Patent Publication Number: US-8523741-B2

Title: Method and system for monitoring sport related fitness by estimating muscle power and joint force of limbs

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This non-provisional application is a divisional application of U.S. patent application Ser. No. 12/696,396, filed Jan. 29, 2010, now U.S. Pat. No. 8,246,555 which itself claims priority under 35 U.S.C. §119(a) on Patent Application No. 098133931 filed in Taiwan, R.O.C. on Oct. 7, 2009, the entire contents of which are hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to a method and system for monitoring sport related fitness, and more particularly, to a fitness monitoring method and system capable of performing a fitness evaluation at any time and place since it is not only configured with a monitor platform adapted to be arranged in any common household living space, but also with a portable fitness detection module. 
     TECHNICAL BACKGROUND 
     Research has shown that a large number of the health problems in society are either caused in whole or in part by an unhealthy lifestyle that often result in poor eating habits, high stress levels, lack of exercise, poor sleep habits, and so on. Recognizing this fact, the field of physical fitness assessment and testing has seen an increasing demand with rising public interest in physical fitness and the relevance of performance to soldiers, firefighters, athletes, and the like. Moreover, it is used in the field of patient rehabilitation. These days, physical fitness is considered a measure of the body&#39;s ability to function efficiently and effectively in work and leisure activities, to be healthy, to resist hypokinetic diseases, and to meet emergency situations. Accordingly, a general-purpose physical fitness program must address the following essentials: cardio-respiratory endurance, muscular strength and muscular endurance, joint flexibility, and body composition, which are health related issues, However, the sport-related issues are also included, which are muscular power, agility, speed and coordination, etc. Scoring high on those physical fitness assessments usually indicates better heath and better exercise performance. However, good physical fitness is not easy to obtain and certainly can not be achieved overnight. It requires a person to maintain a healthy lifestyle while exercise in a regular basis. Nevertheless, for motivating a person to live a healthier life and exercise regularly, it would be a great help if data of detailed physical fitness can be provided to that person in a daily basis to be used as a guide for achieving a healthier lifestyle, for monitoring progress, and for brainstorming solutions when problems arise. 
     Conventionally, a fitness assessment is a series of measurements that help determine physical fitness. The basic formula of any conventional fitness assessment is to evaluate body mass index (BMI), resting heart rate and blood pressure, and aerobic fitness before, during or after a moderate workout that may last a specific period of time. There are many fitness assessment products available on the market, including weight scales with BMI monitoring ability and tread mills with heart rate/respiratory monitoring ability, and so on. However, all those fitness assessment products have the following shortcomings: 
     (1) The fitness assessment can only be conducted on the specific exercise platform; 
     (2) The result of the fitness assessment can be very subjective since it is provided from assessors sometimes only basing on the readings from those fitness assessment products while those assessors might not be particularly well trained; 
     (3) Those products with balance evaluation system are imported that are bulky and expensive; 
     (4) It is required for the user to be applied by at least a set of electrodes for enabling a wireless or wired EMG test to be perform; In those conventional fitness assessment products, it is common to use a game console, such as Wii fit, for motivating its users to perform the designated exercises. However, such game console neither is not appropriate for elders, nor is not therapeutic effective in clinical practice; 
     (5) There is no dynamic balance monitoring for the control of the nervous, muscular and skeletal systems relating to the motions of upper and lower limbs that is available in conventional fitness assessment products with game console; and 
     (6) The conventional fitness assessment products with game console are not designed with the ability relating to the recognizing of motion accuracy and coordination. 
     TECHNICAL SUMMARY 
     The present disclosure relates to a fitness monitoring method and system capable of performing a fitness evaluation at any time and place since it is not only configured with a monitor platform adapted to be arranged in any common household living space, but also with a portable fitness detection module. 
     In an exemplary embodiment, the present disclosure provides a method and system for monitoring sport related fitness, in which the system comprises: a sensing module and a force/track detection module, wherein sensor values from the sensing module are fed to the force/track detection module to be used as base for estimating feature parameters and classifying a motion series relating to muscle power and joint force of limbs so as to obtain skill-related fitness parameters corresponding to the sensing of the sensor module. 
     Further scope of applicability of the present application will become more apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the disclosure, are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure will become more fully understood from the detailed description given herein below and the accompanying drawings which are given by way of illustration only, and thus are not limitative of the present disclosure and wherein: 
         FIG. 1  is a block diagram of a system for monitoring sport related fitness according to an exemplary embodiment of the present disclosure. 
         FIG. 2  is a schematic diagram showing the coordination of shanks and thighs of lower limbs in an ergonomic model used in the present disclosure. 
         FIG. 3  is a schematic diagram showing the relationship between sport-related fitness and a stiffness elliptic inclusion according to the present disclosure. 
         FIG. 4  is a schematic diagram showing an identification projection of the present disclosure. 
         FIG. 5  shows a motion of a lower limb with respect to the variation of force direction during the motion according to the present disclosure. 
         FIG. 6  is a schematic diagram showing the detection of a lower limb motion according to an embodiment of the present disclosure. 
         FIG. 7  is a schematic diagram showing how the joint angles in lower limb are related to each other. 
         FIG. 8  is a schematic diagram showing the detection of an upper limb motion according to an embodiment of the present disclosure. 
         FIG. 9  is a schematic diagram showing how the joint angles in upper limb are related to each other. 
     
