Electromyographic control systems and methods for coaching of exoprosthetic users

Abstract

To provide systems and methods for the coaching of users through successful calibration of a myoelectric prosthetic controller.SOLUTION: The systems and methods are comprised of, and / or utilize, hardware and software components to input and analyze electromyography (EMG) based signals in association with movements, and to calibrate and output feedback about the signals. The hardware is further comprised of an apparatus for the detection of EMG signals, a prosthesis, an indicator, and a user interface. The software is further comprised of a user interface, a pattern recognition component, a calibration procedure, and a feedback mechanism. The systems and methods facilitate calibration of a myoelectric controller and provides the user with feedback about the calibration including information of the signal inputs and outputs, and messages about connected hardware and how to optimize signal data.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 62 / 807,306, filed February 19, 2019. The entirety of the prior provisional application is incorporated herein by reference.

[0002] The present invention (eg, an electromyographic control system) relates generally to the field of electromyography (EMG) and guiding extracorporeal prosthetic users, and more specifically to guiding these users through the use of an electromyographic control system. [Background technology]

[0003] When a control system for a prosthesis is first set up, the control based on the EMG signals generated by the user needs to be calibrated. Also, during normal day-to-day use of a myoelectric external prosthesis, the control of the prosthesis may deteriorate. This deterioration can occur from many different causes, such as muscle fatigue, moisture, or misalignment of the prosthesis socket. Due to these frequently changing variables, myoelectric prosthesis users need a way to regain accurate control of their device. Traditionally, when a myoelectric prosthesis user needs to initially set up or recalibrate their system, it involves a trip to a prosthetist and an in-person appointment, which can take hours to verify that the system is responding correctly to their EMG signals. This is not always possible and is very inconvenient for the user.

[0004] For the foregoing reasons, there is a need for an electromyographic control system and method for instructing an extracorporeal prosthesis user. Summary of the Invention

[0005] The need for the disclosed invention arises from the need to have a method to quickly guide a user for successful calibration of a myoelectric prosthetic controller. The embodiments described herein describe an electromyographic control system and method that allows a myoelectric prosthetic user to quickly recalibrate their myoelectric prosthetic controller on the spot without having to go to a specialist. In addition, the electromyographic control system and method described herein allows the user to receive feedback regarding their calibration, thereby allowing the user to have the most accurate control of their device. The electromyographic control system and method of the present disclosure can be utilized in these and other embodiments described herein, such that the electromyographic control system and method guides the user to quickly and accurately (as well as initially) calibrate and recalibrate a myoelectric prosthetic controller and / or myoelectric prosthetic control system. In various embodiments described herein, during a given calibration session, the electromyographic control system and method may guide the user through the process of calibrating EMG signals generated from the action of their own muscles for use with the myoelectric prosthetic control system, and then provide feedback regarding the information gathered from the calibration session. Calibrating the EMG signals generated from the user's muscle movements includes one or more of changing, modifying, adding, deleting, or enhancing specific movements to improve the quality of the signal received by the myoelectric prosthesis control system. Calibrating the EMG signals generated from the user's muscle movements may also include the prosthesis control system using pattern recognition capabilities to recognize characteristics in the user's EMG signals that determine when the user is performing a specific movement. In various embodiments, based on the quality of the information received from the calibration session, the user may receive feedback that tells the user that he or she has been successfully calibrated in all respects or, additionally or alternatively, that he or she has adjusted certain parameters and recalibrated. In some embodiments, the entire calibration procedure using the disclosed electromyographic control system and method may take less than five minutes and can be performed multiple times a day, providing greater advantage to the prosthesis user.

[0006] Representative embodiments of the present invention provide many advantages over commonly used methods for calibrating myoelectric prosthetic control systems. A general objective of the present invention is to provide a method for simple and rapid recalibration and training of myoelectric prosthetic control systems. In various embodiments, many of the novel features described result in a new method for quickly guiding a user through a successful calibration procedure for their myoelectric control system.

[0007] To accomplish this, the present invention generally comprises a system for guiding a user through the setup and calibration of a myoelectric prosthetic control system. In some embodiments, the disclosed system may further comprise a system for input of signal data from a plurality of electrodes. In further embodiments, the disclosed system may also comprise a button including a housing, an indicator, and a tactile interface. In addition, the disclosed system may also comprise a software component including a user interface and a pattern recognition component.

