration method in which a step response of the physiological data PD(t) or of the exertion data BD(T) is generated by an abrupt change in the manipulated variable, and wherein the computing unit is configured to determine the control parameters KP, KI, and KD from the step response.

15. The apparatus according to claim 13, wherein the computing unit is configured to identify at least one abrupt change in the manipulated variable, and the resulting step response of the physiological data PD(t) or of the exertion data BD(T) after a training session, and wherein the computing unit is configured to determine the control parameters KP, KI, and KD from the at least one step response.

16. The apparatus according to claim 1, wherein the exertion data BD(t) is a power, in particular a pedalling power in the case of a bicycle, in particular of an electric bicycle, or, in the case of a bicycle ergometer, a running power, a rowing power, a speed, a torque, a rotation speed, an angular speed and/or a knee abduction torque.

17. The apparatus according to claim 1, wherein the assistance unit comprises an electric motor, a gearbox, and/or a brake.

18. The apparatus according to claim 1, wherein the physiological data PD(t) comprise a heart rate, a heart rate variability, an electrocardiogram, an oxygen saturation of the blood, a blood pressure, a neurological activity, in particular an electroencephalography, an adduction, in particular a knee adduction, and/or a knee bend.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The disclosure will now be described with reference to the drawings wherein:

[0032] FIG. 1 shows an overview of an apparatus according to an exemplary embodiment of the disclosure.

[0033] FIG. 2 shows a detail of the overview according to an exemplary embodiment of the disclosure.

[0034] FIG. 3 shows a plot of f.sub.1(e) and f.sub.2(e).

[0035] FIG. 4 shows a plot of f.sub.3(e).

[0036] FIG. 5 shows a plot of a step response of the physiological data PD(t) that is generated by an abrupt change in the manipulated variable.

[0037] FIG. 6 shows a plot of various measured variables recorded during a training session.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0038] FIGS. 1 and 2 show that the apparatus 1 for controlling a training device 2 includes: [0039] the training device 2 that is configured to absorb a mechanical power 9 applied by a person 8 undertaking physical training, wherein the training device 2 includes an assistance unit 6 that is configured to assist the training and/or to make the training more difficult, wherein the training device 2 includes an exertion measuring apparatus 5 that is configured to measure mechanical exertion data BD(t) of an effort applied by the person during the training, wherein t is the time, [0040] a body sensor 7 that is configured to measure physiological data PD(t) of the body of the person 8, [0041] a computing unit 3 in which a mathematical model of the form

[00006]$m\mathrm{PD}\left(t+T\right)=a_{10}+\underset{x}{.Math.}{B}_x\left(t\right)$

is stored, in which

[00007]$B_1\left(t\right)=\underset{i=1}{\overset{j}{.Math.}}{a}_{1i}*\left(\underset{d=0}{\overset{D_i}{.Math.}}\mathrm{BD}\left(t-{\tau }_{1i}-d*K_i\right)/\left(D_i+1\right)\right)\mathrm{and}{B}_2\left(t\right)=\underset{i=1}{\overset{k}{.Math.}}{a}_{2i}*\mathrm{PD}\left(t-{\tau }_{2i}\right)$

wherein the computing unit 3 is configured, with an optimization algorithm 11, to adjust the coefficients a.sub.xi, the summand a.sub.10, the delays τ.sub.xi at least partially, and the delay T individually for each person in such a way that mPD(t+T) approaches the measured physiological data PD(t+T), and to prepare a prediction mPD(t+T) of the physiological data PD(t+T) on the basis of the model, and [0042] a control unit 4 that is configured to take a predetermined reference variable for the physiological data PD(t), to take the prediction mPD(t+T) as a control variable, and to control an assistance u(t) of the assistance unit 6 as a manipulated variable. An averaging of D.sub.i+1 measuring points, which have a time separation K.sub.i, is performed in the term B.sub.1(t).

[0043] The training device 2 can include an altimeter that is configured to measure the altitude h(t) of the training device 2, and can be

[00008]$B_3\left(t\right)=\underset{i=1}{\overset{l}{.Math.}}{a}_{3i}*h\left(t-{\tau }_{3i}\right)$

in the model. Additionally, the training device 2 can include a temperature sensor that is configured to measure the temperature Temp(t) in the surroundings of the training device 2, and can be

[00009]$B_4\left(t\right)=\underset{i=1}{\overset{m}{.Math.}}{a}_{4i}*\mathrm{Temp}\left(t-{\tau }_{4i}\right)$

in the model. The training device 2 can include an inclinometer that is configured to measure an incline N(t) of the training device 2, and can be

[00010]$B_5\left(t\right)=\underset{i=1}{\overset{n}{.Math.}}{a}_{5i}*N\left(t-{\tau }_{5i}\right)$

in the model.