    
    
     DESCRIPTION OF THE EXEMPLARY EMBODIMENTS 
     For your esteemed members of reviewing committee to further understand and recognize the fulfilled functions and structural characteristics of the disclosure, several exemplary embodiments cooperating with detailed description are presented as the follows. 
     Please refer to  FIG. 1 , which is a block diagram of a system for monitoring sport related fitness according to an exemplary embodiment of the present disclosure. IN  FIG. 1 , the system for monitoring sport related fitness is primarily composed of: a sensing module  10  and force/track detection module  20 . 
     The sensing module  10  is configured with a pressure detecting unit  11 , a position detecting unit  12  and a processing unit  13 , in which the pressure detecting unit  11 , being used for sensing a value relating to pressure, can be a device selected from the group consisting of: a pressure mat, a foot pad, and a force plate, that is provided for a user to step thereon so as to generate a pressure value accordingly; the position detecting unit  12 , being used for sensing a value relating to motion track, can be a device including at least one device selected from the group consisting of: accelerometers and gyroscopes that can be adapted to be arranged at an arm, an elbow, waist, or a knee of a user; and the processing unit  13  is used for fetching and synchronizing the sensor values, such as the aforesaid pressure values from the pressure detecting unit  11  and the motion track values from the position detecting unit  12 . 
     It is noted that by arranging the pressure detecting unit  11  and the position detecting unit  12  at different positions of a user, the limb motions of the user capable of being detected thereby can be different. For any human body, the force/track detection module  10  is enabled to tracking an exercise performed by the limbs of the user, which includes upper limbs such as arms and lower limbs such as legs, while the detected track is resulting from at least one exercise selecting from the group consisting of: the stretching exercise of the upper limbs and the stretching exercise of the lower limbs. Moreover, the stretching exercise of the upper limbs includes at least one movement selected from the group consisting of: lifting a heavy object, pushing/pulling, punching, racket swinging, and pitching; and the stretching exercise of the lower limbs includes at least one movement selected from the group consisting of: one foot lifting, one leg squatting, one leg standing, two legs standing. By attaching the position detecting unit  12  to one upper limb or lower limb of the user, the motion track of such upper limb or lower limb can be sensed thereby. In the present disclosure, the user is allowed to have one of his/her upper limb, one of his/her lower limb, both of his/her upper limb, both of his/her lower limb, or even one selected upper limb and one selected lower limb, to be mounted by the position detecting unit  12  at will. In addition, when the user, having the position detecting unit  12  attached therein, is stepped on the pressure detecting unit  11  while performing an exercise, the force variation relating to the exercise of the user can be detected thereby as simultaneously the motion track relating to the exercise is detected by the position detecting unit  12 . 
     The force/track detection module  20  is provided for receive the sensor values from the sensing module  10  to be used as base for estimating feature parameters and classifying a motion series relating to muscle power and joint force of the limbs so as to obtain fitness parameters corresponding to the sensing of the sensor module. Moreover, the force/track detection module  20  includes a parameter calculation module  21  and a motion identification module  22 . 
     The parameter calculation module  21  is primarily composed of a feature capturing unit  211  and an ergonomic model database  212 . The feature capturing unit  211  is used for fetching the sensor values from the sensing module to be used in an estimation for obtaining the feature parameters relating to muscle power and joint force of the limbs, in which each feature parameter is related to at least one value selected from the group consisting of: gravity center of human body, biped center of mass, displacement of the center of gravity, direction, speed, distance traveled, relative position, coefficient of rigidity, joint angle, variation of joint angle, muscle power. The ergonomic model database  212  is used for storing data relating to ergonomic models to be used as basis for estimating the feature parameters. 
