Abstract:
A device for acquiring data and diagnosing a musculoskeletal injury. The device includes a semi-flexible housing, at least one ultrasonic transducer, a positional localizer, and a transmission system. The semi-flexible housing is positioned proximate a portion of the musculoskeletal system of a patient and supports the at least one ultrasonic transducer and the positional localizer. The at least one ultrasonic transducer is configured to acquire an ultrasonic data indicative of a bone surface. The positional localizer is positioned at a select location relative to the at least one ultrasonic transducer and tracks movement of the housing. The transmission system transmits the ultrasonic data of the at least one ultrasonic transducer and the movement data of the positional localizer to a data analyzer for analysis and diagnosis.

Description:
RELATED APPLICATIONS 
       [0001]    The present application claims the filing benefit of co-pending PCT Patent Application No. PCT/US2010/022939, filed on Feb. 2, 2010, and is a Continuation-In-Part of co-pending U.S. patent application Ser. No. 12/364,267, filed on Feb. 2, 2009, the disclosures of both applications are hereby incorporated by reference herein in their entirety. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates to devices and methods for evaluating a physiological condition of a musculoskeletal system and, more particularly, to evaluating the physiological condition of bodily joints. 
       BACKGROUND OF THE INVENTION 
       [0003]    In humans, the knee joint  50 , as shown in  FIGS. 1 ,  2 , and  3 , is functionally controlled by a mechanical system governed by three unique types of forces: (1) active forces resulting from motion, such as those resulting from a muscle flexing or relaxing; (2) constraining forces that constrain motion, such as those resulting from ligaments being in tension; and (3) interaction forces that resist motion, such as those acting upon bones. In addition to these three types of forces, the soft tissue in the knee joint  50  (e.g., cartilage and the meniscus) produce a dampening effect distributing the compressive loads acting on the knee joint  50 . 
         [0004]    Knee joint motions are stabilized primarily by five ligaments which restrict and regulate the relative motion between the femur  52 , the tibia  54 , and the patella  56 . These ligaments are the anterior cruciate ligament (“ACL”)  58 , the posterior cruciate ligament (“PCL”)  60 , the medial collateral ligament (“MCL”)  62 , the lateral collateral ligament (“LCL”)  64 , and the patellar ligament  66 . An injury to any one of these ligaments  58 - 66  or other soft-tissue structures may cause detectable changes in knee kinematics and the creation of detectable vibrations, each of which may be representative of the type of knee joint injury and/or the severity of the injury. These visual (knee kinematics) and auditory (vibrations) changes are produced as the bones  52 ,  54 ,  56  move in a distorted kinematic pattern and differ significantly from the look and sound of a properly balanced knee joint  50  moving through the same range and types of motion. 
         [0005]    Conventionally, knee vibration has been detected using microphones with or without stethoscope equipment and correlated with clinical data regarding various joint problems. However, microphones and stethoscopes cannot reliably detect frequencies, especially those experiencing strong interference from noise. Also the signal clearance can be substantially be influenced by skin friction. It is desirable, therefore, to provide a diagnostic tool that compares patient specific data with kinematic data while providing visual feedback to clinicians. 
       SUMMARY OF THE INVENTION 
       [0006]    While the present invention will be described in connection with certain embodiments, it will be understood that the present invention is not limited to these embodiments. To the contrary, this invention includes all alternatives, modifications, and equivalents as may be included within the spirit and scope of the present invention. 
         [0007]    A device for acquiring data and diagnosing a musculoskeletal injury in accordance with one embodiment of the present invention includes a semi-flexible housing, at least one ultrasonic transducer, a positional localizer, and a transmission system. The semi-flexible housing is positioned proximate a portion of the musculoskeletal system of a patient and supports the at least one ultrasonic transducer and the positional localizer. The at least one ultrasonic transducer is configured to acquire an ultrasonic data indicative of a bone surface. The positional localizer is positioned at a select location relative to the at least one ultrasonic transducer and tracks movement of the housing. The transmission system transmits the ultrasonic data of the at least one ultrasonic transducer and the movement data of the positional localizer to a data analyzer for analysis and diagnosis. 
         [0008]    Another embodiment of the present invention is directed to a method of diagnosing a musculoskeletal injury. The method includes creates a 3D model of a portion of the musculoskeletal system of a patient. A feature data is acquires by a sensor that is positioned proximate the portion of the musculoskeletal injury. The feature data is compared, by a neural network, to a database of feature data. A dataset within the database of feature data is representative of the musculoskeletal injury. Then, based on the comparing, a diagnosis is returned. 
         [0009]    Still another embodiment of the present invention is directed to a diagnostic system for diagnosing a musculoskeletal injury. The system includes a 3D model reconstruction module that acquires a structural data indicative of a bone surface. The bone is within a portion of the musculoskeletal system of a patient. The 3D model reconstruction module constructs a patient-specific model from the structural data. The system further includes a kinematic tracking module that acquires movement data while the portion of the musculoskeletal system is articulated. A vibroarthography model acquires vibration data generated by the articulation. The structural data, the movement data, and the vibration data are received and analyzed by an intelligent diagnosis module in order to determine injury type. 
         [0010]    The above and other objects and advantages of the present invention shall be made apparent from the accompanying drawings and the description thereof. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present invention and, together with a general description of the invention given above, and the detailed description of the embodiments given below, serve to explain the principles of the present invention. 
           [0012]      FIG. 1  is a side elevational view of a posterior portion of a knee joint with a 90° flexion. 
           [0013]      FIG. 2  is a side elevational view of the knee joint of  FIG. 1  but with the knee joint fully extended. 
           [0014]      FIG. 3  is a side elevational view of an anterior portion of the knee joint in  FIG. 1 . 
           [0015]      FIG. 4  is a flow chart illustrating a method of determining a type of knee injury in accordance with one embodiment of the present invention. 
           [0016]      FIG. 5  is a schematic diagram of a diagnostic system in accordance with one embodiment of the present invention. 
           [0017]      FIG. 6  is another schematic diagram of the diagnostic system of  FIG. 5 . 
           [0018]      FIG. 7  is a schematic view of a knee brace in accordance with one embodiment of the present invention. 
           [0019]      FIG. 8  is a side elevational view of a vibration detection module in accordance with one embodiment of the present invention. 
           [0020]      FIG. 9  is a side elevational view of an exemplary shoe having a sensor array, for a shoe module, in accordance with one embodiment of the present invention. 
           [0021]      FIG. 9A  is an exemplary wireless transmitter for use with the shoe module of  FIG. 9 . 
           [0022]      FIG. 9B  is an enlarged view of one exemplary positional sensor of the shoe module of  FIG. 9 . 
           [0023]      FIG. 10  is a schematic view of an ultrasound transducer wand for use with the diagnostic system in accordance with one embodiment of the present invention. 
           [0024]      FIG. 11  is a diagrammatic view of an ultra wide band transmitter in accordance with one embodiment of the present invention. 
           [0025]      FIG. 12  is a diagrammatic view of an ultra wide band receiver in accordance with one embodiment of the present invention. 
           [0026]      FIG. 13  is a Cartesian coordinate system depicting an ultra wide band positioning system in accordance with one embodiment of the present invention. 
           [0027]      FIG. 14  is a diagrammatic view comparing one embodiment of an ultra wide band positioning system to a global positioning system. 
           [0028]      FIG. 15  illustrates the error in detecting a position along each of the x-, y-, and z-axes and with respect to a sequentially acquired series of data points. 
           [0029]      FIG. 16  is an exemplary screen capture of a user interface of the diagnostic system of  FIG. 5 . 
           [0030]      FIG. 17  is a side elevational view of a leg with a knee brace in accordance with another embodiment of the present invention. 
           [0031]      FIG. 18  is a side elevational view of a leg with a knee brace in accordance with another embodiment of the present invention. 
           [0032]      FIG. 19  is an individual transducer tracking sub-brace for use with a knee brace in accordance with one embodiment of the present invention. 
           [0033]      FIG. 20  is an inter-transducers mechanical link sub-brace for use with a knee brace in accordance with one embodiment of the present invention. 
           [0034]      FIG. 21  is a rotating transducer sub-brace for use with a knee brace in accordance with one embodiment of the present invention. 