[0008] In various embodiments, the disclosed system may collect signal data from multiple electrode inputs. These electrodes may be made from conductive materials. The multiple electrodes may be coupled to software components in a manner that allows for communication of the signal data.

[0009] In a further embodiment, an indicator on a button that may comprise part of the system may send an indication to the user. The indication may be displayed as a visual, auditory, and / or tactile stimulus. The tactile interface of the button allows the user to initiate a calibration procedure, for example, when a component is manually pressed by the user to initiate a calibration procedure or when an accelerometer is activated by the user to initiate a calibration procedure.

[0010] In various embodiments, the software components of the system may include a user interface (UI) that is used to provide informational feedback to the user. This informational feedback may comprise quality requirement criteria used in identifying an objective level of calibration quality, and / or a message identifying one or more possible causes of insufficient signal data. The message may include data used as an indication of the cause of the non-optimal signal data input and a recommended procedure for optimizing the signal data input. The user interface may also include information about the calibration procedure and a set of instructions to guide the user through the calibration procedure. The user may also utilize the user interface to monitor the signal data in real time. The user interface allows the user to select various combinations of prostheses, select various operations supported by the prostheses, and provide an indication of the signal data input and output. Along with allowing the user to select various prostheses and operations, the user interface provides the user with an identification of connected hardware and the status of the connected hardware. In some embodiments, the user interface is displayed as a virtual application. The application may be included on a smartphone or computer and is installed using content from the Internet or uploadable content from a physical disk or drive. The installation method is compatible with most digital operating systems. The software component also includes a method for initiating a calibration procedure from the virtual application.

[0011] In various embodiments, the software components for the system may include a pattern recognition component. The pattern recognition component may be used to receive, analyze, and output the signal data. In some embodiments, the pattern recognition component also includes an adaptive machine learning system to recognize the user-specific signal data. This adaptive machine learning system recognizes the user-specific signal data and references it to a specific action performed by the user. It gives the signal data and the action an identification and classifies this information. In some embodiments, the pattern recognition component may be coupled to a user interface to communicate the classified signal data to the user. In some embodiments, the adaptive machine learning system classifies sections of the user experience data to determine if a particular action requires more calibration events compared to others. In embodiments, the machine learning system or component may prompt or initiate new solutions or request new actions for or by the user to improve the calibration quality of a particular action.

[0012] In accordance with the above, and by virtue of the disclosure herein, the present disclosure includes improvements in computer functionality or improvements in other technologies, since at least the present disclosure describes that prosthetic devices and their associated various components may be improved or enhanced with the disclosed electromyographic control systems and methods that provide the respective users with calibration and more accurate control of the prosthetic device. That is, the present disclosure describes improvements in the functionality of the prosthetic device itself or "any other technology or technical field" (e.g., the field of electromyography) since the disclosed electromyographic control systems and methods improve and enhance the operation of the prosthetic device by introducing a user recalibration procedure to correct non-optimal EMG signal data, reduce error rates, and eliminate errors and malfunctions typically experienced over time by prosthetic devices lacking such systems and methods. This is an improvement over the prior art, at least because such previous systems were prone to errors due to lacking the ability to quickly guide a user through a successful calibration procedure of the user's myoelectric control system.

[0013] Additionally, the present disclosure includes the application of various features and functions as described herein in conjunction with or through the use of specific machines, such as myoelectric prosthetic controllers, prosthetic devices, buttons, and / or other hardware components as described herein.

[0014] Additionally, the present disclosure includes transforming or reducing a particular item to a different state or thing, for example, transforming or reducing the calibration and / or operation of a prosthetic device from a non-optimal or erroneous state to an optimal or calibrated state.

[0015] Further, the present disclosure includes the addition of certain features outside of routine, conventional activities well understood in the art, or non-conventional steps that, in various embodiments, demonstrate certain useful applications, such as electromyographic control systems and methods for coaching extracorporeal prosthesis users.

[0016] The advantages will become more apparent to those skilled in the art from the following description of preferred embodiments shown and described by way of example. As will be understood, the present embodiments are capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. [Brief description of the drawings]

[0017] The objects, features, and advantages of the present invention and additional embodiments will be more readily understood with reference to the following disclosure when considered in conjunction with the accompanying drawings, in which like reference numbers are used to identify the same components in the various figures.