     The motion identification module  22  is composed of a motion model database  221 , a match unit  222 , a fitness parameter database  223 , a comparison unit  224  and a notification unit  225 . The motion model database  221  has the at least one motion track model stored therein, whereas each motion track model is at least one model selected from the group consisting of: a motion track template, a motion track statistic model, a motion track probabilistic model. The match unit  222  is used for performing the matching calculation, such as a distance calculation or a similarity calculation, upon the feature parameter resulting from the estimation of the parameter calculation module  21  and the at least one motion track models form the motion model database  221  so as to obtain an optimal motion track corresponding to the feature parameters to be classified into the motion series that is used for estimating the fitness parameters. Moreover, the fitness parameter is related to at least one value selected from the group consisting of: muscular strength, muscular endurance, agility, flexibility, muscle power, speed, and balance. 
     The fitness parameter database  223  is provided for storing skill-related fitness parameters while providing the stored skill-related fitness parameters to be compared with the fitness parameters obtained from the estimation of the motion identification module  22 . The comparison unit  224  is used for comparing the fitness parameters obtained from match unit  222  with the skill-related fitness parameters stored in the fitness parameter database  223  so as to output a comparison result accordingly. Moreover, the notification unit  225  is used for displaying the comparison result as it is electrically connected to the comparison unit  224 , and thus the notification unit  225  can be a device selected from the group consisting of: cellular phones, personal digital assistants (PDAs), computers, speakers, alarms, indication lamps, and other audio/video devices, that is capable of connecting to the comparison unit  224  in a wired or wireless manner, i.e. by cable or through a network system for instance. 
     Please refer to  FIG. 2 , which is a schematic diagram showing the coordination of shanks and thighs of lower limbs in an ergonomic model used in the present disclosure. In  FIG. 2 , each thigh  31  is referred as a first limb L 1  while the corresponding shank  32  along with the foot  33  are referred as a second limb L 2 , in which the upper end of the first limb L 1  is connected to a widesence stationary end  34  whereas the widesence stationary end  34  includes the hip joint, the pelvic cavity and the lumbar vertebra; and the lower end of the first limb L 1  is connected to the knee joint  35 . Moreover, the upper end of the second limb L 2  is connected to the knee joint  35  while the lower end thereof is connected to the foot  33  as the foot  33  is stepping on a planar surface  36 . When the foot  35  is subjected to a force of any direction, there will be a reacting force acting on the knee joint  35  according to the Newton&#39;s third laws of motion, and thus, in order to keep balance, it s required for the knee joint  35  to generate a force for counterbalancing the reacting force. At this moment, since the station end  34  is in the status of widesence stationary, only the knee joint  35  will be caused to rotate by the torque resulting from the force of counterbalancing that the addition of the displacement vectors relating to the knee joint  35  and the foot  33  will construct a nonlinear relationship. Therefore, with respect to the rigidity of the foot  33 , the relationship between the displacements and forces working on the foot  33  and the knee joint  35  can be defined and described by a matrix. 
     As shown in  FIG. 1  and  FIG. 2 , assuming the foot  33  on the planar surface  36  is subjected to a force representing as [F X , F Y ], such force [F X , F Y ] is detected by the sensing module  10  so as to be fed into the parameter calculation module  21  of the force/track detection module  20 . Moreover, the stiffness of the foot  33  is an 2×2 endpoint stiffness matrix obtained by performing a partial differential calculation upon the known restoring forces and displacements in the x-axis and y-axis directions. It is noted that the aforesaid stiffness matrix can not be a diagonal matrix or a determinate matrix that is a zero matrix. For keeping the entire lower limbs shown in  FIG. 2  to move without tipping off and the whole chain of motions in the lower limbs in a stable balance state, the aforesaid stiffness matrix must be a symmetric matrix, i.e. S xy =S yx , that is defined in the following equations: 
     
