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ADAPTIVE EXERCISE MACHINE
BACKGROUND OF THE INVENTION
The present invention relates generally to the use of strength identification in the design and implementation of control systems for exercise equipment, and more particularly to a method and apparatus for strength identification and control of an exercise machine based upon the velocity dependence of the strength of the user.
Improvements or atrophy in muscular capacity are related to the type of activities performed. For example, an astronaut living in a gravity free environment requires very little strength to perform his daily tasks; thus over time, the astronauts' body will lose its muscular definition. On the other hand, a bodybuilder will increase his strength over time due to the repetitive loading of the various muscle groups. This idea has been formalized with the so called "Principle of Specificity." This principle states that an important factor in establishing a training routine is to try to develop exercises that will train the body in a highly specific 2Q manner, thereby improving its response to the precise demands that will be placed upon it in competition or in everyday life. By training the body in a highly specific manner it is thought the proper neural and physiological adaptations take place in the body. For example, endurance 2J training increases the number of mitochondria in the muscle cell making it more able to metabolize fats.
Typically exercise machines are configured to take advantage of the Principle of Specificity in two ways. First, by isolating a particular muscle, or group of muscles, and then 30 by providing the particular resistance desired by the user. Usually, the resistance provided by the exercise machine can be categorized into one of three groups: "isotonic" (constant torque), "isokinetic" (constant velocity), or "isometric" (constant position). Furthermore, in some of the state-of- 35 the-art exercise machines, a variable radius cam is used in conjunction with a weight stack so that a configuration dependent resistance is achieved. This position dependent resistance is important because of the varying geometry of the musculoskeletal leverage system. 40
Prior art exercise machines may incorporate some type of program that controls torque or velocity which may be selected by the user. It is believed, however, that no prior art machine identifies the strength characteristic of the user and adapts the exercise program to the user based on the strength 45 data gathered. For example, U.S. Pat. No. 2,777,439 discloses a method of providing a resistance which varies with position. U.S. Pat. No. 2,921,791 discloses a device which provides a constant resistive torque. U.S. Pat. Nos. 3,212, 776; 3,465,592 and 3,784,194 disclose machines which 50 automatically adjust resistance to maintain a constant speed of exercise motion. Likewise, U.S. Pat. Nos. 4,184,678; 3,848,467; 3,869,121; 4,082,267 and 4,261,562 disclose various methods of producing and electrically controlling a resistive torque to produce a preselected program of a 55 plurality of constant velocity motions. U.S. Pat. No. 3,589, 193 discloses several methods of providing predetermined types of resistance torques in an ergometer.
None of the prior art exercise devices discussed above provide a method of testing user strength and adapting the 60 resistance accordingly to provide a specified type of workout. An object of the present invention is to provide a method of testing the strength characteristic of a user of an exercise machine and controlling the resistance of the machine based on the strength data gathered. 65
Another object of the present invention is to provide a method of optimizing the power output of the user.
Another object of the present invention is to provide a controller which has the desirable property of being passive (i.e. safe to operate) and can regulate the velocity of the workout to conform to a desired position dependent function which may be specified by a performance index.
Additional objects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objects and advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the claims.
SUMMARY OF INVENTION
The current invention is an apparatus and method for controlling the torque of an exercise machine acting on a user. The invention determines the torque which the user is able to exert at different positions and velocities, and develops a strength model of the user. Based on the strength model of the user, the invention determines a desired velocity profile for the user's exercise. The velocity profile may be chosen to maximize the amount of power output by the user or to provide whatever other type of exercise is desired. The invention then controls the torque acting on the user so that the exercise is accomplished according to the desired velocity profile.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the invention and, together with the following detailed description, serve to explain the principles of the invention:
FIG. 1 is a schematic block diagram of the preferred embodiment of the present invention.
FIG. 2 is a schematic block diagram of the muscle characteristic identifier of the present invention.
FIG. 3 is a schematic block diagram of the adaptation mechanism of the present invention.
FIG. 4 is a three dimensional graphical representation of a Hill Surface.
FIG. 5 is a graphical representation of the Hill relation at a constant position.
FIG. 6 is a schematic block diagram of the torque observer and the identifier algorithm of the present invention.
FIG. 7 is a schematic diagram of a system in accordance with the present invention.