[0018] The figures described below show various aspects of the systems and methods disclosed herein. It should be understood that each figure shows an embodiment of a particular aspect of the disclosed systems and methods, and each of the figures is intended to correspond to a possible embodiment thereof. Furthermore, wherever possible, the following description will refer to reference numerals contained in the following figures, and features shown in multiple figures will be designated with consistent reference numerals.

[0019] Although the currently considered arrangements are shown in the drawings, it being understood that the present embodiments are not limited to the precise arrangements and instrumentalities shown.

[0020] [Figure 1] 1 shows an exemplary flow chart illustrating the use and operation of an EMG control system to guide a user through a calibration procedure and feedback of EMG signals, according to various embodiments disclosed herein. [Diagram 2] 13A-13C illustrate example representations of virtual user interfaces demonstrating electrode connectivity, calibration quality, and virtual arm and / or prosthetic mode switching, according to various embodiments disclosed herein. [Diagram 3] 10 illustrates an example representation of a virtual user interface showing the system of FIG. 1 for each connected device, calibrated strengths for multiple movements, and instruction of an extracorporeal prosthesis user, including messages and training advice, in accordance with various embodiments disclosed herein. [Figure 4A] 1 with a prosthetic wrist and hand equipped with electrodes, and two separate modalities for accessing the virtual user interface(s) of either FIG. 2 and / or FIG. 3, in accordance with various embodiments disclosed herein. [Figure 4B] 4B illustrates a further exemplary representation of the user of FIG. 4A with a prosthetic wrist and hand with electrodes attached to the user, according to various embodiments disclosed herein. [Figure 5A]2 illustrates an example representation of signal data received from a calibration by a user and analyzed by the system of FIG. 1 for guidance of an extracorporeal prosthesis user, according to various embodiments disclosed herein. [Figure 5B] 5B illustrates a further exemplary representation of the signal data of FIG. 5A, including a classification of the signal data according to index behavior as illustrated by FIG. 5A. [Figure 6A] 4B shows a representation of an electronic button, including a housing, an indicator, and a tactile interface, of the system of FIG. 1 and FIG. 4A according to various embodiments disclosed herein. [Figure 6B] 6A shown in conjunction with the system of FIGS. 1 and 4A according to various embodiments disclosed herein.

[0021] These figures depict preferred embodiments for purposes of illustration only. Alternative embodiments of the systems and methods illustrated herein may be employed without departing from the inventive principles described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] While the present invention is capable of embodiment in many different forms, certain exemplary embodiments thereof are shown in the drawings and described in detail herein, with the understanding that the present disclosure should be considered as an example of the principles of the invention and is not intended to limit the invention to the specific embodiments illustrated. In this regard, before describing at least one embodiment consistent with the present invention in detail, it should be understood that the invention is not limited in its application to the details of construction and the arrangement of components described above and below as shown in the drawings or described in the examples. Methods and apparatus consistent with the present invention are capable of other embodiments and can be practiced and carried out in various ways. It should also be understood that the phraseology and terminology employed in this specification, as well as the summary included below, are for the purpose of description and should not be considered limiting.

[0023] As disclosed in various embodiments herein, a system 100 is described for coaching an extracorporeal prosthesis user. The system 100 may also be referred to herein as a "system" and / or an "electromyography control system." The system 100 generally comprises hardware and software components for inputting and analyzing EMG-based signals associated with a user's (e.g., user 123) movements, and for calibrating and outputting feedback about the signals. The system 100, and portions thereof including its various hardware and software components, are illustrated herein by the figures provided.

[0024] For example, FIG. 4A shows an exemplary representation of a user (e.g., user 123) with a prosthetic wrist and hand (e.g., prosthetic device 124) equipped with electrodes 122, and two separate modalities (e.g., computer 125a of FIG. 2 and mobile device 126a of FIG. 3 (not shown)) for accessing virtual user interface(s) 125 and 126 of either FIG. 2 and / or FIG. 3, in accordance with various embodiments disclosed herein.

[0025] In addition, FIG. 4B shows a further exemplary representation of the user of FIG. 4A with a prosthetic wrist and hand (e.g., prosthetic device 124) with electrodes 122 attached to the user 123, according to various embodiments disclosed herein.

