Patent Publication Number: US-10318863-B2

Title: Systems and methods for autoconfiguration of pattern-recognition controlled myoelectric prostheses

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a non-provisional that claims benefit to U.S. Provisional Patent Application No. 61/675,147, filed on Jul. 24, 2012, which is herein incorporated by reference in its entirety 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT (IF APPLICABLE) 
     This invention was made with Government support under Grant No. R-01-HD-05-8000 awarded by the Department of Health and Human Services, National Institutes of Health. The Government has certain rights in the invention. 
    
    
     FIELD 
     This disclosure relates generally to the field of human-machine interfaces, and in particular to a system and method for autoconfiguring pattern-recognition controlled myoelectric prostheses. 
     BACKGROUND 
     Myoelectric prostheses, which rely on electromyography (EMG) signals to control joint movement, are often used to effectively treat upper-limb amputation. The control principles used in commercially available prostheses have been available for many decades and rely on an estimate of the amplitude of strategically placed electrodes coupled with simple rules to form control signals for controlling the operation of the prosthesis. Only a limited number of movements may be restored and to achieve a particular task for movement of the prosthesis, and the movements must be controlled sequentially as only one motion may be controlled at a time. 
     Pattern recognition has also been used to extract control signals for prosthetic limbs. However, in order to achieve optimal or near-optimal use of the pattern-recognition controlled prosthetic limb, example data related to each type of limb movement should be recorded from each patient to train, configure, and calibrate prosthesis movement. In addition to configuring or training the prosthesis prior to initial use, prosthesis users may be required to reconfigure the prosthesis to maintain performance levels. Conventional pattern recognition training systems often require additional hardware and technological capacity, which can hamper the user&#39;s capability to reconfigure the prosthesis. 
     SUMMARY 
     In some embodiments a prosthesis guided training system can include a plurality of sensors for detecting electromyographic activity. A computing device, which can include a processor and memory, extracts data from the electromyographic activity. A real-time pattern recognition control algorithm and an autoconfiguring pattern recognition training algorithm are at least partially stored in the memory. In one embodiment, the computing device determines movement of a prosthesis based on the execution of the real-time pattern recognition control algorithm by the processor. The computing device can also alter operational parameters of the real-time pattern recognition control algorithm based on the processor executing the autoconfiguring pattern recognition training algorithm. 
     Additional objectives, advantages and novel features will be set forth in the description which follows or will become apparent to those skilled in the art upon examination of the drawings and detailed description which follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a simplified illustration of one embodiment of a prosthesis guided training system; 
         FIG. 2  is a simplified block diagram of one embodiment of a computing device and related components for the prosthesis guided training system; 
         FIG. 3  is a flow chart illustrating the operation of a real-time pattern recognition control algorithm for the prosthesis guided training system; 
         FIG. 4  is a flow chart illustrating the operation of an autoconfiguring pattern recognition training algorithm for the prosthesis guided training system; 
         FIG. 5A  is an image of a user employing a conventional prosthesis training system; 
         FIG. 5B  is an image of a user employing one embodiment of a prosthesis guided training system; and 
         FIGS. 6 and 7  are tables that depict averaged test subject responses to questionnaire items. 
     
    
    
     Corresponding reference characters indicate corresponding elements among the view of the drawings. The headings used in the figures do not limit the scope of the claims. 
     DETAILED DESCRIPTION 
     Referring to the drawings, embodiments of a prosthesis guided training system are illustrated and generally indicated as  100  in  FIG. 1 . In one embodiment, the prosthesis guided training system  100  can include a prosthesis  102  that is fitted to an individual  104 . More specifically, the prosthesis  102  can be configured to operate as a myoelectrically-controlled device that can be professionally custom fit for the user  104 . For example, as shown in  FIG. 1 , in one embodiment, the prosthesis  102  can be configured as a prosthetic limb for an individual with a shoulder disarticulation. In other embodiments, the prosthesis  102  can be configured differently, such as a prosthetic limb for individuals  104  with transradial and/or transhumoral amputations. In yet other embodiments, the prosthesis  102  can be configured for individuals  104  with amputations or disarticulations of other limbs. 