FIGS. 8A and 8B are schematic representations of a semi-active actuator in accordance with the present invention.
DETAILED DESCRIPTION OF THE
The present invention will be described in terms of the preferred embodiment. The preferred embodiment is an apparatus and method for testing a user's strength characteristics and adapting the resistive torque of an exercise machine according to those characteristics. Such a system is shown in FIG. 1.
As is shown in FIG. 1, and as discussed in more detail below, a user 10 exercises on an exercise machine 100 such as an exercise bike, stair climber, or rowing machine by exerting a torque T. The signal T can be either a force or torque depending upon the configuration of the exercise
machine 100. Position data x is transferred from exercise machine 100 to a muscle characteristic identifier 105. Similarly to T, that x can represent either linear position or angular position depending on the configuration of the exercise machine 100. Henceforth, x and T will be consid- 5 ered as angular position and torque, respectively. The resistance actuator 110 is a device which causes the torque x that the exercise machine 100 exerts on the user 10 to increase or decrease according to the commands of the resistance controller 120. The muscle characteristic identifier 105 10 characterizes the strength of the user 10. The muscle characteristic identifier 105 estimates strength parameters a(x) and b(X) and sends them, along with the position data x, and the velocity data x to the resistance controller 120. The muscle characteristic identifier 105 also sends the strength 15 parameters, a(x) and b(x) to a velocity profile specifier 140. The velocity profile specifier 140 determines a velocity profile Vd and sends Vd to the resistance controller 120. The resistance controller 120 outputs the new desired torque, xd to the resistance actuator 110. The resistance actuator 110 is 20 controlled by the resistance controller 120 to produce a torque x on the exercise machine 100 that minimizes the tracking error from the desired velocity profile. This results in user 10 executing the exercise motion at position x and velocity x. The torque command to the resistance actuator 25 110 is calculated by resistance controller 120 based upon the velocity profile Vd and the current strength information of the user 10.
FIG. 2 shows an input/output diagram of the muscle characteristic identifier 105. The muscle characteristic iden- 30 tifier 105 estimates the strength of the user 10 using an adaptation mechanism 131. A position encoder/decoder 132 monitors the position of the exercise motion using an optical sensor or other method commonly known in the art and sends a position signal x to a finite differencing module 133 35 within the muscle characteristic identifier 105. The finite differencing module 133 calculates the velocity of the exercise motion x using a backward differencing method. This method simply divides the difference between the current position measurement and the previous position measurement by the sampling time of the control software. The x data is sent to the adaptation mechanism, along with the position data x. The adaptation mechanism 131 also receives as input xd which comes from resistance controller 120. The ^ adaptation mechanism 131 outputs the current estimates of the strength parameters a(x) and b(x).
FIG. 3 shows a schematic block diagram of the adaptation mechanism 131. The adaptation mechanism 131 calculates the strength parameters a(x) and b(x) based upon the error 50 signal, Q, between the filtered torque, Q, which results from torque output, T, of the user 10 and the current estimate of the filtered torque, Q. The torque T that the user 10 is applying to the exercise machine 100 is not directly measured. A filtered version of T, Q, is obtained using a torque 55 observer 134. The torque observer 134 requires as inputs the position x and velocity x of the exercise motion, and the desired resistance torque xd from the resistance controller 120. The position of the exercise motion is obtained from the position encoder/decoder 132. The torque observer calcu- 60 lates a filtered torque Q and sends the Q data to a comparator 135 which calculates the error in Q based on the current estimate of Q, Q obtained from the identifier algorithm 136. The identifier algorithm 136 uses the Hill relation described below to estimate Q according to strength parameters esti- 65 mates a(x) and b(x), from the position data x and velocity data x. The detailed operation of the torque observer 134 and
the identifier algorithm 136 is shown schematically in FIG. 6, described below.