[0026] As shown by at least Figures 4A, 4B, and 6B, the system 100 comprises a plurality of electrodes 122, a button 128, and a software component 136 coupled to the prosthetic device 124. For example, the system 100 may be communicatively coupled to the prosthetic device 124 via wired or wireless hardware and / or software components (e.g., via a connectivity module 132, a mobile application-based UI 126, a mobile device 126a, etc.). For example, as shown in Figure 6B, the connectivity module 132 may include a hardware component 132c represented as a wireless communication component 132c, such as a USB-based wireless transmitter that communicates via BLUETOOTH, WIFI, or other wireless-based communication standards. The prosthetic device 124 is coupled to the system 100, where signal data 127, such as shown herein with respect to Figures 5A and 5B, may be collected from the plurality of electrodes 122. In various embodiments, the electrodes 122 may be configured as EMG electrodes. For example, as shown in FIG. 1 (118) and FIG. 5A (117, 118), the signal data 127 may indicate, define, or classify the signal data (e.g., EMG signal data of a user's movements over time) as any one or more of "inconsistent," "liftoff," "noisy," "quiet," "indistinct," "similar," "early," "late," "weak," or "strong," etc. For example, FIG. 5A shows an exemplary representation of the signal data 127 received from calibration by the user 123 and analyzed by the system 100 for extracorporeal prosthesis user guidance. The multiple electrodes 122 may collect the signal data 127 and forward it to the software component 136.

[0027] 1 shows an exemplary flow chart depicting the use and operation of the system 100 for guiding a user through a calibration procedure and feedback of an electromyographic signal. As shown in FIG. 1, the button 128 may be implemented as a button user interface 103. Additionally, as shown in FIG. 6A, the button 128 may include an indicator 129 (e.g., an LED or visual indicator) where the user 123 may be informed regarding the quality of the signal data 127 through an auditory, tactile, or visual stimulus. The button 128 may be coupled to the prosthetic device 124 for interaction by the user 123.

[0028] FIG. 6B illustrates the button of FIG. 6A shown with the system 100, and various components including the electrodes 122, the connector to the prosthetic device 124, the button 128, the button housing 131, and the connection module 132 (e.g., the embodiment of FIG. 6B is shown with various hardware components 132a, 132b, and 132c). In various embodiments, a myoelectric prosthetic controller that may be configured or calibrated to control the prosthetic device 124 may be included in the hardware components 132a and / or 132b. In some embodiments, the button 128 may include a haptic interface 130 that allows a user 123 to interact with the system 100 and control (110) the calibration of the prosthetic device 124 as described herein with respect to FIG. 1. For example, FIG. 1 illustrates a user calibration 110 of the system 100 in which a user (e.g., the user 123) calibrates the system 100 for EMG control of a prosthetic device (e.g., the prosthetic device 124). The haptic interface 130 is coupled to an input / output connection module 132 through which information may be communicated between the buttons 128 and software components 136. In some embodiments, the components of the buttons 128 may be housed within a housing 131 (e.g., a button housing) of a substantially rigid material.

[0029] The software component 136 may comprise a user interface (e.g., a computer-based user interface 125 as shown by FIG. 2 and / or a mobile application-based user interface 126 as shown by FIG. 3) and an analysis (111) pattern recognition algorithm, as shown and described by FIGS. 1-3, 5A, and 5B. In various embodiments, the user interface (e.g., a computer-based UI 125 and / or a mobile application-based UI 126) is accessed by initiating (101) a process for use of the prosthetic device 124. As shown in FIG. 1, initiation (101) of use of an electromyography control system (system 100) for guidance of an extracorporeal prosthetic user is shown. For example, in various embodiments, the user 123 must first decide whether to initiate a calibration (e.g., via a reset calibration procedure (104) or a calibration protocol (109)) through a button user interface (103) or a virtual user interface (102).

[0030] Once the interface method is selected, the user 123 must decide whether to reset (104) the calibration data of the prosthetic device 124, fully calibrate (105) the device (e.g., the prosthetic device 124), or calibrate (106) a single portion of the device (e.g., the prosthetic device 124). As shown in FIG. 1, the user 123 may initiate a reset of the user's calibration data (104) via a button user interface 103. In some embodiments, a reset of the user's calibration data (104) via the button user interface 103 may follow an end of a session (108) or use of the EMG control system (system 100). Additionally or alternatively, the user 123 may initiate a full calibration of the user's signal data (105) via either the button user interface (103) or the virtual user interface (102). Further, additionally or alternatively, the user 123 may initiate a single run calibration (106) of the user's signal data via the virtual user interface (102). To guide the user 123, the user 123 may then determine whether to be guided (107) by the prosthetic device 124 (e.g., an extracorporeal prosthetic device), the virtual user interfaces 125, 126, or both the prosthetic device 124 and the virtual user interfaces 125, 126. For example, as shown in FIG. 2, the user 123 may select a guidance method (107) for guiding the user through the extracorporeal prosthetic calibration via prosthetic arm guided training, virtual arm guided training, or both prosthetic and virtual arm guided training.