           [0035]      FIG. 22  is a diagrammatic representation of an inertia based localizer circuit in accordance with one embodiment of the present invention. 
           [0036]      FIG. 23  is a diagrammatic representation of an alternate individual transducer tracking sub-brace circuit architecture in accordance with one embodiment of the present invention. 
           [0037]      FIG. 24  is a diagrammatic representation of a high voltage circuit for use with a knee brace in accordance with one embodiment of the present invention. 
           [0038]      FIG. 25  is a diagrammatic representation of the circuit layout of the high voltage circuit of  FIG. 24 . 
           [0039]      FIG. 26  is a diagrammatic representation of a high voltage multiplexer for use with a sub-brace of a knee brace in accordance with one embodiment of the present invention. 
           [0040]      FIG. 27  a diagrammatic representation of a receiving circuit for use with a sub-brace of a knee brace in accordance with one embodiment of the present invention. 
           [0041]      FIG. 28  is a diagrammatic representation of a diagnostic system in accordance with an embodiment of the present invention. 
           [0042]      FIG. 29  is a flow chart illustrating one method of using the diagnostic system of  FIG. 28 . 
           [0043]      FIGS. 30A-30C  illustrate various kinematic feature vectors acquired from a knee joint moving through a range of motion. 
           [0044]      FIGS. 31A-31C  illustrate feature vectors of a femoral position with respect to the tibia. 
           [0045]      FIG. 32  illustrates average medial and lateral femoral condyle positions during a deep knee bend of a patient having an anterior cruciate ligament deficit. 
           [0046]      FIG. 33  is a diagrammatic representation of a neural network classifier in accordance with one embodiment of the present invention. 
           [0047]      FIG. 34  is a diagrammatic representation of a construction of a neural network. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0048]    The exemplary embodiments of the present invention are illustrated and described below to encompass diagnosis of bodily abnormalities and, more particularly, devices and methods for evaluating the physiological condition of the musculoskeletal system (such as joints) to discern whether abnormalities exist and the extent of any abnormalities. Of course, it will be apparent to those of ordinary skill in the art that the exemplary embodiments discussed below are merely examples and may be reconfigured without departing from the scope and spirit of the present invention. However, for clarity and precision, the exemplary embodiments, as discussed below, may include optional steps, methods, and features that one of ordinary skill should recognize as not being a requisite to fall within the scope of the present invention. By way of example, the exemplary embodiments disclosed herein are described with respect to diagnosing a knee joint injury. Nevertheless, the exemplary embodiments may be utilized to diagnose other injuries of the musculoskeletal system (such as a hip joint injury or a bone fracture), as the knee joint  50  ( FIG. 1 ) is merely exemplary to facilitate an understanding of the embodiments disclosed. 
         [0049]    Turning now to the figures and in particular to  FIG. 4 , with reference also to  FIG. 1 , a low level exemplary process flow for a method  70  of determining a type of knee joint injury in accordance with one embodiment of the present invention is described. Still more particularly, the method  70  includes constructing a 3D model of the knee joint  50  (Block  72 ), which may include the detection of motion sound (Block  74 ) as well as tracking the kinematics (Block  76 ). The detected sound and tracked kinematics are automatically analyzed (Block  78 ) and the knee injury recognized based upon the analysis (Block  80 ). 
         [0050]      FIG. 5  illustrates a first exemplary diagnostic system  82  for implementing the method  70  of  FIG. 3 . The diagnostic system  82  includes four modules: (1) a pulse echo A-mode ultrasound based 3D model reconstruction (“PEAUMR”) module  84  ( FIG. 6 ) for constructing a patient-specific 3D-model of the patient&#39;s knee joint  50  ( FIG. 1 ); (2) a joint kinematics tracking (“JKT”) module  86  for tracking the kinematics of the knee joint  50  ( FIG. 1 ) using the patient-specific 3D model of the knee joint  50  ( FIG. 1 ) from the PEAUMR module  84 ; (3) vibroarthography (“VA”) module  88  for capturing sounds emanating from the knee joint  50  ( FIG. 1 ) while in motion; and (4) an intelligent diagnosis (“ID”) module  90  for identifying a likely diagnosis of the knee joint  50  ( FIG. 1 ) using the kinematic data and the vibration data. Each of these four modules  84 - 90  is described in further detail below. If desired, a foot module  92  ( FIG. 6 ) may be included with the JKT module  86  for providing dynamic force data, also described in detail below. 
         [0051]    It will be understood by those of skill in the art that the diagnosis system  82  is usable with or without the use of the VA module  88 . For example, the present invention may be used to mathematically describe the relative motion of the bones  52 ,  54 ,  56  in the patient&#39;s knee joint  50  as such motion is tracked on a 3D-patient specific bone model. The bone model and motion may be compared with a database of mathematical descriptions of joint motion. The database could contain mathematical descriptions of healthy or clinically undesirable joint motion. 
         [0000]    As will be discussed in more detail hereafter, the interaction between bodily tissue (e.g., bone against cartilage or bone against bone) in a dynamic environment creates certain vibrations that are indicative of the condition or state of health of the joint. Even the healthiest and youngest joints create vibrations. However, joints that exhibit degradation, whether through wear or injury, will exhibit vibrations that are much more pronounced and amplified as compared to those of a healthy joint. The VA module  88  with the diagnostic system  82  utilizes those sounds, such as vibrations, exhibited by the joint during a range of motion to diagnose the condition of the joint without requiring an invasive procedure or subjecting the patient to radiation. 
         [0052]      FIG. 6  provides still further details of the diagnostic system  82 . The modules  84 - 92  may output the acquired data to a computer  96  for data processing by way of, for example, a neural network  98 . The data processing, as will be discussed in more detail below, may provide one or more of a visual output, an audible output, and a diagnosis by way of a visual display  100 . 
         [0053]    Referring still to  FIGS. 4-6 , and now also  FIG. 7 , the VA module  88  is shown and comprises a plurality of accelerometers (three are shown  120   a ,  120   b ,  120   c ) that are utilized to detect sound, specifically, vibrations occurring as a result of motion of the knee joint  50 . In this exemplary VA module  88 , the accelerometers  120   a ,  120   b ,  120   c  are mounted directly to the skin or external tissue surface of the patient, as skin-mounted sensor  119   s , in order to detect sounds from bone and soft tissue interaction. An intervening adhesive may be utilized between the accelerometers  120   a ,  120   b ,  120   c . In the context of the knee joint  50 , the VA module  88  includes one accelerometer  120   a  mounted on the medial side of the knee joint  50 , a second accelerometer  120   b  mounted on the lateral side of the knee joint  50 , and a third accelerometer  120   c  mounted on the front side of the knee joint  50 , proximate the patella  56  ( FIG. 3 ). As illustrated, the accelerometers  120   a ,  120   b ,  120   c  are mounted to the patient so that each lies along a common plane  121 , though this is not required. It should also be understood, however, that any number of accelerometers  120   a ,  120   b ,  120   c  may be utilized to detect sounds generated by the patient&#39;s knee joint  50 . 
         [0054]    Each accelerometer  120   a ,  120   b ,  120   c  is in communication with one or more signal conditioning circuits or electronics  122 . The accelerometers  120   a ,  120   b ,  120   c  are operative to detect sound, specifically vibrations, and output the sound detected in the form of frequency data (measured in Hertz) to the conditioning circuits  122 . This frequency data is processed by the conditioning circuits  122  and communicated to the computer  96  as digital frequency data. While the accelerometers  120   a ,  120   b ,  120   c  are generating frequency data, the conditioning circuits  122  may include a clock  123  to time stamp the frequency data generated. As will be discussed in more detail below, correlating the frequency data with the time stamp provides a constant against which all of the detected data can be compared on a relative scale. 
         [0055]    The first accelerometer  120   a  on the medial side of the knee joint  50  detects vibrations generated primarily by the interactions between the medial condyle  110  ( FIG. 1 ) of the femur  52  against the medial cartilage  112  ( FIG. 1 ) on top of the medial portion of the tibia  54 . Similarly, the second accelerometer  120   b  on the lateral side of the knee joint  50  detects vibrations generated primarily by the interactions between the lateral condyle  114  ( FIG. 1 ) of the femur  52  against the lateral cartilage  116  ( FIG. 1 ) on top of the lateral portion of the tibia  54 . The third accelerometer  120   c  on the front of the knee joint  50 , proximate the patella  56  ( FIG. 3 ), detects vibrations generated primarily by the interactions between the femur  52  against the patella  56  ( FIG. 3 ). The resulting data output by the accelerometers  120   a ,  120   b ,  120   c  may then be wirelessly transmitted to the computer  96  via a wireless transmitter  124 , such as an ultra-wide band transmitter, and utilized in combination with data from the other modules to ascertain the appropriate diagnosis. 