[0044] The control unit can, for example, be a PID controller. The PID controller can, for example, be configured to determine the assistance u(t) in accordance with

[00011]$u\left(t\right)=K_P*f_1\left(e\left(t\right)\right)+K_I*\underset{\tau =0}{\overset{\tau =t}{\int }}{f}_2\left(e\left(\tau \right)\right)d\tau +K_D*\frac{d{f}_3\left(e\left(t\right)\right)}{\mathrm{dt}}$

wherein K.sub.P, K.sub.I and K.sub.D are control parameters, wherein e(t) is the control deviation at time t, wherein the functions f.sub.1(e), f.sub.2(e) and f.sub.3(e) are selected such that underestimation errors are weighted more strongly than overestimation errors. Here it is possible that

[00012]$f_1\left(e\right)=\underset{i=0}{\overset{p}{.Math.}}{c}_{i1}*e^i\mathrm{and}{f}_2\left(e\right)=\underset{i=0}{\overset{q}{.Math.}}{c}_{i2}*e^i$

and f.sub.3(e)=0 for e<0 and f.sub.3(e)=e for e≥0, while in f.sub.1(e) and f.sub.2(e) the polynomial can be different in different ranges of e. FIG. 3 shows an exemplary plot of f.sub.1(e)=f.sub.2(e), and FIG. 4 shows an exemplary plot of f.sub.3(e). As can be seen from FIG. 3, the functions f.sub.1(e) and f.sub.2(e) can have a bisector and only lie above the bisector in a range 0<e<E.sub.1 or 0<e<E.sub.2 respectively. Particularly when the physiological data are a heart rate, the following can, for example, apply: f.sub.1(e)=f.sub.2(e)=e for e>12 or e<0 and f.sub.1(e)=f.sub.2(e)=2*e−0.082*e.sup.2 for 0≤e≤12. As can be seen from FIG. 4, it is, for example, possible for f.sub.3(e) to be governed by f.sub.3(e)=e for e>0 and f.sub.3(e)=0 for e≤0.

[0045] It is conceivable that the computing unit 3 is configured to adjust the control parameters K.sub.P, K.sub.I and K.sub.D individually for each person 8. For this purpose, the computing unit 3 can be configured to carry out a calibration method in which a step response of the physiological data PD(t) is generated by an abrupt change in the manipulated variable at a time T.sub.0, wherein the computing unit 3 is configured to determine the control parameters K.sub.P, K.sub.I and K.sub.D from the step response. An exemplary step response is illustrated in FIG. 5. The computing unit 3 can be configured to record the physiological data PD(t) continuously to generate the step response. The computing unit 3 can be configured to switch the assistance unit 6 from a constant first assistance u.sub.1 to a constant second assistance u.sub.2, in order thereby to bring about the abrupt change in the manipulated variable. For example, u.sub.1 can be from 80% to 100% and u.sub.2 can be from 0% to 20%. The person can be shown information here indicating that they should train as far as possible at a constant frequency, for example a pedalling frequency. The computing unit 3 can be configured to wait long enough, both during the first assistance u.sub.1 and during the second assistance u.sub.2, for the physiological data PD(t) to have stabilized around a value of PD.sub.1 before the changeover and around a value of PD.sub.2 after the changeover. The computing unit 3 can be configured to wait at least 2 minutes both before and after the changeover. To determine the control parameters from the step response, the computing unit 3 can be configured to apply an inflection tangent 13 to the step response. Before applying the inflection tangent 13, PD(t) can be adjusted by a function, for example a polynomial, and the inflection tangent 13 can be applied to the adjusted function. A method of least square errors can be employed to adjust the function. The point of intersection of the inflection tangent 13 with PD(t)=PD.sub.1 determines a delay duration T.sub.U that starts at T.sub.0, and the point of intersection of the inflection tangent 13 with PD(t)=PD.sub.2 determines a settling duration T.sub.G that starts at the end of T.sub.U. The control parameters can now be determined, for example according to K.sub.P=1.2*T.sub.G/(K.sub.S*T.sub.U), K.sub.1=0.6*T.sub.G(K.sub.S*(T.sub.U).sup.2) and K.sub.D=0.6*T.sub.G/K.sub.S, wherein K.sub.S is the amplification factor and can be calculated as the ratio of the control parameter change to the assistance change.