[0031] In addition, FIG. 2 illustrates an exemplary representation of a virtual user interface 102 that indicates electrode connectivity (e.g., via user interface indicator(s) 119 indicating that an electrode 122 is connected to the system 100), calibration quality (e.g., via data quality requirements 114), and virtual arm and / or prosthetic mode switching (e.g., via leads 107). In some embodiments, the virtual user interface 102 may include user interface indicator(s) 119 that may indicate whether a particular electrode 122 is connected to the system 100. Then, as illustrated in FIG. 2, if an electrode is not connected, the virtual user interface 102 may display a user interface indicator 120 regarding the status of the connected electrode 122 (e.g., "no signal contact").

[0032] Once the user 123 selects a method of lead 107, the EMG control system (system 100) begins or starts a calibration protocol 109. The user 123 may then go through a selected series of motions, i.e., calibration classes such as index motions 134 including, for example, any one or more of elbow motions ("bend" and / or "extend"), wrist motions ("pronation" and / or "supination"), and / or hand motions ("open", "close", "use a tool", "keystroke", and / or "pinch") as shown in FIG. 3 and prompted by the calibration protocol 109 via the selected method of lead 107 that generates signal data 127 and calibrates the system 100. For example, in various embodiments, the system 100 (e.g., by the software component 136 and / or by the pattern recognition component) may analyze (111) the signal data 127 (e.g., EMG data) in relation to the prosthetic device 124 (e.g., an extracorporeal prosthetic device) and each prompted motion from the calibration protocol 109 (e.g., elbow motion ("bend" and / or "extend"), wrist motion ("pronation" and / or "supination"), and / or hand motion ("open", "close", "use tool", "keystroke", and / or "pinch"), as shown in FIG. 3 ). The system 100 may then classify the signal data 127 and prepare it for the user 123 as meaningful feedback (112) on the calibration quality. In this manner, the system 100 provides the signal data feedback (112).

[0033] 1, the signal data may be analyzed (111) by the system 100 (e.g., by the software component 136 and / or by the pattern recognition component) to determine its use and quality. If the signal data 127 is determined (113) by the system 100 (e.g., by the software component 136 and / or by the pattern recognition component) to be appropriate for use with the prosthesis 124 (e.g., useful and of high quality as illustrated by any of FIGS. 1, 2, 3, 5A and / or 5B), no message may be provided (115) to the user 123 and the calibration of the extracorporeal prosthesis system may end (108), which may mark the end of the current use or session of the electromyography control system (system 100). That is, the system 100 (e.g., by the software component 136 and / or by the pattern recognition component) determines that the signal data is appropriate and does not provide a message to the user (115). 2, the system 100 may provide the user with data quality requirement criteria 114 (e.g., a “fair” quality requirement criteria and / or a three-star rating) to indicate suitable signal data 127. As a further example, as shown in FIG. 3, the system 100 may provide the user with data quality requirement criteria 114 (e.g., any of a high (e.g., five) star rating for any of the indicator actions 134 such as “bend”, “extend”, “pronate”, “supinate”, “close”, “use tool”, “keystroke”, “pinch”, etc.) to indicate suitable signal data 127.

[0034] However, if the signal data 127 is determined (116) to be inadequate (e.g., by the software component 136 and / or the analytical pattern recognition algorithm) (e.g., inadequate calibration as determined by any of FIGS. 1, 2, 3, 5A, and / or 5B), the user 123 may be provided with an indication of inadequate calibration (116) through either the button user interface 103 or the virtual user interface 102. That is, the system 100 may determine (116) that the signal data 127 is inadequate (e.g., by the software component 136 and / or by the pattern recognition component) and may provide an indication or message of inadequate calibration to the user. For example, as shown in FIG. 1, the system 100 may provide the user with light, sound, or tactical feedback indicative of inadequate / insufficient calibration via the button user interface 103 / button 128. As a further example, as shown in FIG. 3, the system 100 may provide the user, via the virtual user interface 102, with data quality requirement criteria 114 (e.g., three-star quality requirement criteria for the signal data 127 collected for the “open” indicator action) and, in some cases, indicate that the captured signal data 127 was inadequate (116).