       
         
           
             
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     where,
         dF X  is the differentiation of F X  with respect to displacement;   dF Y  is the differentiation of F Y  with respect to displacement;   S XY ∂F x /∂Y, is the partial differentiation of F X  with respect to displacement in Y-axis direction;   S YX ∂F Y /∂X, is the partial differentiation of F Y  with respect to displacement in X-axis direction;   S XX =∂F X /∂X, is the partial differentiation of F X  with respect to displacement in X-axis direction;   S YY =∂F Y /∂Y F Y ,       

     is the partial differentiation of with respect to displacement in Y-axis direction; and
         [S] is a stiffness matrix representing the stiffness of the foot  33 .       

     Assuming the biped system shown in  FIG. 2  is under a certain balance control (0&lt;φ&lt;2π) whereas the foot on the planar surface  36  is affected to moved in multiple directions with a minimum displacement, the displacement of the foot  33  can be represented as [dX,dY]=[cos φ, sin φ], and the restoring force in x-axis direction and y-axis direction can be defined as following:
 
 F   X =−( S   XX  cos φ+ S   XY  sin φ)
 
 F   Y =−( S   YX  cos φ+ S   YY  sin φ)
 
     When the direction of the displacement is varied from 0 degree to 360 degrees, a stiffness ellipse can be obtained, as the one shown in  FIG. 3 . In  FIG. 3 , the line C 11  connecting a random point on the stiffness ellipse C 1  and the center thereof indicates the magnitude of the restoring force required when the selected random point is shifted from its current stable balance state by a unit displacement C 21 . The stiffness ellipse C 1  is defined by the following equation: 
     
       
         
           
             
               
                 
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     In the stiffness ellipse C 1 , the long axis indicates the direction of maximum restoring force, so that it is referred as the maximal stiffness axis; and on the other hand, the short axis thereof is referred as the minimal stiffness axis. Let the length of the long axis C 12  to be 2a and that of the short axis C 13  to be 2b, the following equation can be obtained:
 
2 a= 2√{square root over (2)}( S   XX  cos φ+ S   XY  sin φ)
 
2 b= 2√{square root over (2)}( S   YX  cos φ+ S   YY  sin φ)
 
     Accordingly, under balance control, the length of the long axis C 12  should equal to that of the short axis C 13 , so that the stiffness ellipse C 1  is substantially conformed to the circle C 2 . 
     The operation principle of motion track identification performed by the motion identification module  22  is described in the following with reference to  FIG. 1 . In a motion identification operation, when an stretching exercise is performed and detected by the sensing module  10 , the sensor values corresponding to the stretching exercise will be issued by the sensing module  10  that are received by the parameter calculation module  21  to be used in a calculation for obtaining feature parameters relating to muscle power and joint force of the limbs. Then, the obtained feature parameters are fed to the motion identification module  22 , in which the match unit  22  is enabled to perform a matching calculation upon the feature parameters and the motion track models so as to obtain an optimal motion track corresponding to the feature parameters according to the similarity between the vector of the feature parameters and the compared motion track model, which can be represented by a probability P(X|C k ) in the present embodiment, and simultaneously, a GMM (Gaussian Mixture Model) is used as the motion track model, which are defined by the following equation: 
     
       
         
           
             
               
                 
                   
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     wherein,
         X={x 1 , x 2 , . . . , x t , . . . , x T } represents the vector of feature parameters obtained from the calculation of the parameter calculation module that is fed into the motion identification module, and x t εR d ;   C k  represents the k th  type movement in a motion track;   Λ k  represents the model parameters of the k th  type of movement;   P(x t |Λ k  is the probability model for each type of movement, being a GMM
 
(Gausian Mixture model) that it is composed of: weights w k,m  for each mixture, expected value vectors μ k,m  of each Gaussian distribution, and covariance matrixes Σ k,m , whereas those parameters and weights are obtained using an Expectation-Maximaization (EM) algorithm.
       