         [0056]      FIG. 8  illustrates one example of a plurality of thin film accelerometers (four are shown,  120   a ,  120   b ,  120   c ,  120   d ) that are suitable for detecting the vibrations produced by motion of the knee joint  50 . Thin film accelerometers  120   a ,  120   b ,  120   c ,  120   d  may be used in lieu of sound sensors because of better performance and less noise susceptibility. The thin film accelerometers  120   a ,  120   b ,  120   c ,  120   d  may also be used as a localizer and include the same circuitry. The accelerometers  120   a ,  120   b ,  120   c ,  120   d  are attached to the patients so the outputs may be amplified, digitized, and sent wirelessly to the computer  96  as described below. 
         [0057]    With reference now to  FIGS. 4-6  and  9 , the foot module  92  (also referred to as the contact force module (“CFM”)) is shown and includes a plurality of pressure sensors  130  that are utilized to detect pressure or a contact force occurring at the bottom of the foot (not shown) when the knee joint  50  ( FIG. 1 ) is moved through a range of motion under a loaded condition. In other words, as the patient walks, jogs, runs, etc., the foot module  92  detects pressure data at the bottom of the foot when the foot is partially or fully in contact with the ground. In exemplary form, the pressure sensors  130  are incorporated into an insole  132  of a shoe  134  that conforms to the general shape of a patient&#39;s foot. Because humans have different sized feet, the insoles  132  may be incrementally sized to accommodate humans with differently sized feet or to accommodate a particular type of shoe  134  (or lack thereof) needed for a particular activity. 
         [0058]    The pressure sensors  130  may be arranged in a grid-shaped pattern on the insole  132 , which may include a series of rows and columns. The pressure sensors  130  are exposed to the underside of a patient&#39;s foot so that the location and amplitude (or amount) of the contact forces applied by the foot to the shoe  134 , by way of the insole  132 , may be measured. As will be discussed in more detail hereafter, the location of the pressures and the relative amount of pressures provides information relevant to diagnosis of injury. For example, the detected pressures of a patient with a limp caused by a knee joint injury would differ from the detected pressures of a patient with a healthy knee joint and a normal gait. 
         [0059]    In one embodiment, each sensor  130  may include a capacitor having a deformable dielectric between two electrode plates. Changes in the pressure applied to the plates cause a strain, or deformation, of the dielectric medium. Thus, a pressure applied to the capacitive sensor  130  changes the spacing between the plates and the measured capacitance. The capacitive sensors  130  are arrayed across the area of pressure measurement to provide discrete pressure data points corresponding to strains/deformation at the various locations of the array. These strains/deformations are used to find the stresses and thus the compressive forces and to calculate the output of pressure data having units of force per unit area and time (i.e., N/m sec). 
         [0060]    The sensors  130  in the grid-shape enable positioning of each detected pressure from each of the sensors  130  relative to another sensor  130 . The resultant data, which includes a two-dimensional map of the pressure sensors  130 , is either stored on the computer  96  or stored locally with the sensors  130 . The resulting data may be wirelessly transmitted to the computer  96  via a wireless transmitter  136 , such as an ultra-wide band transmitter. Using the 2D map of the sensors  130  stored on the computer  96  in combination with the received sensor pressure data, the computer  96  is operative to generate data tying detected pressure to position, specifically the position of one pressure sensor  130  with respect to another. 
         [0061]    By tying amounts of compressive force to its applied position, the foot module  92  provides data reflecting precisely what pressures are exerted at what location. In addition, the computer  96  may include an internal clock  97  to associate a time of which the pressure is applied with the pressure data generated by the pressure sensors  130 . Accordingly, the diagnostic system  82  not only knows how much pressure was exerted and the location where the pressure was applied, but also has time data indicating the duration of the applied pressures. Again, by tying the pressure data generated by the pressure sensors  130  to time, the pressure data can be correlated with the sound data generated by the VA module  88  using a common time scale. As a result, the diagnostic system  82  may evaluate how pressures exhibited at the bottom of the foot change as a function of time, along with how the vibrational data changes during the same time. 
         [0062]      FIGS. 4-6  and  10  illustrate the details of the JKT module  86 , which comprises an ultrasound creation and positioning submodule  140 , an ultrasound registration submodule  142 , and an ultrasound dynamic movement submodule  144 . Specifically, each submodule  140 ,  142 ,  144  includes an A-mode ultrasound transducer to generate sound and to detect reflected sound, wherein the reflected sound is representative of the structure, position, and acoustical impedance of the knee joint  50  ( FIG. 1 ). Commercially-available transducers may include, for example, an immersion unfocused 3.5 MHz transducer, such as those that are available from Olympus Corp. (Tokyo, Japan). Those skilled in the art are familiar with the operation of ultrasound transducers generally and, more specifically, an A-mode ultrasound transducer that generates sound pulses and detects sound that is reflected at tissue boundaries of tissues having different acoustic impedances. The magnitude of the reflected sound and the time delay are utilized to determine the distance between the ultrasound transducer and the tissue interface. 
         [0063]    In the illustrated embodiment, the A-mode ultrasound transducers are utilized to detect the interface between bone and the surrounding soft tissue so that the location of the bone surface may be determined. Because the operation of ultrasound transducers (including the A-mode ultrasound transducers) is well known to those skilled in the art, a detailed discussion of the operation of ultrasound transducers in general, and A-mode ultrasound transducers specifically, has been omitted only for purposes of brevity. 
         [0064]    The ultrasound creation and positioning submodule  140  as shown in  FIG. 10  comprises one or more A-mode ultrasound transducers  150  fixedly mounted to a wand  152 . The wand  152  further includes at least one positioning device  170 . In this exemplary embodiment, the ultrasound creation and positioning submodule  140  is physically separate from the ultrasound registration submodule  142  ( FIG. 6 ) and the ultrasound dynamic movement submodule  144  ( FIG. 6 ), the latter two of which are mounted to a rigid knee brace  220  schematically illustrated in  FIG. 17 . In this fashion, the ultrasound creation and positioning submodule  140  is repositionable with respect to the rigid knee brace  220  ( FIG. 17 ) and adapted to place one or more of its A-mode ultrasound transducers  150  in contact with the patient&#39;s epidermis, proximate the knee joint  50  ( FIG. 1 ). It should be noted, however, that the knee brace  220  ( FIG. 17 ) does not have to be rigid, other than the linkages between certain components. Moreover, the knee joint  50  ( FIG. 1 ) may be scanned by the ultrasound wand  152  before positioning the brace  220  ( FIG. 17 ) thereon. 
         [0065]    One of the functions of the ultrasound creation and positioning submodule  140  is to generate an electrical signal that is representative of the ultrasonic wave detected by the transducers  150  as the wand  152  moves over the patient&#39;s epidermis, proximate the knee joint  50  ( FIG. 1 ). The ultrasound transducers  150  receive the ultrasonic wave based upon the magnitude of the reflected ultrasonic wave from the bone-tissue interface. As discussed previously, the magnitude of the electrical signal and the delay between the generation of the ultrasonic wave by the ultrasound transducer  150  to detection of the reflected ultrasonic wave by the ultrasound transducer  150  is indicative of the distance to the bone underneath the transducer  150 . But, distance data alone is not particularly useful; therefore, one or more positioning devices  170  are used to provide a 3D coordinate system, one example of which is shown in  FIG. 11 . 