[0035] In various embodiments, the signal data 127 may be determined to be inappropriate by the system 100 (e.g., by the software component 136 and / or by the pattern recognition component) because it is defined or classified as, for example, “noisy,” “quiet,” or “inconsistent,” etc., over a given period of time, as shown in FIG. 5A (117, 118) and FIG. 1 (118). For example, any one or more of the inappropriate or suboptimal signal data 127 (e.g., “noisy,” “quiet,” or “inconsistent,” etc., as shown in FIG. 5A or FIG. 1 (118)) may be due to insufficient electrode skin contact (or other contact) of the electrode 122 with the user 123 causing inappropriate or poor signal quality, and thus inappropriate signal data 127 and improper calibration. For example, as shown in FIG. 3, a significant loss of electrode skin contact may be detected by the system 100, and the system 100 may rely on consistent electrode skin contact for optimal performance.

[0036] In either case (i.e., in the case of proper calibration or improper calibration), as illustrated by any of FIGS. 1, 2, and / or 3, the user 123 is provided with quality requirement criteria 114 based on the collected signal data 127. For example, FIG. 5B illustrates an exemplary representation of the signal data 127 of FIG. 5A, including classification of the EMG signal data by the exemplary index operation 134 of FIG. 5A. For example, as illustrated in FIG. 5B, the software component 136 of the system 100 may include a pattern recognition component. The pattern recognition component may be used to receive, analyze, and output the signal data 127. In some embodiments, the pattern recognition component may also include an adaptive machine learning system or model for recognizing the unique signal data 127 of the user. The adaptive machine learning system may recognize the unique signal data 127 of the user (e.g., user 123) and reference it to a particular operation (e.g., a calibration class) performed by the user (e.g., either "open", "pronate", "close", and / or "supinate" as illustrated in FIGS. 5A and 5B). The pattern recognition component may provide identification to the signal data 127 and associated actions (e.g., index actions 134) and classify or group (127a) this information across values ​​of unique signal data 127 specific to a given user, for example, as represented by Figure 5B. The pattern recognition component may be coupled to a user interface (e.g., 125 or 126) for communication of the classified signal data 127 to the user 123 via one or more messages.

[0037] In various embodiments, the pattern recognition component (or, alternatively and more generally, the electromyography software components described herein) are configured to determine statistics from the EMG signal data during calibration to provide the recommendations described herein. For example, based on a statistical analysis of the EMG signal data (e.g., signal data 127), common user errors (e.g., “lift-off”) are identified and solutions are proposed. The statistics determined and analyzed by the pattern recognition component may include time domain feature covariance, signal magnitude, change in signal magnitude over time, separation index between motion classes, frequency of electrode lift-off, and various others. Additionally or alternatively, fuzzy logic may be applied to these statistics to indicate when a particular, yet widely observed error is more likely to be caused, for example, by user error and / or user contact with the electrodes 122. In such an embodiment, fuzzy logic may be applied to each statistic. Additionally or alternatively, statistics generated from the EMG signal data may be converted to values ​​compatible with fuzzy logic by assigning a probabilistic value indicating the confidence that the statistic is outside of an expected range. These values ​​(e.g., as described for FIG. 5B) may be analyzed by a pattern recognition component for certain combinations indicative of a general problem (e.g., "lift-off"). For example, such problems may include signal clarity problems, problems indicative of muscle contractions during recording, excessive movement, and poor electrode connection, timing problems during calibration, noise problems, and signal similarity problems, as described and illustrated herein for FIG. 1 (118), FIG. 5A (117, 118), and / or Tables 1-6. Each problem may correspond to a calibration class that is assigned, together or individually, a rating (e.g., data quality requirements 114) that is displayed to the user, as described. In general, the pattern recognition component analyzes the severity for each general problem and the confidence that a given problem is the root cause of insufficient control in order to select a message type, or messages, to send to the user.As described herein, such messages include assessments (e.g., data quality requirements 114) indicating expected performance, as well as instructional messages (e.g., message 117) indicating what was detected and what can be done to correct errors in subsequent calibrations.