     For further modeling a motion for identification, a Latent Semantic Analysis (LSA) is introduced for converting those feature parameters to an identification space where they can be trained to be used in a later classification process, as that shown in  FIG. 4 , by that the projection direction D 1  of maximal identification effectiveness is found. 
     In the present embodiment, a method of singular value decomposition (SVD) is used for locating the optimal discriminative matrix T T  to be used for converting those feature parameters to the identification space, and then training the model parameters Λ k  of each C k  for fitting the result with a Gaussian Mixture Model so as to achieve the aforesaid modeling. 
     The aforesaid algorithm is defined by the following equation: 
                 P   ⁡     (     X   |     C   k       )       ≈     P   ⁡     (         T   T     ⁢   X     |     C   k       )         =         ∏     t   =   1     T     ⁢     P   ⁡     (         x   ^     t     |       Λ   ^     k       )         =         ∑     t   =   1     T     ⁢     log   ⁢           ⁢     P   ⁡     (         x   ^     t     |       Λ   ^     k       )           =         ∑     t   =   1     T     ⁢     log   (       ∑     m   =   1       M   k       ⁢         w   ^       k   ,   m       ⁢     N   ⁡     (           x   ^     t     ;       μ   ^       k   ,   m         ,       ∑   ^       k   ,   m         )           )       =       ∑     t   =   1     T     ⁢     log   ⁢           (       ∑     m   =   1       M   k       ⁢         w   ^       k   ,   m       ⁢                ∑   ^       k   ,   m              1   /   2           (     2   ⁢   π     )     d       ⁢     exp   (       -     1   2       ⁢       (         x   ^     t     -       μ   ^       k   ,   m         )     T     ⁢         ∑   ^       k   ,   m       -   1       ⁢     (         x   ^     t     -       μ   ^       k   ,   m         )         )         )                       
wherein {circumflex over (x)} t  and {circumflex over (Λ)} k ={ŵN k,m , {circumflex over (μ)} k,m , {circumflex over (Σ)} k,m ; 1≦m≦M k } represent the feature parameters and the model parameters in the identification space, by that, during the classification, the model similarity calculation is performed using the probability model P(X|C k )≈P(T T X|C k ) that is projected in the identification space.
 
     Please refer to  FIG. 5  to  FIG. 7 , which show a motion of a lower limb with respect to the variation of force direction during the motion according to the present disclosure. According to the monitoring system shown in  FIG. 1  when it is used for detecting the motion of lower limbs, it can use a pressure mat as its pressure detecting unit  11  while attaching an accelerometer or gyroscope to be used as the position detecting unit  12 . 
     In  FIG. 5 , the body mass center, i.e. the waist, is defined to be the end point of an lower-limb model, in which: 
     F X : basing on F=ma, it is the product of body mass with the second order time differentiation of body gravity center movement; 
     F Y : basing on the Newton&#39;s third laws of motion, it is the product of the sensing area with the total instant pressure detected by the pressure mat. 
     Please refer to  FIG. 6 , which is a schematic diagram showing the detection of a lower limb motion according to an embodiment of the present disclosure. In  FIG. 6 , P 1  is the mass center of the body; D is the total displacement length of the body mass center P 1  during the standing-up movement in centimeter (cm); D x  is the length of displacement in X-axis direction in centimeter (cm); D y  is the length of displacement in Y-axis direction in centimeter (cm); and θ d  is the included angle between D and D x . 
     Please refer to  FIG. 7 , which is a schematic diagram showing how the joint angles in lower limb are related to each other. In  FIG. 7 , L f  is the length of the thigh; L t  is the length of the shank; θ H  is the stretching angle of the hip joint; θ K  is the stretching angle of the knee joint; T Hip  is the torque exerting on the hip joint; and T knee  is the torque exerting on the knee joint. Thereby, the relationship of angle variation with respect to the forces and torques exerting on the joints is defined as following: 
     
       
         
           
             
               [ 
               
                 
                   