     Referring to  FIGS. 1 and 2 , in one embodiment the prosthesis guided training system  100  includes a computing device  106  for controlling the operation of one or more prosthesis  102 . In some embodiments, the computing device  106  may include at least one processor  108  in operative communication with a memory  110 . For example, the computing device  106  may be a personal computer, workstation, server, or mobile device, while the processor  108  may be a hardware device that processes software, other machine-readable instructions, retrieved data, and/or received data. In addition, the memory  110  may store software or other machine-readable instructions and data. The memory  110  may also include volatile and/or non-volatile memory. The memory  110  may include a database to store data related to parameters for various components of the prosthesis guided training system  100 , one or more prostheses  102 , one or more electromyography (EMG) signal patterns, or any other data. The computing device  106  may further include various hardware and accompanying software components, such as a signal amplifier  112  or a signal filter  114 , that may be configured for receiving EMG signal data from one or more EMG sensors  116 , via a signal input device  117 , and generating an output  118  that may be used in prosthesis  102  operations. For example, EMG signals can be collected by the EMG sensors  116 , transmitted to the signal input device  117  such that the EMG signal data can be conditioned and later transmitted to the computing device  106  for additional processing. 
     In addition, the computing device  106  may also include a communication system to communicate with one or more components of the prosthesis guided training system  100 , such as the EMG sensors  116 , and/or other sensors and/or computing devices and systems, over a communication network via wireline and/or wireless communications, such as through the Internet, an intranet, and Ethernet network, a wireline network, a wireless network. The computing device  106  may further include a display (not shown) for viewing data or one or more user interfaces (UI), such as a computer monitor, and an input device (not shown), such as a keyboard or a pointing device (e.g., a mouse, trackball, pen, touch pad, or other device) for entering data and navigating through data, including images, documents, structured data, unstructured data, HTML pages, other web pages, and other data. 
     The computing device  106  may include a database (not shown) and/or is configured to access the database. The database may be a general repository of data including, but not limited to user data, patient data, historical training data, or algorithms, among others. The database may include memory and one or more processors or processing systems to receive, process, query and transmit communications and store and retrieve such data. In another aspect, the database may be a database server. 
     According to one aspect, the computing device  106  includes a computer readable medium (“CRM”)  120 , which may include computer storage media, communication media, and/or any another available media that can be accessed by the processor  108 . For example, CRM  120  may include non-transient computer storage media and communication media. By way of example and not limitation, computer storage media includes memory, volatile media, nonvolatile media, removable media, and/or non-removable media implemented in a method or technology for storage of information, such as machine/computer readable/executable instructions, data structures, program modules, or other data. Communication media includes machine/computer readable/executable instructions, data structures, program modules, or other data and includes an information delivery media or system. Generally, program modules include routines, programs, instructions, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. 
     By way of example and not limitation, the CRM  120  may store executable instructions to execute a real-time pattern recognition control algorithm  200  ( FIG. 3 ) at a classification module  122  or an autoconfiguring pattern recognition training algorithm  300  ( FIG. 4 ) at a training module  124 . More specifically, in one embodiment, during an initial setup and configuration of the prosthesis guided training system  100  the computing device  106  can cause the processor  108  to execute the autoconfiguring pattern recognition training algorithm  300 , as described in further detail below. Moreover, during conventional operations of the prosthesis  102  (i.e., day-to-day, real-time activities), the computing device  106  can cause the processor  108  to execute the real-time pattern recognition control algorithm  200 , as described in further detail below. 
     Referring back to  FIG. 1 , in some embodiments, the computing device  106  can be incorporated with the prosthesis  102 . As a result, the processor  108  can execute or perform the real-time pattern recognition control algorithm  200  and/or the autoconfiguring pattern recognition training algorithm  300  and the output  120  need not be relayed to a remote prosthesis  102  because of the integral nature of the computing device  106  and the prosthesis  102 . Alternately, the computing device  106  can be remotely positioned with respect to the prosthesis  102  so that the output  120  is transmitted to the prosthesis  102  from a remote location. 