         [0066]    The positioning devices  170  of the ultrasound creation and positioning submodule  140  are fixedly mounted to the wand  152  and may include any of a number of positioning devices  170 . For example, the wand  152  may include one or more optical devices (as the positioning devices  170 ) that are configured to generate, detect, and/or reflect pulses of light. These pulses of light interact with a corresponding detector or light generator to discern the position of the wand  152 , in 3D space, and with respect to a fixed or reference position. One such device includes a light detector configured to detect pulses of light emitted from light emitters having known positions. The light detector detects the light and sends a representative signal to the computer  96  or otherwise a controller (not shown) of the light detector. The computer  96  is also provided the time at which the light pulses were emitted by the optical devices  170 . In this matter, the computer  96  determines the position of the wand  152  relative to the known positions of the detectors. Because the ultrasound transducer  150  and the optical devices  170  are fixedly mounted to the wand  152 , the position of the ultrasound transducers  150  with respect to the position of the optical devices  170  is known. Similarly, because the ultrasound transducers  150  are generating signals representative of the straight line distance between the transducers  150  and the bone-tissue interface, and the position of the transducers  150  with respect to the optical devices  170  is known, the position of the bone-tissue interface with respect to the optical devices  170  may be determined. In other words, as the wand  152  moves over the patient&#39;s epidermis, the optical devices  170  generate data that is determined, by the computer  96 , to represent that the relative position of the optical devices  170  with respect to the light detectors has changed in the 3D coordinate system. This change in the position of the optical devices  170  may be easily correlated to the position of the bone-tissue interface, in 3D, because the position of the bone-tissue interface relative to the ultrasound transducers  150 , as well as the position of the optical devices  170  with respect to the ultrasound transducers  150  are known. Accordingly, the 3D position data may be used in combination with the fixed position data (distance data for the position of the ultrasound transducers  150  with respect to the optical devices  170 ) for the ultrasound transducers  150  in combination with the distance data generated in response to the signals received from the ultrasound transducers  150  to generate composite data. The composite data may, in turn, be used to create a plurality of 3D points representing a plurality of distinct points on the surface of the bone, along the bone-tissue interface. As will be discussed in more detail below, these 3D points are utilized in conjunction with a default bone model to generate a virtual, 3D representation of the patient&#39;s bone. 
         [0067]    Alternatively, the positioning devices  170  may comprise one or more inertial measurement units (“IMUs”). IMUs are known to those skilled in the art and include accelerometers, gyroscopes, and magnetometers that work together to determine the position of the IMUs in a 3D coordinate system. Because the A-mode ultrasound transducer  150  and the IMUs  170  are fixedly mounted to the wand  152 , the position of the ultrasound transducers  150  with respect to the position of the IMUs  170  is known. Similarly, because the ultrasound transducers  150  are generating signals representative of the straight line distance between the transducers  150  and the bone-tissue interface and the position of the transducers  150  with respect to the IMUs  170  is known, the position of the bone-tissue interface with respect of the IMUs  170  may be determined. In other words, as the wand  152  moves over the patient&#39;s epidermis, the IMUs  170  generate data that is determined, by the computer  96 , to represent that the relative position of the IMUs  170  has changed in the 3D coordinate system. This change in the position of the IMUs  170  may be easily correlated to the position of the bone-tissue interface in 3D because the position of the bone tissue interface relative to the ultrasound transducer  150  is known, as is also the position of the IMUs  170  with respect to the ultrasound transducers  150 . Accordingly, the 3D position data may be used in combination with the fixed position data (distance data for the position of the ultrasound transducers  150  with respect to the IMUs  170 ) for the ultrasound transducers  150  in combination with the distance data generated in response to the signals received from the ultrasound transducers  150  to generate the composite data as described above. 
         [0068]    Referring now also to  FIGS. 11-12 , the positioning devices  170  may still alternatively comprise one or more ultra-wide band (UWB) transmitters. UWB transmitters are known to those skilled in the art, but the use of UWB transmitters and receivers for millimeter resolution 3D positioning is novel. In that regard, one or more UWB transmitters  170  are fixedly mounted to the wand  152  and configured to sequentially transmit UWB signals to three or more UWB receivers  172  having known positions in a 3D coordinate system. This embodiment of the positioning device  170  is comprised of active tags or transmitters  170  that are tracked by the UWB receivers  172 . The system architecture of the UWB transmitter  170  is shown in  FIG. 11  where a low noise system clock (“crystal clock”)  174  triggers a baseband UWB pulse generator  176  (for instance a step recovery diode (“SRD”) pulse generator). The baseband pulse from the baseband UWB pulse generator  176  is upconverted by a local oscillator  178  via a double balanced wideband mixer (not shown). The upconverted signal is amplified and filtered (“bandpass filter”  180 ). Finally the signal is transmitted, via an omnidirectional antenna  182 , to the computer  96  ( FIG. 6 ). The UWB signal may travel through an indoor channel where significant multipath and pathless effects cause noticeable signal degradation. 
         [0069]    The UWB receiver  172  architecture in accordance with one embodiment is shown in  FIG. 12 . The signal is received via a directional UWB antenna  184  and is filtered and amplified (“bandpass filter”  186 ), downconverted by a local oscillator  187 , and low-pass filtered (“LPF”)  188 . A sub-sampling mixer  190  triggered by a second low noise system clock (“crystal clock”)  192  is used to tune extend the pulse by about 1,000 to about 100,000 times. This effectively reduces the bandwidth of the UWB pulse and allows sampling by a conventional analog-to-digital converter (“ADC”)  194 . 
         [0070]    Each UWB transmitter  170  and receiver  172  is in communication with the computer  96 . Accordingly, the computer  96  detects each time the UWB transmitter  170  transmits a UWB signal, as well as the time at which the UWB signal was transmitted. Similarly, the computer  96  detects the position of each of the UWB receivers  172  in the 3D coordinate system, as well as the time at which the UWB signal was received. The final time-difference-of-arrival (“TDOA”) calculation, via a UWB positioning system  183 , is shown in  FIG. 13 . 
         [0071]    Referring now to  FIG. 13 , for the TDOA calculation, at least four base receivers  172  (Rx 1 , Rx 2 , Rx 3 , Rx 4 ) are needed to localize the 3D position of the UWB transmitter  170  (“Tag”). The geometry of the receivers Rx 1 , Rx 2 , Rx 3 , Rx 4  has important ramifications on the achievable 3D accuracy through what is known as geometric position dilution of precision (“PDOP”). A combination of novel filtering techniques, high sample rates, robustness to multipath interference, accurate digital ranging algorithms, low phase noise local oscillators, and high integrity microwave hardware are needed to achieve millimeter range accuracy (e.g. ranging from about 5 mm to about 7 mm in 3D real-time). An analogy of the UWB positioning system  183  to a GPS system  185  is shown in  FIG. 14 . 
         [0072]      FIG. 15  shows actual experimental errors in each of the x-, y-, and z-coordinates for detecting the position of the UWB transmitter  170  in 3D space and in real-time for over 1000 samples while the transmitter  170  is moving freely within the 3D space. 
         [0073]    Because the A-mode ultrasound transducers  150  and the UWB transmitters  170  are fixedly mounted to the wand  152 , the position of the ultrasound transducers  150  with respect to the position of the UWB transmitters  170  is known. Similarly, because the ultrasound transducers  150  are generating signals representative of the straight line distance between the ultrasound transducers  150  and the bone-tissue interface and the position of the ultrasound transducers  150  with respect to the UWB transmitters  170  is known, the position of the bone-tissue interface with respect to the UWB transmitter  170  may be determined. In other words, as wand  152  moves over the patient&#39;s epidermis, the UWB transmitters  170  transmit UWB signals that are correspondingly received by the UWB receivers  172 . These UWB signals are processed by the computer  96  in order to discern whether the relative position of the UWB transmitters  170  has changed in the 3D coordinate systems, as well as the extent of such a change. This change in 3D position of the UWB transmitters  170  can be easily correlated to the position of the bone-tissue interface in 3D because the position of the bone relative to the ultrasound transducer  150  and the position of the UWB transmitters  170  with respect to the ultrasound transducer  150  are known. Accordingly, the UWB 3D position data may be used in combination with the fixed position data (distance data for the position of the ultrasound transducers  150 ) to generate the composite data as described above. 
         [0074]    Regardless of the positioning device  170  utilized with the ultrasound creation and positioning submodule  140 , the wand  152  is repositioned over the skin of the patient, proximate to the knee joint  50  ( FIG. 1 ) while the knee joint  50  ( FIG. 1 ) is bent. Bending the patient&#39;s knee joint  50  ( FIG. 1 ) during data acquisition enables the creation of a 3D series of points for each of the bones of the knee joint  50  ( FIG. 1 ) (the distal femur  52 , the proximal tibia  54 , and the patella  56 ). Thus, as the wand  152  is repositioned, the data from the transducer  150  is transmitted to a wireless transmitter  200  mounted to the wand  152 . When the wireless transmitter  200  receives the data from the transducers  150 , the transmitter  200  transmits the data via a wireless link to the computer  96 . 
         [0075]    In order to power the devices on-board the wand  152 , an internal power supply (not shown) may be provided. In one embodiment, the internal power supply comprises one or more rechargeable batteries. 
         [0076]    Transformation is needed for transforming the position data from a reference coordinate frame of reference to a world frame of reference. According to one embodiment of the present invention, a linear movement of the ultrasound transducer  150  may be described: 
         [0000]        v ( n+ 1)= v ( n )+ a ( n ) dt   Equation 1
 