                     
                       T 
                       Hip 
                     
                   
                 
                 
                   
                     
                       T 
                       Knee 
                     
                   
                 
               
               ] 
             
             = 
             
               
                 
                   [ 
                   
                     
                       
                         
                           F 
                           X 
                         
                       
                     
                     
                       
                         
                           F 
                           Y 
                         
                       
                     
                   
                   ] 
                 
                 · 
                 
                   J 
                   T 
                 
               
               = 
               
                 
                   [ 
                   S 
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                 ⁡ 
                 
                   [ 
                   
                     
                       
                         
                           ⅆ 
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                           ⅆ 
                           Y 
                         
                       
                     
                   
                   ] 
                 
               
             
           
         
       
       
         
           
             J 
             = 
             
               [ 
               
                 
                   
                     
                       
                         
                           - 
                           
                             L 
                             f 
                           
                         
                         ⁢ 
                         
                           sin 
                           ⁡ 
                           
                             ( 
                             
                               θ 
                               H 
                             
                             ) 
                           
                         
                       
                       - 
                       
                         
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                         ⁢ 
                         
                           sin 
                           ⁡ 
                           
                             ( 
                             
                               
                                 θ 
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                         - 
                         
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                           t 
                         
                       
                       ⁢ 
                       
                         sin 
                         ⁡ 
                         
                           ( 
                           
                             
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                             + 
                             
                               θ 
                               K 
                             
                           
                           ) 
                         
                       
                     
                   
                 
                 
                   
                     
                       
                         
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                           f 
                         
                         ⁢ 
                         
                           cos 
                           ⁡ 
                           
                             ( 
                             
                               θ 
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                             ) 
                           
                         
                       
                       + 
                       
                         
                           L 
                           t 
                         
                         ⁢ 
                         
                           cos 
                           ⁡ 
                           
                             ( 
                             
                               
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                         t 
                       
                       ⁢ 
                       
                         cos 
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                           ( 
                           
                             
                               θ 
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                             + 
                             
                               θ 
                               K 
                             
                           
                           ) 
                         
                       
                     
                   
                 
               
               ] 
             
           
         
       
     
     Please refer to  FIG. 8  and  FIG. 9 , which show a motion of an upper limb with respect to the variation of force direction during the motion according to the present disclosure. In  FIG. 8 , the motion performed by the upper limb is a racket swinging movement. According to the monitoring system shown in  FIG. 1  when it is used for detecting the motion of upper limb, it can attach accelerometers at the user&#39;s shoulder joint, elbow joint, and wrist joint to be used as the position detecting units  12 , that are able to detect the smoothness of the racket swinging movement in a continuous manner. 
     In  FIG. 8 , D is the average wrist displacement length during the racket swinging movement in centimeter (cm); D x  is the length of displacement in X-axis direction in centimeter (cm); D y  is the length of displacement in Y-axis direction in centimeter (cm); and θ d  is the included angle between D and D x . 
     In  FIG. 9 , Ls is the length of the upper arm; Lt is the length of the lower arm; θs is the included angle between the shoulder joint and the elbow joint; θ E  is the included angle between the upper arm and the lower arm; T shoulder  is the torque exerting on the shoulder joint; and T elbow  is the torque exerting on the elbow joint. Thereby, the relationship of angle variation with respect to the forces and torques exerting on the joints is defined as following: 
     
       
         
           
             
               [ 
               
                 
                   
                     
                       T 
                       shoulder 
                     
                   
                 
                 
                   
                     
                       T 
                       elbow 
                     
                   
                 
               
               ] 
             
             = 
             
               
                 
                   [ 
                   
                     
                       
                         
                           F 
                           X 
                         
                       
                     
                     
                       
                         
                           F 
                           Y 
                         
                       
                     
                   
                   ] 
                 
                 · 
                 
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                   T 
                 
               
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             = 
             
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                             ( 
                             
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                       - 
                       
                         
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                         ⁢ 
                         
                           sin 
                           ⁡ 
                           
                             ( 
                             
                               
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                         sin 
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                           ( 
                           
                             
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                         ⁢ 
                         
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                             ( 
                             