       FIG. 3  illustrates one method for executing the real-time pattern recognition control algorithm  200 . In some embodiments, the processor  108  of the computing device  106  executes the real-time pattern recognition control algorithm  200 . In one embodiment, the processor  108  can initiate the real-time pattern recognition control algorithm  200  at an EMG signal data input stage  202 , wherein an EMG signal data is acquired from the signal input device  117  based on a predetermined time interval. For example, the predetermined time interval can extend for a relatively short time period, such as between 100-250 milliseconds (ms). 
     After collection, the EMG signal data is transmitted to an EMG signal data conditioning stage  204 . At the EMG signal data conditioning stage  204 , operations such as, but not limited to, signal filtering, time aligning, thresholding, rectification, and other suitable signal conditioning processes are executed. The processor  108  executes a feature extraction phase  206 , such that the EMG signal data obtained during a particular predetermined time interval is further processed, reduced, and/or extracted. By way of example only, portions of the EMG signal data can be extracted based on the presence of one or more signal features, which can include, but are not limited to time-based signal features such as zero-crossings, slope sign changes, waveform length, root-mean-square, and/or mean-absolute value; frequency-based signal features such as wavelet transforms, wavelet packets, and/or Fourier transforms; and other auto-regressive model coefficients. 
     After the feature extraction phase  206 , the processed and extracted EMG signal data may be transmitted to a pattern recognition stage  208 , such that the processor  108  may compute level estimation  210  and classification  212 . In one embodiment, a classification operation  212  executes an algorithm, such as linear discriminant analysis (LDA), which may be used to identify an output class of user intent from the input set of EMG signal data (i.e., the direction and type of movement that the individual  104  wishes the prosthesis  102  to move). In some embodiments, classification operation  212  can execute other algorithms in addition to, or in lieu of LDA, including a Gaussian mixture model (GMM), a support vector machine (SVM), or an artificial neural network (ANN). When identifying the output class from the input set of EMG signal data, classification operation  212  employs a set of parameters such as: boundary-defining weights and offsets; neural network node information; and/or comparative models. 
     In one embodiment, a level estimation stage  210  can be used by the computing device  106  to compare an intensity of the processed EMG signal data to a set of stored signal parameters  214 , as shown in  FIG. 3 . As a result of level estimation stage  210 , the computing device  106  can determine a scaling factor of the output class&#39; intensity level (i.e., a magnitude of force or level of actuation with which the prosthesis  102  will move). The stored signal parameters  214  can include dynamic ranges of sets of calibration signals, such as weights, offsets, nodes, models, neurons, vectors, and other suitable data that can be stored in the memory  110  ( FIG. 2 ). Some or all of the stored signal parameters  214  can be created or modified during execution of the autoconfiguring pattern recognition training algorithm  300 . 
     As further shown in  FIGS. 1 and 3 , after the pattern recognition stage  208 , the processed EMG signal data can be further processed at an output conditioning stage  216 . The output conditioning stage  216  can combine the output class of motion intent (i.e., the result of classification  212 ) and estimated level of actuation (i.e., the result of level estimation  210 ) and use a transfer functionality to coordinate the processes of the prosthesis  102  such as rate-limiters, ramp activations, and exponential output profiles. Finally, at an actuation stage  218 , the processor  108  converts the data resulting from the output conditioning stage  216  to commands appropriate for the computing device  106  to direct the movement of the prosthesis  102 . In some embodiments, the real-time pattern recognition control algorithm  200  can be continuously repeated when the prosthesis guided training system  100  is in a real-time operation mode (e.g., in a non-training mode for day-to-day operations). 