         [0000]        s ( n+t )= s ( n )+ v ( n ) dt= 0.5 a ( n ) dt   2   Equation 2
 
         [0000]    where s(n+1) is the position of the ultrasound transducer  150  at a current state, s(n) is the position from a previous state, v(n+1) is the instantaneous velocity of the current state, v(n) is the velocity from previous state, a(n) is the detected acceleration, and dt is the sampling time interval. The previous equations describe the dynamic motion and positioning of a point in 3D Euclidean space. Additional information is needed to describe 3D orientation and motion. 
         [0077]    The orientation of the ultrasound transducer  150  may be described by using a gravity-based accelerometer (for example ADXL-330, analog device) and extracting the tilting information from each of a pair of orthogonal axes. The acceleration output on each of the x-, y-, or z-axes is due to gravity and is equal to the following: 
         [0000]        A   i =( V   outx   −V   off )= S   Equation 3
 
         [0000]    where A i  is the acceleration of the ultrasound transducer  150  along each of the x-, y-, or z-axes, V outx  is the voltage output on each of the x-, y-, or z-axes, V off  is the offset voltage, and S is the sensitivity of the accelerometer. The yaw, pitch, and roll may be thus calculated as: 
         [0000]    
       
         
           
             
               
                 
                   ρ 
                   = 
                   
                     arctan 
                     ( 
                     
                       
                         A 
                         x 
                       
                       
                         
                           
                             A 
                             y 
                             2 
                           
                           + 
                           
                             A 
                             z 
                             2 
                           
                         
                       
                     
                     ) 
                   
                 
               
               
                 
                   Equation 
                    
                   
                       
                   
                    
                   4 
                 
               
             
             
               
                 
                   ϕ 
                   = 
                   
                     arctan 
                     ( 
                     
                       
                         A 
                         y 
                       
                       
                         
                           
                             A 
                             x 
                             2 
                           
                           + 
                           
                             A 
                             z 
                             2 
                           
                         
                       
                     
                     ) 
                   
                 
               
               
                 
                   Equation 
                    
                   
                       
                   
                    
                   5 
                 
               
             
             
               
                 
                   θ 
                   = 
                   
                     arctan 
                     ( 
                     
                       
                         
                           
                             A 
                             y 
                             2 
                           
                           + 
                           
                             A 
                             x 
                             2 
                           
                         
                       
                       
                         A 
                         z 
                       
                     
                     ) 
                   
                 
               
               
                 
                   Equation 
                    
                   
                       
                   
                    
                   6 
                 
               
             
           
         
       
     
         [0000]    where pitch is ρ (the x-axis relative to the ground), roll is φ (the y-axis relative to the ground) and roll is θ (the z-axis relative to the ground). Since the accelerometer is gravity-based, the orientation does not require information from the previous state once the accelerometer is calibrated. The static calibration requires the resultant sum of accelerations from each of the three axes to equal 1−g (where g is the nominal acceleration due to gravity at the Earth&#39;s surface at sea level, defined to be precisely 9.80665 m/s 2  (approximately 32,174 ft/s 2 )). Alternatively, an orientation sensor that provides yaw, pitch and roll information of the bodily tissue in question may be used. One such orientation sensor may be the commercially-available model IDG-300 from InvenSense (Sunnyvale, Calif.). The orientation of the ultrasound transducer  150  may then be resolved by using, for example, a direction cosine matrix transformation: 
         [0000]        X   2   CθCφCθCφS   p −SθC p   CθSφC   p   −SθS   p   X   1  
 
         [0000]        Y   2   =SθCφSθSφS   p   −CθC   p   SθSφC   p   −CθS   p   Y   1   Equation 7
 
         [0000]    
       