[0046] It is conceivable that the computing unit is configured to identify at least one abrupt change in the manipulated variable and the resulting step response of the physiological data PD(t) or of the exertion data BD(t) after a training session, wherein the computing unit is configured to determine the control parameters K.sub.P, K.sub.I and K.sub.D from the at least one step response. It is also conceivable that the computing unit is configured to use the calibration method for a coarse adjustment of the control parameters K.sub.P, K.sub.I and K.sub.D and to use the at least one step response identified outside the calibration method following the training session in order to perform a fine adjustment of the control parameters K.sub.P, K.sub.I and K.sub.D.

[0047] It is moreover conceivable for the computing unit to be configured to generate a second step response. For this purpose, the computing unit can be configured so that after the physiological data PD(t) have stabilized following the abrupt change in the manipulated variable, it switches the assistance over from u.sub.2 to u.sub.1 and waits again until the exertion data BD(t) or the physiological data PD(t) have stabilized. The control parameters K.sub.P, K.sub.I and K.sub.D can differ when the assistance u(t) increases or decreases.

[0048] The computing unit 3 can be configured to adjust, on the basis of the optimization algorithm 11 (see FIG. 2), the coefficients a.sub.xi, the summand a.sub.10, the delays τ.sub.xi and the delay T after the training session, making use of the exertion data BD(t) ascertained in a plurality of training sessions and the physiological data PD(t) ascertained in the plurality of training sessions as well, optionally, as the altitude h(t) ascertained in the plurality of training sessions, the temperature Temp(t) ascertained in the plurality of training sessions and/or the incline N(t) ascertained in the plurality of training sessions in order to take an underlying fitness of the person 8 into consideration. For this purpose, the computing unit 3 can be configured to adjust the coefficients a.sub.xi, the summand a.sub.10, the delays τ.sub.xi and the delay T after the training session with the optimization algorithm 11, which has the steps of: a) specifying in each case a plurality of discrete values for each of the coefficients a.sub.xi, for the summand a.sub.10, for each of the delays τ.sub.xi, and for the delay T; b) setting a.sub.xi, a.sub.10, τ.sub.xi and T to one of the values; c) calculating mPD(t+T) on the basis of the model; d) calculating a modelling error between the measured physiological data PD(t+T) and mPD(t+T) for a plurality of t; e) repeating steps b) to d) for all combinations of the values; f) choosing those values for a.sub.xi, a.sub.10, τ.sub.xi and T that result in the lowest modelling error. Underestimation errors can be weighted 11 more strongly than overestimation errors in step d).

[0049] As can be seen from FIG. 2, the computing unit 3 can be configured to adjust, with an algorithm for adjusting a current fitness 12, the coefficients a.sub.xi and the summand a.sub.10 during a training session, making use of the exertion data BD(t) ascertained in the training session and the physiological data PD(t) ascertained in the training session as well, optionally, as the altitude h(t) ascertained in the training session, the temperature Temp(t) ascertained in the training session and/or the incline N(t) ascertained in the training session in order to take the current fitness of the person 8 into consideration. For this purpose the computing unit can, for example, be configured to determine, with the algorithm for adjusting the current fitness 12, a difference Diff(t)=mPD(t)−PD(t) between the prediction of the physiological data mPD(t) and the measured physiological data PD(t), and if the difference Diff(t) exceeds a threshold value Threshold.sub.1>0, to correct the coefficients a.sub.xi by adding a respective constant const.sub.1xi, as well as to correct the summand a.sub.10 by adding a constant const.sub.10 and, if the difference Diff(t) falls below a threshold value Threshold.sub.M<0 to correct the coefficients a.sub.xi by adding a respective constant cons.sub.Mxi as well as to correct the summand a.sub.10 by adding a constant const.sub.M0.

[0050] The coefficients a.sub.xi ascertained in the optimization algorithm 11 as well as delays τ.sub.xi and T and the coefficients a.sub.xi ascertained in the algorithm for adjusting the current form 12 as well as the summand a.sub.10 ascertained in the optimization algorithm 11 and in the algorithm for ascertaining the current fitness are used to prepare the prediction mPD(t+T) in a step 10. The prediction mPD(t+T) is the control variable in the control unit 4 and the manipulated variable is the assistance u(t).