     Referring now to  FIG. 1 , in one embodiment, the prosthesis guided training system  100  can include a training activation button  126  that is in communication (e.g., wired or wireless communication) with the computing device  106 . More specifically, the individual  104  can actuate, depress, and/or otherwise contact the training activation button  126  to initiate the processor  108  to execute the autoconfiguring pattern recognition training algorithm  300  ( FIG. 4 ). For example, the individual  104  can press the training activation button  126  for a predetermined time (e.g., about two seconds) to start the autoconfiguring pattern recognition training algorithm  300 . By requiring that the individual  104  depress the training activation button  126  for a predetermined time, accidental activation of the autoconfiguring pattern recognition training algorithm  300  can be avoided. In other embodiments, in addition to, or in lieu of the training activation button  126 , the prosthesis guided training system  100  can be configured so that the individual  104  may execute the autoconfiguring pattern recognition training algorithm  300  via voice command, a body-powered analog input switch, a muscle co-contraction, a specific output class of motion, and/or any other suitable manner of activation. 
       FIG. 4  illustrates one method for executing the autoconfiguring pattern recognition training algorithm  300 . In some embodiments, the autoconfiguring pattern recognition training algorithm  300  can be executed at any time. More specifically, the autoconfiguring pattern recognition training algorithm  300  can be executed before, during, and after the real-time pattern recognition control algorithm  200  is executed. For example, during use of the prosthesis  102 , the individual  104  can assess the responsiveness, accuracy, and/or dexterity of the prosthesis  102 . If the individual  104  wishes to retrain or recalibrate the prosthesis  102 , the individual  104  can execute the autoconfiguring pattern recognition training algorithm  300  at an initiation stage  301 . For example, the initiation stage  301  can include the individual  104  pressing the training activation button  126  to execute the autoconfiguring pattern recognition training algorithm  300 . 
     Once executed, the autoconfiguring pattern recognition training algorithm  300  assesses a position of the prosthesis  102 . More specifically, as noted above, the autoconfiguring pattern recognition training algorithm  300  can be executed during operations of the real-time pattern recognition control algorithm  200  ( FIG. 3 ). As a result, the prosthesis  102  may be in use (e.g., wrist flexed, elbow in an extended position, hand in an closed position, etc.) so that the prosthesis  102  may have to be returned to a resting position prior to continuing execution of the autoconfiguring pattern recognition training algorithm  300 . By way of example only, the computing device  106  may need to perform an optional step at a device positioning stage  302 . At the device positioning stage  302 , if the prosthesis  102  is in a non-resting position, the autoconfiguring pattern recognition training algorithm  300  may require that the prosthesis  102  return to a resting position (i.e., a home position) by coordinating self-actuation commands. At device positioning stage  302 , the individual  104  ensures that the prosthetic device  102  is in a known starting position and orientation (i.e., for the purposes of calibration and realignment). At device positioning stage  302  may include inherent motor and joint position data as well as timed endpoint homing sequences. 
     In one embodiment, after the prosthesis  102  is in the required position, the processor  108  executes a device prompting stage  304 , wherein the prosthesis  102  self-actuates or moves in one or more different directions, motions, or sequences of directions and motions. The self-actuations or movements can be used to direct the individual  104  to contract his or her muscles to mimic performing the self-actuation of the prosthesis  102 . Moreover, the self-actuations can occur at a variable or a constant speed. During the device prompting stage  304 , the computing device  106  can execute a user mimicking stage  306  to cause the individual  104  to generate EMG signal data corresponding to the self-actuation or movements of the prosthesis  102 . At the same time or substantially the same time, a data collection operation  308  can also be executed so that the individual-specific  104  EMG signal data can be collected and stored in the memory  110  with an appropriate indicator denoting the type of movement associated with the particular EMG signal data set. 
     For example, at device prompting stage  304 , the individual mimicking stage  306 , and data collection operation  308 , the prosthesis  102  moves so that a joint of the prosthesis  102  (e.g., the wrist) flexes, extends, or articulates in any other suitable orientation in a predetermined sequence. While visualizing the prosthesis  102  movements, the individual  104  can generate muscle contractions or may attempt to mimic the activity being performed by the prosthesis  102  and the EMG signal data generated by the individual  104  is captured and stored in the memory  110 . In other words, during or after the prosthesis  102  prompts the individual  104  with a movement, the individual  104  can mimic that movement with muscle contractions and the EMG signal data received by the EMG signal sensors  116  can be stored in the memory  110  with an appropriate indicator of the movement type. Additionally, after movement of the prosthesis  102  is terminated, the individual  104  may also cease moving. After the user  104  stops mimicking the prosthesis&#39;  102  movement and the prosthesis  102  returns to an at-rest or home position, the computing device  106  can store the EMG signal data from this relaxed or “no-motion” state as a basal or threshold level of EMG signal activity. 