      
       Z 
       2 
       −SφCφS 
       p 
       CθC 
       p  
      
     
         [0000]    where C represents cosine and S represents sine. 
         [0078]    Referring again to  FIGS. 4-6 , and now also to  FIG. 16 , the PEAUMR module  84  is described in greater detail. The PEAUMR module  84  constructs a 3D model of the patient&#39;s knee joint  50  ( FIG. 6 ) by converting the transcutaneously acquired a set of 3D data points (using the tracked pulse echo A-mode ultrasound transducer  150 ), that, in total, are representative of the shape of the bone-tissue interface and therefore each bones&#39; surface. 
         [0079]    Before the patient data is acquired, software residing on the computer  96  may request a series of inputs from the user to adapt the diagnostic system  82  to equipment specific devices and the particular portion of the musculoskeletal anatomy to be modeled. For example, a menu  204  on a user interface  206  may be presented for the user to select the type of digitizer, which may include, without limitation, ultrasound. After the type of digitizer is selected, the user may actuate buttons  205   a ,  205   b  to connect to or disconnect from the digitizer, respectively. 
         [0080]    As wand  152  moves over the patient&#39;s epidermis, the set of points is generated, numerically recorded, viewable in a data window  210 , and ultimately utilized by the software to conform a selected bone model to the patient&#39;s actual bone shape. Consequently, the wand  152  is repositioned over the bones (the distal femur  52 , the patella  56 , the proximal tibia  54 ) for approximately 30 seconds so that the discrete points to typify the topography of the bone. Repositioning the wand  152  over the bone in question for a longer duration results in more 3D points being generated increases the resolution and improves the accuracy of the patient-specific bone model. A partial range of motion of the knee joint  50  ( FIG. 1 ) while repositioning the wand  152  over the knee joint ( FIG. 1 ) aids in scanning additional portions of the bone in question for new 3D points that may have been obscured by other bones in another range of motion position. 
         [0081]    Before, during, or after the ultrasound data is acquired, the software provides various drop-down menus allowing the software to load a bone model  208  that is roughly the same shape as the patient&#39;s bone. The computer  96  receives the ultrasound data, the computer  96  includes software that interprets the A-mode ultrasound transducer data and constructs a 3D map having discrete 3D points corresponding to points on the surface of the scanned bone. That is, the shape of the patient&#39;s bone is reconstructed in virtual space, using a set of points outlining the surface of the patient&#39;s bone as acquired by the tracked ultrasound transducer  150  ( FIG. 10 ). The set of points is applied to an atlas-based deformable model software to reconstruct the patient-specific 3-D model. 
         [0082]    More specifically, the computer  96  may include a database having a plurality of bone models of various portions of the musculoskeletal system, for example, the femur  52 , the tibia,  54 , and the patella  56 , that are classified and selectable in a menu  212 , for example based upon ethnicity, gender, height ranges, the side of the body, and so forth. Each of these classifications is accounted for in a drop-down menu of the software so that the model initially chose by the software most closely approximates the body of the patient. 
         [0083]    For mapping each bone, the computer  96  uses either a default bone model or the selected bone model as a starting point to construction of the ultimate patient-specific, virtual bone model. The default bone model may be a generalized average, as the morphing algorithms use statistical knowledge of a wide database population of bones for a very accurate model. The selected bone model expedites computation. For example, in the case of generating a patient-specific model of the femur  52  where the patient is a 53 year old, Caucasian male, who is six feet tall, a default femoral bone model is selected based upon the classification of Caucasian males having an age between 50-60, and a height ranging from 5′10″ to 6′2″. In this manner, selection of the appropriate default bone model more quickly achieves an accurate patient-specific, virtual bone model because of the number of iterations between the patient&#39;s actual bone (typified by the 3D map of bone points) and the default bone model are reduced. Nevertheless, in view of the model bones taking into account numerous traits of the patient (ethnicity, gender, bone modeled, and body side of the bone), it is quite possible to construct an accurate patient-specific 3D model with as few as 150 data points comprising the set which typically may be acquired by repositioning the wand  152  over the bone for 30 seconds for each bone. Ultrasound will not be affected whether the patient has a prosthetic implant. 
         [0084]    After the appropriate bone model is selected, the computer  96  superimposes the 3D points onto the default bone model and, thereafter, carries out a deformation process so that the bone model exhibits the 3D bone points detected during the signal acquisition. The deformation process also makes use of statistical knowledge of the bone shape based upon reference bones of a wide population. After the deformation process is complete, the resulting bone model is a patient-specific, virtual 3D model of the patient&#39;s actual bone. The foregoing process is repeated for each bone comprising the specific joint to create patient-specific, virtual 3D models of the patient&#39;s anatomy. 
         [0085]    Referring back to  FIGS. 4-7 , and now also  FIG. 17 , the JKT module  86  may be configured to track the kinematics of the knee joint  50  ( FIG. 1 ) and display the kinematics on the patient-specific 3D bone model generated by the PEAUMR module  84  using, for example, one or more bone motion tracking braces  220 . Generally, the bone motion tracking brace  220  includes pulse echo A-mode ultrasound transducers  222  to transcutaneously localize the bone-tissue interface and derive a set of points outlining each bone&#39;s surface. 
         [0086]    Turning specifically to  FIG. 17 , the brace  220  includes a plurality of A-mode ultrasound transducers  222  fixedly mounted to the knee brace  220 . Specifically, in the context of a knee joint  50 , there are at least two A-mode ultrasound transducers  222  (i.e., “a transducer group”  222   a ,  222   b ) fixedly mounted to the knee brace  220  for tracking of the tibia  54  ( FIG. 1 ) and the femur  52  ( FIG. 1 ). In other words, the knee brace  220  includes at least six ultrasound transducers  222  in order to track the two primary bones  52 ,  54  ( FIG. 1 ) of the knee joint  50 . Each transducer group  222   a ,  222   b  includes a rigid, mechanical connection linking the transducers  222  and the positioning devices  224  to the knee brace  220 . In this manner, the relative positions of the transducers  222  with respect to one another do not change. A first transducer group  222   a  at least partially circumscribes a distal portion of the femur  52  ( FIG. 1 ); while a second transducer group  222   b  at least partially circumscribes a proximal portion of the tibia  54  ( FIG. 1 ); and an optional third transducer group (not shown) overlies the patella  56  ( FIG. 3 ) if patella kinematics are desired. The ultrasound registration submodule  142  is accordingly configured to provide a plurality of static reference points for each bone as the bone is moved through a range of motion. 
         [0087]    Each ultrasound transducer  222  is tracked using an accelerometer or a sensor-specific localizer (or any other appropriate inertial sensor). The tracking may then be used to generate localized bone points from the outputs of the ultrasound transducers  222  and to virtually display bone movement on the 3D model while the knee joint  50  ( FIG. 1 ) is taken through the range of motion. 
         [0088]    Referring to  FIGS. 6 and 17 , the ultrasound dynamic movement submodule  144  comprises a plurality of positioning devices  224  that is configured to feed information to the computer  96  regarding the 3D position of each transducer group  222   a ,  222   b  of the ultrasound registration submodule  142 . In exemplary form, the position devices  224  may comprise light detectors operative to detect pulses of light emitted from light emitters having known positions. The light detectors  224  detect the light and transmit representative signals to a control circuitry (not shown) associated with the knee brace  220 . The knee brace  220  transmits this information to the computer  96 , which also knows when the light pulses were emitted as a function of time and position. In this manner, the computer  96  may determine the position of the transducers  222  in the 3D coordinate system. Because the ultrasound transducers  222  and the optical devices  224  are fixedly mounted to the knee brace  220 , the position of the ultrasound transducers  222  with respect to the position of the optical devices  224  is known. Similarly, because the ultrasound transducers  222  are generating signals representative of the straight line distance between each of the ultrasound transducers  222  and the bone-tissue interface beneath, and the position of the ultrasound transducers  222  with respect to the optical devices  224  is known, the position of the bone-tissue interface with respect to the optical devices  224  may be easily determined. In other words, as the knee joint  50  ( FIG. 1 ) is moved, and correspondingly so too is the knee brace  220 , the optical devices  224  generate data that is determined by the computer  96  that the relative position of the optical devices  224  has changed in the 3D coordinate system. This change in the position of the optical devices  224  may be easily correlated to the position of the bone in question in 3D because the position of the bone relative to the ultrasound transducer groups  222   a ,  222   b  is known, as is the position of the optical devices  224  with respect to the ultrasound transducer groups  222   a ,  222   b . Accordingly, the optical devices  224  generate data that is used in combination with the fixed position data (distance data for the position of the ultrasound transducers  222 ) to generate the composite data. The composite data may, in turn, be used to create dynamically moving map of the bone on the patient-specific 3D model. 