The torque observer 134 calculates the filtered torque Q. Since the torque command sent to the resistance actuator by the resistance controller 120 is known, the torque applied by the user 10 can be calculated based upon the dynamics of the exercise motion. For this to be accomplished, the inertia of the exercise motion must be known. For example, if the exercise motion is a pedaling exercise the inertia of the thighs, lower leg, and crankshaft must be known. This can be done by using charts of human inertia values for various segments of the human body, or alternatively a series of experiments can be performed to determine the inertia of the limbs. In either case, once the inertia of the motion is known the dynamic equation governing the motion can be calculated. This equation is in the following form:
M(x) is the inertia of the motion, C(x,x)x are the coriolis and centripetal forces, and G(x) is the torque due to gravity, x is the resistance torque, provided by resistance actuator 110, and T is the torque applied by the user 10. Note that, in general, the inertia term is a function of the exercise position, x. The coriolis and centripetal terms can be obtained from the spatial derivative of the inertia term, M(x). The only remaining unknown in this equation is the acceleration, x. Unfortunately, the acceleration is not measurable due to noise within the system. To circumvent this problem, the above equation can be filtered. This method eliminates the need for acceleration measurement but gives a filtered T value, Q, and not the true T. The only difference between the filtered T, Q, and the true T is some phase lag and amplitude modulation. It is this Q signal that is used by the adaptation mechanism. This signal is subtracted from the current estimate of the torque to obtain the error signal, Q, used in the identifier algorithm 136. The identifier algorithm 136 then updates the strength parameters a(x) and b(x) used to model muscle strength using the Hill relation.
The Hill relation describes the force-velocity properties of human skeletal muscle. The force producing capability of muscle declines with increasing muscle shortening velocity. This velocity dependence is not unlike that observed in a D.C. motor, except that the form of the velocity dependence is different. For muscles, the rate of decline with velocity is in the form of a smooth hyperbolic relation. The Hill relation can be applied to muscle groups actuating a motion, as well as to individual muscles. The Hill relation applies only to shortening or concentric muscle contractions. Eccentric contractions are not modeled by the Hill relation.
Muscle force is dependent on position as well as velocity. The position dependence is due to a combination of two factors. The first factor is the length dependence within the contractile machinery of the muscle fibers. The second factor affecting the position dependent strength of an individual is the kinematics of the particular motion. The kinematics determine the leverage that a particular muscle has on the bone that it is connected to and on the axis of the exercise motion. For example, during the performance of a biceps curl the maximum torque is produced when the elbow is at 90 degrees to the upper arm. When the forearm is extended or flexed to its maximum angle the amount of torque that can be applied to the forearm is reduced. These two regions are sometimes referred to as the weak points in the motion.
FIG. 4 shows a plot of torque as a two dimensional function of position and velocity. Note that the velocity
dependence of the surface is assumed to be linear instead of the hyperbolic velocity dependence mentioned above. The x-axis is velocity. The y-axis is position and the z-axis is Torque. This plot is called a Hill surface, A. Force may be plotted instead of torque, depending on the configuration of 5 the exercise machine 100. Thus, given a position and velocity, the Hill surface shows the corresponding force or torque produced by the muscles involved in the motion.
The Hill surface may also change as a function of time. The time dependence can be due to either fatigue or a change 10 in effort level. As muscles fatigue, the height of the Hill surface decreases. An increase in effort would raise the Hill surface and a decrease in effort would lower it.
As long as the exercise motion is concentric, (i.e. the muscle motion is such that the muscles shorten instead of 15 lengthen) the Hill relation can be used to effectively model the user's strength. Bi-directional motion may be concentric since the limb flexor may be shortening during one direction of the motion and the limb extensor may be shortening during the opposite direction. 20
FIG. 5 shows the parameters a(x) and b(x) used to fit the Hill relation as a linear relationship, shown as Hill Curve A, between torque and velocity. The x-axis is velocity and the y-axis is torque and power. A more complicated hyperbolic relation could be used, but experiment has shown that a 25 simple linear relation is sufficient. The torque-velocity data that has been experimentally obtained fits a linear dependence. The linear relationship in FIG. 5 is described by two parameters. The first parameter, a(x), represents the isometric strength of the user 10. The second parameter, b(x), is the 30 slope of the Hill relation. Together, these parameters are referred to as the Hill parameters. These parameters change with the position, x, of the workout.
To identify the Hill parameters for an individual user 10, the resistance controller 120 must periodically provide an 35 excitation phase that changes the resistive torque of exercise machine 100 between a high and low level. This causes the exercise motion to slow down and speed up alternately. High and low velocity data points are thus obtained which show the velocity dependent strength of the user 10. This process 40 is called the "learning phase" because the muscle characteristic identifier 105 learns the value of a(x) and b(x) during this phase.