     In one embodiment, the types of movements and the sequence of movements employed by the prosthesis  102  at device prompting stage  304  can be the same or substantially the same during each execution of the autoconfiguring pattern recognition training algorithm  300 . As a result, the quality and repeatability of the elicited EMG signal data can be improved, as can the individual&#39;s  104  comfort level with the prosthesis guided training system  100 . Also, by repeating the sequence and movements, a number of sessions required to produce satisfactory performance of the prosthesis guided training system  100  can be reduced. 
     As further shown in  FIG. 4 , after the computing device  106  stores the EMG signal data for a particular self-actuation of the prosthesis  102 , the autoconfiguring pattern recognition training algorithm  300  reaches a decision point  310 . At the decision point  310 , the computing device  106  determines whether any additional EMG signal data related to other movements is still required to complete execution of the autoconfiguring pattern recognition training algorithm  300 . For example, in embodiments where the prosthesis  102  is for an individual  104  with a transhumeral amputation, execution of the autoconfiguring pattern recognition training algorithm  300  may require EMG signal data from elbow motions, wrist motions, and hand motions to complete the calibration process. Accordingly, at the decision point  310 , the computing device  106  determines whether sufficient EMG signal data related the possible prosthesis  102  motions has been gathered and stored in the memory  110 . If sufficient EMG signal data has been gathered and stored (i.e., “yes” in  FIG. 4 ), the autoconfiguring pattern recognition training algorithm  300  may proceed to the next steps. If the computing device  106  determines that there is insufficient EMG signal data stored in the memory  110  (i.e., “no” in  FIG. 4 ), the device prompting stage  304 , the individual mimicking stage  306 , and data collection operation  306  can be repeated until sufficient EMG signal data is stored in the memory  110 . 
     In one embodiment, if the computing device  106  determines that sufficient EMG signal data has been stored in the memory  110 , the autoconfiguring pattern recognition training algorithm  300  proceeds to process the stored data and complete the calibration of the prosthesis  102 . The stored EMG signal data may be first processed at data conditioning stage  312 . In the data conditioning stage  312 , the computing device  106  employs activity thresholds, algorithms, and other methods to further identify, mark, and associate training data. After conditioning the EMG signal data, a feature extraction process  314  may be executed. Similar to the real-time pattern recognition control algorithm  200 , the feature extraction process  314  further processes the EMG signal data. By way of example only, during operation of the feature extraction process  314 , portions of the EMG signal data can be extracted based on the presence of one or more signal features, which can include, but are not limited to: time-based signal features such as zero-crossings, slope sign changes, waveform length, root-mean-square, and/or mean-absolute value; frequency-based signal features such as wavelet transforms, wavelet packets, and/or Fourier transforms; and other auto-regressive model coefficients. In one embodiment, the feature extraction process  314  can differ from the feature extraction phase  206  in that the EMG signal data processed in the feature extraction phase  206  is time limited by the predetermined time interval. In other embodiments, the feature extraction process  314  and the feature extraction phase  206  can be the same or substantially the same process. 
     In one embodiment, after the feature extraction process  314 , the processed EMG signal data may be passed to a classifier training stage  316 . In the classifier training stage  316 , the processed EMG signal data can be further processed using one or more algorithms (e.g., LDA, GMM, SVM, ANN, etc.) to define the stored signal parameters  214  to be used at the classification stage  212  of the real-time pattern recognition control algorithm  200 . In one embodiment, the stored signal parameters  214  are stored in the memory  110  of the computing device  106  at system update stage  318 , for application by the real-time pattern recognition control algorithm  200 . After completion of the system update stage  318 , the user  104  can enter or re-enter the real-time pattern recognition control algorithm  200  to continue non-training operations of the prosthesis  102 . 