         [0089]    Alternatively, the positioning devices  224  may be comprised of one or more IMUs. Because the ultrasound transducers  222  and the IMUs  224  are fixedly mounted to the knee brace  220 , the relative positions between the ultrasound transducers  222  and the IMUs  224  are known. Similarly, because the ultrasound transducers  222  are generating signals representative of the straight line distance between the transducer  222  and the bone-tissue interface, and the position of the transducers  222  with respect to the IMUs  224  is known, the position of the bone with respect to the IMUs  224  may be easily determined. In other words, as the knee joint  50  ( FIG. 1 ) with the knee brace  220  moves, the IMUs  224  generate data that is determined, by the computer  96 , as a change in the position of the IMUs  224 . This change in the position of the IMUs  224  may be easily correlated to the position of the bone in 3D because the position of the bone relative to the ultrasound transducer groups  222   a ,  222   b  is known, as well as the position of the IMUs  224  with respect to the ultrasound transducer groups  222   a ,  222   b . By way of example, because the ultrasound transducers  222  do not move with respect to the knee brace  220 , any movement of the IMUs  224  in space means that the knee brace  220  has also moved in space, and by continuing to track the distance data provided by each IMU  224 , the movement of the bone may be correspondingly tracked. IMU tracking of the bone movements requires a static registration between the IMUs  224  and an initial known body position (such as standing). The IMUs  224  enable measurement of the relative motion between different bones via their corresponding ultrasound transducer group data and the IMU data. The IMUs  224  may be used alone or in conjunction with other positioning devices  170  ( FIG. 10 ), such as those described in detail above. In this scenario, the IMU position is updated at a certain interval with the absolute position provided by the additional positioning system to minimize error. Therefore the two positioning systems act together as one positioning system. 
         [0090]    As was described previously with respect to the wand  152 , the positioning devices  224  of the brace  220  may alternatively be comprised of one or more ultra wide band (UWB) transmitters. In that regard, one or more UWB transmitters  224  are fixedly mounted to the brace  220  and operable to transmit sequential UWB signals to three or more UWB receivers (not shown) having known positions in the 3D coordinate system. Each UWB transmitter  224  is in communication with the computer  96 , as are the plurality of UWB receivers (not shown). Accordingly, the computer  96  detects each time the UWB transmitter transmits a UWB signal, as well as the time at which the UWB signal was transmitted. Similarly, the computer  96  detects the position of each of the UWB receivers (not shown) in the 3D coordinate system, as well as the time at which the UWB signal was received. The computer  96  may then use the custom digital signal processing algorithms to accurately locate the leading-edge of the received UWB pulse based on the position of each UWB receiver (not shown), the time when each UWB signal was received, and the time that the UWB signal was transmitted. The position may then be determined by the TDOA calculation as was described with reference to  FIG. 11 ). Again, because the ultrasound transducers  222  do not move with respect to the knee brace  220 , any movement of the transducers  222  in space means that the brace  220  has moved. The movement of the knee brace  220  is tracked using the computer  96  in combination with the UWB transmitters  224  and the UWB receivers (not shown). Similarly, because the fixed orientation between the UWB transmitters  224  and the ultrasound transducers  222  changes in position in the 3D coordinate system, the UWB transmitter  224  may correspondingly be used to track movement of each bone. 
         [0091]    In order to communicate information from the submodules  142 ,  144  to the computer  96 , the brace  220  may include a transmitter  228 , such as a UWB transmitter, in communication with the ultrasound transducer  222  to facilitate wireless communication of data to the computer  96 . It should be noted that if UWB transmitter  228  is also utilized as the positioning devices  224 , a dedicated transmitter  228  is unnecessary as the UWB transmitters  224  could function to also send ultrasound data directly to the computer  96  over a wireless link. 
         [0092]    It should be understood that use of the transmitter  228  and a field programmable gate array design enables the computations to be cammed out on a real-time basis. For example, as patient&#39;s knee joint  50  ( FIG. 1 ) is bent while wearing the brace  220 , the ultrasound data is immediately transmitted to the computer  96 , which in real-time, calculates and displays the position and movement of each bone with the 3D patient-specific bone model. 
         [0093]      FIG. 18  illustrates a knee brace  230  in accordance with another embodiment of the present invention. The knee brace  230  has a first sub-brace  232  positioned at the distal portion of the femur  52 , a second sub-brace  234  positioned at the proximal end of the tibia  54 , and a third sub-brace  236  positioned at the patella  56  ( FIG. 3 ). The sub-braces  232 ,  234 ,  236  include a plurality of transducers mounted thereto. Each transducer is responsible for determining the location of a point on the surface of the bone during movement of the knee joint  50 . The sub-braces  232 ,  234 ,  236  reduce the occurrence of problems of locating and tracking the bone using ultrasound data when the motion of the bone relative to the skin is small compared to the gross joint motion. There are at least three approaches disclosed herein for tracking the motion of the ultrasound transducers themselves. 
         [0094]      FIG. 19  illustrates the first approach commonly referred to herein as an “ITT” (individual transducer tracking) approach. In  FIG. 19 , each transducer  238  in the sub-brace  232  has an associated tracking module  240  to individually track each transducer  238 . Using the ITT approach, the transducers  238  may be supported by a flexible length of strap. 
         [0095]    Referencing  FIG. 20 , a sub-brace  241  according to the second approach is shown. The second approach, commonly referred to herein as an “ITML” (Inter-Transducers Mechanical Links) approach, involves the transducers  242  being connected to each other by movable mechanical links  244 . Each mechanical link  244  includes length and angle sensors  246  that allow for detection of the movement of the transducers  242  relative to one another and the relative translational motions of the links  244 . Every two links  244  are connected by a pivot pin  248  that allows rotation and translation of the links  244  relative to each other. The length and angle sensors  246  are mounted to at least one link  244  and proximate to the pivot pin  248  to allow for detection of the angle between adjacent  244  links. The ITML approach features a fewer number of localizers than the ITT approach of  FIG. 19 . 
         [0096]    Referring now to  FIG. 21 , a sub-brace  249  according to the third approach is shown. The third approach, commonly referred to herein as a “RT” (Rotating Transducer) approach, involves using a single ultrasound transducer  250  that is mounted to a carriage  252 . The carriage  252  traverses along a track  254 , located on the inner circumference  256  of the sub-brace  249 . For example, the carriage  252  may be moved along the track  254  by a string loop  258  that is wrapped around the drive shaft (not shown) of a motor  260 . When the transducer  250  reaches the motor  260 , the rotation direction of the motor  260  is changed and the transducer  250  moves in the opposite direction. 
         [0097]    A tracking module  262  such as an inertia-based localizer is mounted to the transducer  250  to track its motion. As the transducer  250  rotates within the inner circumference  256  of the sub-brace  249 , it collects data as to the bone-tissue interface. By using a single transducer  250 , the RT approach includes the advantage of lower cost than the stationary transducer designs and higher accuracy due to the greater number of localized bone surface points for each tracking step, while maintain a mechanical flexibility. 
         [0098]    Referring to  FIG. 22 , a localizer  270  of tracking each ultrasound transducer  238  ( FIG. 19 ) mounted to the sub-brace  232  ( FIG. 19 ) is shown. The localizer  270  comprises a plurality of nodes  272  with each node  272  comprising a CMOS accelerometer and a temperature sensor (not shown) for thermal drive comparison. Each node  272  is integrated to minimize noise and distortion. The outputs of the accelerometers  272  regarding the x-, y-, and z-coordinates and the temperature sensors (not shown) are directed to a multiplexer  274  (“MUX”) that multiplexes the signals. Multiplexed outputs are amplified by an amplifier  276  (“AMP”), and then directed to an ADC  278 . The digital conversion of the signal may be performed within or outside the accelerometers  272 . Outputting digital signals may then be directed to a wireless transmitter  280  by way of a parallel input/serial output device  282 . 
         [0099]    In  FIG. 23  a design alternative for the sub-brace  232  is shown. The electronic architecture includes a high voltage amplifier circuit  286  (“HV IX AMP”) feeding a voltage multiplex circuit (“HV MUX”)  288  to excite each ultrasound transducer  238  and thereby acts as an analog switch. The echo signals from each transducer  238  are multiplexed pursuant to a logic control directing the opening of the switches in the MUX  290  at precise intervals. An exemplary logic control is the MSP430, available from Texas Instruments, Inc. (Dallas, Tex.). The output from the MUX  290  is I amplified by a low noise AMP  292  (“LNA”) and the signal is conditioned using a conditioning circuit (for example, a time-gain-control (“TGC”) circuit  294  and a band-pass filter (“BPF”)  296 , and digitized using an ACS  298 . Electric power to the foregoing components is supplied by way of a battery  300 , which also supplies power to a wireless transmitter module  302 . In exemplary form, the wireless transmitter module  302  utilizes a universe asynchronous receiver/transmitter (“UART”) protocol. The wireless transmitter module  302  includes a wireless transmitter circuit  304  receiving the output from a first in-first out (“FIFO”) buffer (not shown) of the ADS  298  by way of a serial interface  306 . An output from the wireless transmitter circuit  304  is conveyed using a serial link coupled to an antenna  308 . Signals conveyed through the antenna  308  are broadcast for reception by a wireless receiver (not shown) coupled to a controller (not shown) or the computer  96  ( FIG. 6 ). 
         [0100]    Referring now to  FIGS. 24 and 25 , an exemplary high voltage circuit  310  is shown and may be used to trigger and generate the excitation energy for a piezoelectric crystal in the ultrasound transducer  238  ( FIG. 19 ). Exemplary high voltage circuits  10  for use in this embodiment may include, without limitation, the pulsar integrated circuit (HV379) available from Supertex, Inc (Sunnyvale, Calif.). 
         [0101]    Referencing  FIG. 26 , an exemplary high voltage multiplexer  312  is shown and may be used to trigger and excite multiple piezoelectric transducers  238  ( FIG. 19 ) without increasing the number of high voltage circuits  310  ( FIG. 24 ). Exemplary high voltage multiplexer  312  for use in this embodiment may include, without limitation, the high voltage multiplexer (HV2221) available from Supertex, Inc (Sunnyvale, Calif.). The advantage of using a high voltage multiplexer  312  is the ability to use CMOS level control circuitry, thereby making the control logic compatible with virtually any microcontroller or field programmable gate array that is commercially-available. 