An advantage of the use of this adaptive scheme is that the resistance controller 120 can track a fatigue episode. During 45 a fatigue episode the torque-velocity data slowly migrates to lower torque levels. If a Hill relation is continuously fit to this new data a current estimate of the fatigued strength capacity of the user 10 can be adaptively maintained. By virtue of this fatigue tracking ability the exercise machine 50 100 can reduce the resistance to draw more effort from the less fatiguable muscle fibers. Another advantage of the adaptive ability of the exercise machine 100 unrelated to the fatigue tracking is that it has the ability to "custom tailor" the resistance to the particular needs of the user 10, and to 55 account for any strength gains due to training.
The power equation, P=Txx, and the fact that muscular strength declines monotonically with velocity according to the Hill relation implies that there is a velocity that will maximize the mechanical power produced by a user 10 with 60 concentric motion. This velocity can be called the optimum shortening velocity. Depending on the curvature, or rate of decline, of the Hill relation the optimum velocity is located at about Vi of the maximum shortening velocity of the muscle. 65
It can thus be inferred from the Hill relation that there is an optimal velocity that maximizes the power of concentric
exercise motion, and that this velocity is a function of the exercise position, as well as time. The preferred embodiment of this invention controls the velocity of the exercise motion so that it approaches this power maximizing velocity. This characteristic of the preferred embodiment is described as "optipoteric" from the roots opti, as in optimum, and poter, which is late Latin for power. The advantage of the invention is that once the optipoteric velocity is identified using the data gathered regarding the user's strength, the proper resistance can be calculated to cause the user to execute the exercise motion at the optipoteric velocity. Conceivably this type of workout would burn the most calories in a given amount of time.
Utilizing the strength information derived by the invention is not limited to implementing the optipoteric exercise. In other embodiments, any desired velocity profile can be implemented. This may include isokinetic or isotonic exercise. The invention may operate at any velocity profile specified. The workout can be biased toward slow velocities, or towards higher velocities. In this manner the user 10 can take advantage of the so called "Principle of Specificity", which states that an important factor in establishing a training routine is the development of exercises which will train the body in a highly specific manner, thereby improving its response to the precise demands placed upon it in competition or in everyday life.
FIG. 6 is a detailed schematic of the adaptation mechanism 131. Blocks 160-166 comprise the force observer and blocks 170-176 comprise the identifier algorithm. The inputs to the torque observer are the velocity of the exercise machine, x, which is the output of finite differencer 133, and xd the desired torque from resistance controller 120. The input x to the torque observer 134 is implied by the arguments to M(x) and C(x).
Multiplier 160 multiplies x by the inertia of the exercise motion, M(x). Multiplier 161 multiplies the signal p by a gain a resulting in signal si. Multiplier 164 multiplies the input x by itself resulting in the signal x2. Multiplier 163 multiplies the signal x2 by the coriolis function of the exercise machine resulting in signal s2. Summation block 165 adds input signal xd with signals si and s2 resulting in signal s3. Filter 162 takes signal s3 and outputs signal s4 which is modified in that it has amplitude modulation and phase lag relative signal s3. Summation block 166 takes the difference of signals s4 and si and gives signal Q. Q represents the filtered torque output of the user 10. Summation block 170 takes the difference of Q and the current estimate of the filtered torque Q to output error signal Q to the identifier algorithm. Multiplier 171 multiplies the vector signal p by the signal Q to give the vector signal s5. Multiplier 172 represents a matrix multiplication of the vector signal s5 by the matrix P(t). Note that the matrix in block 172 it time varying. The time dependence may be specified by an appropriate update rule which will be obvious to those skilled in the art. Integrator 173 takes the vector signal c and gives the vector signal c which contains the raw information necessary to update the muscle parameters. Splitter 174 splits the vector signal c and splits it into two smaller vector signals ta and cb. The signal ta is the information used to update the a(x) Hill parameter and the signal cb is the information used to update the b(x) Hill parameter. Multiplier 175 takes the vector signal ta and the first part of the unfiltered regressor <|) from block 180, and performs the inner product operation. This operation just multiplies the two vector signals element by element and then takes the sum which will be a scalar signal. This scalar signal is the current estimate of the Hill parameter a(x).