     In one embodiment, the autoconfiguring pattern recognition training algorithm  300  can include at least one additional step or stage that can be used to further establish basal or threshold EMG signal data. More specifically, after the device position stage  302 , but before the device prompting stage  304 , the prosthesis  102  can remain stationary for a short period of time before the computing device  106  begins the device prompting stage  304 . During this stationary period, EMG signal data can be collected while the individual  104  remains in a substantially relaxed state. The EMG signal data gathered during this relaxed state can function as basal myoelectric activity and can be used to calculate a threshold for the EMG signal data generated during subsequent training, calibration, and configuring movements. Moreover, this additional stage can provide the computing device  106  with a comparison level of EMG activity when the user  104  neglects to mimic the self-actuating prosthesis  102  at device prompting stage  304 . The threshold EMG signal data may provide additional training data for a “no-motion” category. 
     Additionally, some conventional training or calibration techniques may provide the individual  104  with an advanced warning prior to data collection stage  308 . For example, conventional prosthesis-training techniques, such as screen-guided training, can provide a countdown (e.g., visual and/or auditory) prior to beginning data collection stage  308 . As a result, the individual  104  can be prepared for data collection stage  308  and the computing device  106  does not necessarily detect significant background data. Because the prosthesis guided training system  100  does not rely on a training screen or auditory output, no advanced warning is provided to the individual  104 . As a result, at least a portion of the EMG signal data collected during execution of the autoconfiguring pattern recognition training algorithm  300  can include background signals that do not reflect the individual  104  mimicking movement of the prosthesis  102 . By creating an EMG signal data threshold, the stages of the autoconfiguring pattern recognition training algorithm  300  after data collection stage  308  can remove the non-useful portions of the EMG signal data, such as the delay that occurs before the individual  104  begins the user mimicking stage  306 , but after initiation of the device prompting stage  304 . 
     By way of example only,  FIGS. 5A and 5B  depict a comparison of the prosthesis guided training system  100  and a conventional prosthesis training system. For example, the conventional prosthesis training system can be configured as a screen-guided training system, as shown in  FIG. 5A . The conventional screen-guided training system can include prompting the individual  104  with visual and surrogate cues (i.e., virtual prosthesis  102  movements) displayed on a screen  128 . As a result, the attention of the individual  104  is directed at the screen  128  while the device remains static and stationary. In comparison, in one embodiment, the prosthesis guided training system  100  does not employ the screen  128  so that the attention of the individual  104  can be focused on the dynamic motions and self-actions of the prosthesis  102  during the device prompting stage  304 , the user mimicking stage  306 , and data collection stage  308 . 
     Relative to conventional, screen-guided training discussed above, the prosthesis guided training system  100  offers several advantages. First, the individual  104  can continue to wear the prosthesis  102  after decreased performance. Individuals  104  of some conventional screen-guided training systems are required to remove their prosthesis  102  should performance decline or the prosthesis  102  become unusable. For example, poor performance can originate from multiple causes, including broken or damaged parts, limb sweating, muscle fatigue, socket shift, and limb volume changes. Sometimes re-donning the prosthesis  102  can correct the problem; however, poor performance may require a visit to a prosthetist. No matter the issue, the prosthesis  102  may need to be removed or turned off, and this can occur at a time or place that can be inconvenient to the individual  104 . Because of this inconvenience, some individuals  104  may choose to leave a prosthesis  102  at home. In comparison, with the prosthesis guided training system  100 , at least some of the aforementioned issues that arise with the screen-guided training system can be overcome without having to disengage the prosthesis  102  or even needing to know what caused the decreased performance. 