[0038] Analysis of the user's EMG signal data 127 by the pattern recognition component (or, alternatively, and more generally, the electromyography software components described herein) may be further described with respect to Tables 1-5 below, which describe signal data collection, derivation functions (statistics), fuzzy logic scaling, fuzzy logic implementation, and evaluation and problem selection.

[0039] Data collection The pattern recognition component (or, alternatively and more generally, the electromyography software components described herein) require input EMG signal data 127 from a calibration session task to analyze, for example, when implementing a calibration quality algorithm. Such data may be used to build a classifier for the pattern recognition component, while other data may be collected for implementation of the calibration quality algorithm. Additionally or alternatively, in the case of a single-class calibration (e.g., only one class is calibrated), some data is only needed from the relevant class, while other data is needed from all classes anyway. Table 1 below provides examples of data or data types (e.g., EMG signal data 127) that may be collected, determined, or otherwise received as described herein. It should be understood that the data or data types in the following table are provided by way of example only and are not limiting. Additional or different data, sizes, etc. may be utilized consistently with the disclosure herein. [Table 1]

[0040] In Table 1 above, K is the total number of classes recognized by the classifier (e.g., typically, K=2-20), k is the number of classes used for calibration (e.g., typically, 1 or K), f is the number of measurement data features per channel (e.g., 7 standard), c is the number of channels that may be associated with the electrodes 122 and / or EMG signal data 127 received via the electrodes 122 (e.g., 8 channels), and B is the number of bytes (in memory, as described herein), each value may be either a float or unit 32 (e.g., 4 bytes each). As shown in the "Size" column above, the pattern recognition component (or, alternatively and generally, the EMG software components described herein), when implementing, for example, a calibration quality algorithm, requires a minimal amount of memory (e.g., a few bytes of information in some cases), allowing the pattern recognition component (or, alternatively and more generally, the EMG software components described herein) to be implemented on devices with minimal memory and / or processing resources. Each of this data may be collected during a calibration session ad as described herein.

[0041] Derived functions (statistics) With respect to an additional portion of the calibration quality algorithm, a pattern recognition component (or, alternatively, and more generally, an electromyography software component as described herein) can process the EMG signal data 127 to generate derived data features, including statistics for use by, for example, implementations of fuzzy logic as described herein. [Table 2]

[0042] In Table 2 above, similar to Table 1, K is the total number of classes recognized by the classifier (e.g., usually K = 2 - 20), k is the number of classes used for calibration (e.g., usually 1 or K), k_m is the number of operating classes (e.g., classes for detecting operations, e.g., ("open", "close", "use tool", "type key", and / or "pinch") as shown in FIG. 3 in calibration (usually 0, 1, or K - 1), B is the number of bytes (in memory as described herein), and each value can be either a float or a unit 32 (e.g., 4 bytes each).

[0043] As described for Table 2 (item 7), the separation index can be calculated by the pattern recognition component (or, alternatively, and more generally, the electromyogram software component described herein) using the following formula.

Equation

[0044] Fuzzy logic transformation Regarding the additional part of the calibration quality algorithm, the pattern recognition component (or, alternatively, and more generally, the electromyogram software component described herein) can conceal the EMG signal data 127 including any one or more of the data in Table I and / or the feature data in Table II. The transformation prepares data for the fuzzy logic implementation(s) as further described herein. Generally, fuzzy logic depends on all variables including 0 - 1 and can heuristically represent the likelihood, confidence, or severity to which a given feature applies. In some cases, two features with opposite polarities can be represented by one variable from - 1 to 1, where 0 represents that neither feature applies. The transformation can be implemented by a piecewise linear function including, for example, the following functions. When x < a, f(x) = 0 Or when x > b, 1 Or when a < x < b,

number

[0045] In the above function, a is the lower bound and b is the upper bound. Table 3 below represents fuzzy functions that may be used by the calibration quality algorithm of the pattern recognition component. [Table 3]

[0046] In Table 3 above, similar to Tables 1 and 2, K is the total number of classes recognized by the classifier (e.g., typically K=2-20), k is the number of classes used for calibration (e.g., typically 1 or K), k_m is the number of action classes (e.g., classes for detecting actions, e.g., ("open", "close", "use tool", "keystroke", and / or "pinch") as shown in FIG. 3 in the calibration (typically 0, 1, or K-1), s is the separation exponent scaling factor (item 8 in Table 2), B is the number of bytes (in memory, as described herein), and each value can be either a float or unit 32 (e.g., 4 bytes each).