[0051] The exertion data BD(t) can, for example, be a power, in particular a pedalling power in the case of a bicycle, in particular of an electric bicycle, or in the case of a bicycle ergometer, a running power, a rowing power, a speed, a torque, a rotation speed, an angular speed and/or a knee abduction torque. If the training device 2 is the bicycle or the bicycle ergometer, the power 9 that is applied by the person 8 during the training and absorbed by the training device 2 is a pedalling power. The training device 2 can, for example, also be a rowing ergometer or a rowing boat, and the exertion data could be the rowing power. The training device could also be an abductor/adductor machine, and the exertion data could be a knee abduction torque.

[0052] The assistance unit 6 can, for example, include an electric motor, a gearbox and/or a brake. The assistance u(t) applied by the assistance unit 6 can be positive, whereby the training is assisted, and/or negative, whereby the training is made more difficult. The electric motor is an example of the assistance unit 6 that is configured to assist the training. In this case, the assistance u(t) could, for example, be a power applied by the electric motor. It is alternatively conceivable that in the case in which the exertion data BD(t) are the power, the control unit 4 is configured to determine the power P.sub.M of the electric motor in accordance with P.sub.M(t)=u(t)*K*BD(t). The factor K indicates what maximum motor assistance is possible. K can, for example, be from 1 to 5 and in particular is 3. A brake of, for example, a bicycle ergometer is an example of the assistance unit that is configured to make the training more difficult. In this case, the assistance could, for example, be a braking power. An example of an assistance unit that is configured to support the training and to make it more difficult is the electric motor that is configured to perform recovery, i.e. to convert a pedalling power of the person into electrical current. The assistance unit 6 can be configured to control the assistance u(t) in small increments. For example, increments of a maximum of 3%, in particular a maximum of 1.5% or a maximum of 1%, are conceivable. It is the case here that 100% corresponds to a maximum assistance u(t) in the case that the assistance unit is configured to assist the training. In the case where the assistance unit is configured to make the training more difficult, −100% corresponds to a maximum opposition to the training.

[0053] The physiological data PD(t) can include a heart rate, a heart rate variability, an electrocardiogram, an oxygen saturation of the blood, a blood pressure, a neurological activity, in particular an electroencephalography, an adduction, in particular a knee adduction, and/or a knee bend. The adduction and/or the knee bend can, for example, be determined with a plurality of inertial measuring units attached to the person 8, which are configured to determine acceleration values and/or rotation data.

[0054] The physiological data PD(t), the exertion data BD(t) and the assistance u(t) of a training session carried out with an electric bicycle as the training device 2 are plotted in FIG. 6. The physiological data PD(t) are the heart rate in beats per minute (bpm). The heart rate can, for example, be measured with the body sensor 7 that is fitted in a chest strap. The exertion data BD(t) are the pedalling power in watts. The pedalling power can, for example, be determined by measuring the torque and the angular speed. To obtain a particularly high quality of the torque, the torque according to FIG. 6 was measured with a torque sensor supplied by Innotorq, as is, for example, described in WO 2015/028345 A1. The angular speed was measured through measurement of the rotation of a pole ring with a magnetic field sensor. The assistance unit 6 according to FIG. 6 is the electric motor of the electric bicycle, whose assistance is controlled from 0% to 100%. In the case in which the electric motor can perform recovery, the assistance can be controlled from −100% to 100%. The dashed line in the upper plot in FIG. 6 represents the reference variable. It can be seen that the reference variable can change over time. It can also be seen that the measured heart rate is at all times a good approximation of the reference variable.

LIST OF REFERENCE NUMERALS

[0055] 1 Apparatus [0056] 2 Training device [0057] 3 Computing unit [0058] 4 Control unit [0059] 5 Exertion measuring apparatus [0060] 6 Assistance unit [0061] 7 Body sensor [0062] 8 Person [0063] 9 Power [0064] 10 Preparation of the prediction mPD(t+T) [0065] 11 Optimization algorithm [0066] 12 Algorithm for adjusting the current fitness [0067] 13 Tangent at inflection point [0068] BD(t) Exertion data [0069] PD(t) Physiological data [0070] mPD(t+T) Prediction of the physiological data [0071] u Assistance [0072] t Time [0073] T.sub.U Delay duration [0074] T.sub.V Settling duration [0075] T.sub.0 Time of the abrupt change in the assistance