     Second, the prosthesis guided training system  100  can eliminate some or all of the need for additional hardware or a surrogate-controlled prosthesis  102 . As previously mentioned, the prosthesis guided training system  100  does not require a screen  128 , monitor, or other visual display device. Accordingly, when the individual  104  needs to execute the autoconfiguring pattern recognition training algorithm  300 , the individual  104  does not need to seek out a visual display with the specific screen-guided training software. As result, expenses can be reduced because of the reduced need for equipment. Moreover, requirements on product developers can be reduced because graphical user interface development and software maintenance costs are greatly reduced, as is the demand for high-quality, high-bandwidth device-to-computer communication. 
     Additionally, individuals  104  can quickly operate the prosthesis guided training system  100 . More specifically, when the individual  104  dons the prosthesis  102  after a period of non-use, the individual  104  can quickly judge if they have acceptable control using what data is stored in the memory  110 . If the individual  104  is unsatisfied, their prosthesis  102  may have been donned in a slightly differently manner, thereby causing EMG sensor  116  to shift, the individual  104  may be more rested or fatigued, may be performing contractions differently, or their skin conditions may have changed and that these changes may affect the execution of the real-time pattern recognition control algorithm  200 . In these cases, the prosthesis guided training system  100  can help the individual  104  recalibrate their control of the prosthesis  102  and resume their activities of daily living. The execution of the autoconfiguring pattern recognition training algorithm  300  can be completed in about one minute. As a result, individuals  104  can relatively quickly complete the execution of the autoconfiguring pattern recognition training algorithm  300  in just about any location because there is no need for a screen  128  to complete the training process. 
     In some conventional training systems, the individual  104  or prosthetist may need to carefully adjust EMG signal gains, thresholds, boosts, and timings using a computer and proprietary graphical user interface. Moreover, many of these conventional systems do not rely on pattern recognition algorithms for day-to-day use of the prosthesis  102 . Conversely, because the prosthesis guided training system  100  collects the EMG signal data for training, the collected and processed gains, thresholds, and boosts can be automatically set. The collected EMG signal data can be used to recalibrate an individual&#39;s  104  dynamic signal output range for each motion every time the autoconfiguring pattern recognition training algorithm  300  is executed. Furthermore, when the sequence of self-actuations or movements during the device prompting stage  304  incorporates a range of movement speeds, a larger dynamic range of EMG signal data intensities could be acquired as training data, thereby enhancing the robustness of the prosthesis guided training system  100 . 
     Further, compared to conventional screen guided training systems, the prosthesis guided training system  100  offers a more real-time-like training and calibration experience, which can improve performance. When using a conventional screen guided training system, the individual  104  and the prosthesis  102  remain stationary and the individual&#39;s  104  attention is focused on the screen  128  and on generating distinct muscle contractions. During real-time use, both the individual  104  and the prosthesis  102  are actively moving, and the individual  104  is focused on the prosthesis  102  and the functional task at hand. However, under day-to-day, real-time conditions, the pattern of EMG signal generation for a distinct movement can change depending on positioning, current movement state, and whether the prosthesis  102  is carrying a load. During execution of the autoconfiguring pattern recognition training algorithm  300 , EMG signal data can be captured while the prosthesis  102  is moving, which can produce more robust stored signal parameters  214  for use in classification  212 . Further, the individual  104  consistency can be improved because the visual attention of the individual  104  can be focused on the prosthesis  102  during calibration and real-time use. 
     Finally, the repeated training sequence of the device prompting stage  304  can benefit the individual  104 . As a result, the quality and repeatability of the elicited EMG signal data can be improved, as can the individual&#39;s  104  comfort level with the prosthesis guided training system  100 . Also, by repeating the sequence and movements, a number of sessions required to produce satisfactory performance of the prosthesis guided training system  100  can be reduced. 
     EXAMPLES 
     The following section is intended as examples of the use of the prosthesis guided training system  100  according to some embodiments of the invention. The following examples are not to be construed as limitations. 