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                       + 
                       
                         
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                         ⁢ 
                         
                           cos 
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                           ( 
                           
                             
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               ] 
             
           
         
       
     
     The system and method disclosed in the present disclosure not only can be applied in the field of exercise, but also can be applied in medical applications, such as the risk quantification and comparison for the elders or the mobility disabled. 
     By using the monitoring system shown in  FIG. 1  in a manner that: there are pressure mats being used as the pressure detecting unit  11  and disposed at locations of highest risk of falling down, such as areas near the gateway of a stair, bed sides, areas in front of a stool, and there are accelerometers or gyroscopes being used as the position detecting units and attached to the belt of the user for detecting information relating to the user&#39;s movement. Thereby, sport-related fitness parameters can be estimated and calculated by the parameter calculation module  21  and the motion identification module  22  to be used in a risk assessment for quantifying the risk of falling down in a stepwise manner, i.e. high risk, middle risk and low risk, so that as soon as an assessment indicating high risk, the system will be enabled to issue a warning signal in a wired or wireless manner. 
     Regarding to the risk quantification and comparison and assuming there are q risk assessment indexes of falling representing as y=└y 1 , . . . , y q ┘, and there are p parameters in the sport-related fitness parameters representing as x=└x 1 , . . . , x p ┘, the method for risk quantification and comparison is designed to find the parameters in the p sport-related fitness parameters that are most correlated with the risk assessment indexes. According to the monitoring system shown in  FIG. 1 , after receiving the fitness parameters estimated and obtained from the match unit  222 , the comparison unit  224  is enabled to compare the estimated fitness parameters with the skill-related fitness parameters stored in the fitness parameter database  223  so as to output a comparison result accordingly; and if the comparison result is in the neighborhood of a high risk value of falling down, the notification unit  225  is enabled to issue a warning signal. 
     The canonical correlation analysis is used for finding the fitness parameters that are most correlated with the risk assessment indexes. That is, in statistics, canonical correlation analysis is a way of making sense of cross-covariance matrices. If we have two sets of variables, {tilde over (x)}=a 1 ′x, a 1 =└a 1,1 , . . . , a 1,p ┘, {tilde over (y)}=b 1 ′y, b 1 =└b 1,1 , . . . , b 1,q ┘ and there are correlations among the variables, then canonical correlation analysis will enable us to find linear combinations of the {tilde over (x)} and the {tilde over (y)} which have maximum correlation with each other. That is, one seeks vectors maximizing the equation of a 1 ′Σ xy b 1  subject to the constraint that:
 
 a   1 ′Σ xx   a   1 =1  b   1 ′Σ yy   b   1 =1
 
     and. Mathematically, the feature vector that is corresponded to the largest eigenvalue λ 1  of Σ xx   −1 Σ xy Σ yy   −1 Σ yx  is a 1  whereas the eigenvalue λ 1  is able the largest eigenvalue Σ yy   −1 Σ yx Σ xx   −1 Σ xy  of whose corresponding feature vector will be 1. 
     To sum up, the system and method for monitoring sport related fitness by estimating muscle power and joint force of limbs has the following advantages: 
     (1) novelty: 
     A. it can use an information of two-dimensional (2D) pressuring sensing and displacement detection to estimate the movement of limbs and the changing of body gravity center in three-dimensional space. 
     B. It can quantify the fitness parameter relating to muscle power and joint force of limbs. 
     (2) Inventiveness: 
     A. It is an inventive software technique with fewer sensors required to be worn on a user. 
     B. By the cooperation of the ergonomic model and the fitness parameter database in the monitoring system of the present disclosure, the muscle power and joint force of limbs can be accurately estimated. 
     (3) Usability: 
     A. The monitoring system not only can be applied in the exercise equipment industry for improving fitness or rehabilitation, but also can be applied in medical applications, such as elder care for lower the risk of falling down. 
     With respect to the above description then, it is to be realized that the optimum dimensional relationships for the parts of the disclosure, to include variations in size, materials, shape, form, function and manner of operation, assembly and use, are deemed readily apparent and obvious to one skilled in the art, and all equivalent relationships to those illustrated in the drawings and described in the specification are intended to be encompassed by the present disclosure.