         [0102]    Referring to  FIGS. 23 and 27 , an exemplary receiving circuit  314 , which comprises the MUX  290 , the LNA  292 , the TGC  294 , the BPF  296 , and the ADC  298  is shown and may be utilized to receive the echo signals from each transducer  238 . Exemplary receiving circuits  314  for use in the this embodiment include, without limitation, the AD9271 8-channel ultrasound receiving integrated circuits, available from Analog Devices, Inc. (Norwood, Mass.). 
         [0103]    With reference now to  FIGS. 28 and 29 , one method  316  of using X-ray fluoroscopy and in-vivo measurements of dynamic knee kinematics, as described above, for understanding the effects of joint injuries, diseases, and evaluating the outcome of surgical procedures is described. In the particular illustrated embodiment, and using the two aforementioned techniques, six degrees of freedom (“DOF”) are determined for the knee joint  50  ( FIG. 1 ) and include the position and orientation of each bone comprising the knee joint  50  ( FIG. 1 ). The accuracy of this method  316  is within 1° of rotation and 1 mm of translation (except for translations that are parallel to the viewing plane). 
         [0104]    Implementation of the method  316  includes joint movement visualization via the 3D model reconstruction with A-mode ultrasound system, as described previously. The method  316  also measures the vibrations produced to accurately localize the vibrational center and to determine the cause of the vibrations&#39; occurrence. 
         [0105]    Interpretation of the vibration and kinematic data is a complicated task involving an in-depth understanding of data acquisition, training data sets, signal analysis, as well as the mechanical system characteristics. Vibrations generated through the interactions of implant components, bones, and/or soft tissues result from induced by driving force leading to a dynamic response. The driving force may be associated with knee-ligament instability, bone properties, and conditions. A normal intact knee joint  50  ( FIG. 1 ) will have a distinct pattern of motion and vibrational characteristics. Once degeneration or damage occurs to the knee joint  50  ( FIG. 1 ), both the kinematic and vibrational characteristics change. This altering, for each type of injury or degeneration, leads to distinct changes (or signature) that may be captured by the kinematic and vibration methods described herein. 
         [0106]      FIGS. 28-34  illustrate a diagnostic system  320  configured to perform the method  316  in accordance with one embodiment of the present invention. The diagnostic system  320  includes the ID module  90  configured to diagnose soft tissue and bone injuries. For example, a first patient having a normal knee joint and a second patient having an anterior cruciate ligament deficit (“ACLD”) may exhibit a similar pattern of posterior femoral translation during progressive knee flexion; however, the first and second patients exhibit different axial rotation patterns of 30° of knee flexion. Accordingly, the ID module  90  includes three stages: (1) a first stage that involves data analysis, (2) a second stage that includes sending the data to a neural network for detecting an injury, and (3) a third stage that classifies or determines severity of a detected injury. 
         [0107]    The first stage includes acquisition of kinematic feature vectors, using multiple physiological measurements taken from the patient while the patient moves the knee joint  50  ( FIG. 1 ) through a range of motion. Exemplary measurements may include, without limitation, medial condyle anteroposterior (“MAP”) motion and lateral condyle anteroposterior (“LAP”) motion. The LAP motion pertains to the anterior-posterior (“AP”) distance of the medial and lateral condyle points  110 ,  114  ( FIG. 1 ) relative to a tibia geometric center. Other exemplary measurements may include lateral shear interferometer (“LSI”) measurement of the distance between the lateral femoral condyle  114  ( FIG. 1 ) and the lateral tibial plateau  321  ( FIG. 3 ), and medial shear interferometer (“MSI”) measurement of the distance between the medial, femoral condyle  310  ( FIG. 1 ) and the medial tibial plateau  321  ( FIG. 3 ) which includes the superior/inferior (“S/I”) distance of the lateral and medial condoyle points  114 ,  110  ( FIG. 1 ) to a tibial plane, as shown in  FIGS. 30A-30C . 
         [0108]    Feature vectors may also include the femoral position with respect to the tibia which is defined by three Euler angles  340 , three translation components with the vibrational signal  342 , and force data  344 . Examples of these vectors are shown in  FIGS. 31A-31C , respectively.  FIG. 32  is a graphical representation  346  showing the average medial and lateral condyle positions during a deep knee bend activity for the second patient having ACLD. The feature vectors that are extracted from the kinematic and vibration analyses are output to the neural network  98  ( FIG. 6 ) for determining the injury, as described in greater detail below. 
         [0109]      FIG. 33  illustrates one embodiment of a neural network classifier  322  having multiple binary outputs  323   a ,  323   b ,  323   c ,  323   d , i.e., each output is either a “1” or “0,” wherein the “1” corresponding to “yes” and the “0” corresponding to “no.” In this neural network classifier  322 , each output  323   a ,  323   b ,  323   c ,  323   d  represents the response of the neural network  98  ( FIG. 1 ) to a particular injury type. For example, one output  323   b  may represent the response for ACLD, wherein its state will be “1” if an ACL injury is detected, and “0” otherwise. Obviously, the neural network  98  ( FIG. 1 ) and the classifier  322  may be significantly more or less sophisticated, depending on the underlying model of the joint in question. 
         [0110]      FIG. 34  illustrates one embodiment of a construction  325  of the neural network  98  ( FIG. 6 ). The construction  325  includes formulating a supervised classifier using a training set  324  of the kinematic and vibration data corresponding to a dataset  326  of normal and injured knee joints. The neural network  98  ( FIG. 6 ) is trained with the training set  324  of vectors, wherein each vector consists of data (sound  328 , kinematic  330 , and force  332 ) collected from the knee joint  50  ( FIG. 1 ). 
         [0111]    Fluoroscopy data  333  may be used to calculate the kinematics. While fluoroscopy data  333  is highly accurate, it requires the patient to remain within the small working volume of the fluoroscope unit and subjects the patient to ionizing radiation for a prolonged period of time. For most dynamic activities where the joints are loaded, such as running, jumping, or other dynamic activities, fluoroscopy is an unacceptable alternative. Therefore, use of fluoroscopy data  333  is not required. 
         [0112]    It should further be noted that electromyography (“EMG”) electrodes  337  ( FIG. 6 ) may also be utilized as a data input for the computer  96  ( FIG. 6 ) and the neural network  98  ( FIG. 6 ). In this fashion, one or more EMG electrodes  337  ( FIG. 6 ) are mounted to the surface of the skin proximate the muscles adjacent the knee joint  50  ( FIG. 1 ) to monitor the electrical signal transmitted to the muscles in order to provide relevant data of a muscle injury or disorder. 
         [0113]    Once the neural network  98  ( FIG. 6 ) is trained, it may be used to classify new cases and categorize an injury type using these kinematic  330 , vibration  328 , and force  332  data. Those skilled in the art will readily understand that the types and classifications desired to be accommodated by the neural network  98  ( FIG. 6 ) necessarily include training the neural network  98  ( FIG. 6 ) on these very types of classifications. Exemplary types and classifications of injuries to mammalian knee joints include, without limitation, osteoarthritic conditions, soft tissue damage, and abnormal growths. Likewise, the neural network  98  ( FIG. 6 ) needs to be trained to differentiate between and normal and abnormal knee conditions. 
         [0114]    Referring again to  FIG. 29 , for a new patient  326 , acquired vibrational, kinematic, and force features  328 ,  330 ,  332  the knee joint  50  ( FIG. 1 ) are compiled and input as a testing set  327  to the trained neural network  334 . The trained neural network  334  then diagnoses the condition of the knee joint  50  ( FIG. 1 ), and returns one of the outputs  323   a ,  323   b ,  323   c ,  323   d.    
         [0115]    Although now shown, some embodiments of the method may be adapted so that the testing set  327  is acquired outside of a clinical setting. For example, a knee brace in accordance with an embodiment of the present invention may be worn by a patient for an extended period of time while performing normal activities. For example, the patient may wear a device incorporating components of at least one of the JKT module  86  ( FIG. 6 ), the VA module  88  ( FIG. 6 ), and the foot module  92  ( FIG. 6 ) during activities that are not reproducible in the office (for example, weight lifting, racquet ball, etc.) and that elicit the pain or patient&#39;s symptoms. In some embodiments, the patient may turn the device on immediately prior to the activity and/or the patient may mark onset of the pain or symptoms when it occurs. This enables analysis of the data range from few seconds before the marked time to see what abnormal sounds or joint kinematic were occurring. 
         [0116]    Data may be stored on a portable hard drive (or any other portable storage device) and then may be downloaded to exemplary systems for analysis. The data can be wirelessly transmitted and stored in a computer. It can also be stored with a miniature memory drive if field data is desired. If the occurrence of the pain is more random, some embodiments of the devices may continuously acquire data. Although, continuously monitoring devices may require a larger data storage capacity. 
         [0117]    It is understood that while the exemplary embodiments have been described herein with respect to the knee joint  50  ( FIG. 1 ), those skilled in the art will readily understand that the aforementioned embodiments may be easily adapted to other joints of the musculoskeletal system of a mammalian animal. For example, embodiments may be adapted for use on hips, ankles, toes, spines, shoulders, elbows, wrists, fingers, and temporomandibular joints. 
         [0118]    While the present invention has been illustrated by a description of various embodiments, and while these embodiments have been described in some detail, they are not intended to restrict or in any way limit the scope of the disclosed invention. Additional advantages and modifications will readily appear to those skilled in the art. The various features of the present invention may be used alone or in any combination depending on the needs and preferences of the user. This has been a description of the present invention, along with methods of practicing the present invention as currently known.