     For example, the individual&#39;s  104  enjoyment and comfort with the prosthesis guided training system  100  was assessed by sampling the preferences of five upper-extremity amputees as test subjects. Each of the subjects had undergone a targeted muscle reinnervation surgical procedure. Three of the subjects had a shoulder disarticulation prosthesis  102  and two of the subjects had a transhumeral prosthesis  102 . Each of the subjects previously used a prosthesis  102  that included a real-time pattern recognition control algorithm  200 . Moreover, each of the test subjects had experience with conventional screen guided prosthesis training systems that are similar to the above-noted conventional training system. Each of the test subjects participated in at least two clinical sessions where the test subjects trained and calibrated their prostheses  102  using the prosthesis guided training system  100 , including execution of the autoconfiguring pattern recognition training algorithm  300 . More specifically, each of the test subjects performed a repetitive functional task and was allowed to recalibrate their prosthesis  102  by executing the autoconfiguring pattern recognition training algorithm  300 , at their convenience. In some sessions, EMG signal data received by EMG sensors  116  and disruptions were simulated in order to investigate the efficacy of recalibration when executing the autoconfiguring pattern recognition training algorithm  300 . Following these sessions, test subjects provided feedback via a questionnaire, as shown in Tables  1  and  2  of respective  FIGS. 6 and 7 . Table  1  includes test subjects&#39; responses to a questionnaire including Likert items, with 1 being a “Strongly Disagree” value and 5 being a “Strongly Agree” value. Table  2  includes test subjects&#39; fill-in-the-blank responses to questions prompted by investigators. 
     As illustrated by the results shown in Table  1 , the test subjects became comfortable with the prosthesis guided training system  100  and enjoyed executing the autoconfiguring pattern recognition training algorithm  300  to recalibrate and retrain their prostheses  102 . More specifically, the test subjects stated that they would be more likely to use their prostheses  102  if they could train and calibrate it themselves at home. Moreover, the test subjects believed that the prosthesis guided training system  100  was easy to use and was not tiring. Additionally, the test subjects stated that by repeating the same sequence of self-actuations and motions during the device prompting stage  304 , it was easier to complete the autoconfiguring pattern recognition training algorithm  300 . As shown in Table  1 , the test subjects also would have felt comfortable training or executing the autoconfiguring pattern recognition training algorithm  300  in front of others. Additionally, as shown in Table  2 , the test subjects would be willing to regularly execute the autoconfiguring pattern recognition training algorithm  300  multiple times during a day of use to ensure adequate use of their prostheses  102 . 
     Additionally, investigators carried out other experiments to directly compare the prosthesis  102  trained with the prosthesis guided training system  100  relative to the screen guided prosthesis training system. In this experiment, two subjects were used to assess the efficacy of the prosthesis guided training system  100  relative to the conventional system. The first subject has a shoulder disarticulation prosthesis  102  and the second subject had a transhumeral prosthesis  102 . 
     In order to assess the efficacy of the prosthesis guided training system  100  relative to the conventional system, the test subjects trained and calibrated their respective prostheses  102  with both the prosthesis guided training system  100  and the screen guided prosthesis system. After each training and calibration, the test subjects were asked to perform a clothespin placement test to measure real-time controllability of their prostheses  102 . The clothespin placement test includes moving clothespins from a horizontal bar to a vertical bar and requires the use of the elbow, wrist, and hand. The time required to move three clothespins was recorded and the test was repeated until the subjects completed three tests without dropping a clothespin. 
     The test subjects more quickly completed the clothespin placement test when they trained their prostheses  102  with the prosthesis guided training system  100  relative to the conventional system. More specifically, the first subject completed the clothespin placement test in average times of 50.5±10.3 seconds and 37.7±5.4 seconds with the screen guided prosthesis training system and the prosthesis guided training system  100 , respectively. Similarly, the second subject completed the clothespin placement test in average times of 25.5±5.8 seconds and 21.8±2.6 seconds with the screen guided prosthesis training system and the prosthesis guided training system  100 , respectively. 
     It should be understood from the foregoing that, while particular embodiments have been illustrated and described, various modifications can be made thereto without departing from the spirit and scope of the invention as will be apparent to those skilled in the art. Such changes and modifications are within the scope and teachings of this invention as defined in the claims appended hereto.