[0047] Fuzzy logic implementation With respect to an additional portion of the calibration quality algorithm, the pattern recognition component (or, alternatively and more generally, the EMG software component described herein) may determine common problems (e.g., "too weak") prevalent in the data signal by implementing fuzzy logic operations on the fuzzy functions of Table 3. The pattern recognition component (or, alternatively and more generally, the EMG software component described herein) may implement the following fuzzy logic operations across the fuzzy functions of Table 3: NOT(X)=1-X(1) AND(X, Y)=X*Y(2) OR(X, Y)=X+YX*Y(3)

[0048] The implementation of these fuzzy logic operations results in the following fuzzy function results and associated conclusions (for a given indicator actuation): These fuzzy function results and associated conclusions generally correspond to the classifications in Figure 1 (118) and Figure 5A (117, 118) described herein. [Table 4]

[0049] Assessment and question selection With respect to additional portions of the calibration quality algorithm, the pattern recognition component (or, alternatively and more generally, the EMG software component described herein) may select what is to be displayed. For each class, a signal is sent from the pattern recognition component (or, alternatively and more generally, the EMG software component described herein) to the software component 136, which may render or display a rating (from a value or rating 1-5) (e.g., data quality requirement criteria 114)) and / or one or more messages, results, on a user interface (e.g., a computer-based user interface 125 as shown by FIG. 2 and / or a mobile application-based user interface 126 as shown by FIG. 3) as described herein.

[0050] As noted above, each problem may be identified via a fuzzy logic implementation, however, in some embodiments, some problems or classes may be determined or set to be more severe than others. In such embodiments, the pattern recognition component may multiply the problem or class by a scaling factor to determine its final priority and maximum evaluation penalty. Examples of scaling factors and penalty scaling are shown in Table 5 below. The fuzzy functions and terms in Table 5 correspond to those in Table 4, where the scaling factors and / or penalty scaling are applied to the output values ​​of the fuzzy functions in Table 4. It should be understood that additional and / or different such scaling factors and penalty scaling may be used. [Table 5]

[0051] Generally, each message (e.g., as described in Table 6 herein and in Figures 1 (118) and 5A (117 and 118)) has its own rating and problem threshold. If the value of the fuzzy function described above for Tables 5 and 6 (determined with or without priority and / or penalty scaling) exceeds the rating threshold and the problem threshold of the message, the message is displayed by software component 136 as described herein, which may render or display the results (rating (value or rating from 1-5) (e.g., data quality requirement criteria 114)) and / or one or more messages) on a user interface (e.g., computer-based user interface 125 as shown by Figure 2 and / or mobile application-based user interface 126 as shown by Figure 3).

[0052] Each of the fuzzy functions in Tables 4 and 5 generally correspond to the classifications of FIG. 1 (118) and FIG. 5A (117, 118) described herein, and the signal data 127 analyzed as described with respect to Tables 1-5 may ultimately indicate, define, or classify the signal data (e.g., EMG signal data of a user's movement over time) as any one or more of "inconsistent," "lift-off," "noisy," "quiet," "unclear," "similar," "fast," "slow," "weak," or "strong," etc.

[0053] In various embodiments, if the user 123 receives an indication of insufficient calibration (116), a message 118 is generated to the user 123 based on the analyzed and classified signal data 127. For example, the system 100 may provide the user 123 with one or more messages (117) related to the signal data 127 based on the signal data 127. The messages 118 are a list of one or more possible message classes that would be provided to the user 123 after the system 100 determines that the collected signal data 127 is inadequate (116). As shown by the embodiment of FIG. 1, the messages 118 may be one or more of the following messages 118: "mismatch," "lift off," "noisy," "quiet," "unclear," "similar," "early," "slow," "weak," "strong," and / or "class off" (not shown). Each message 118 may be generated based on an analysis of the signal data 127 and provided as feedback (112) to the user 123 through the virtual user interface 102. For example, FIG. 3 illustrates an exemplary representation of a virtual user interface 102 showing the calibrated strengths of a number of actions (e.g., instructed actions 134, i.e., prompted actions) of the system 100 for coaching an extracorporeal prosthetic user, including messages and training advice, for each connected prosthetic device (121), according to various embodiments disclosed herein. For example, FIG. 3 illustrates user interface indicators for each connected prosthetic device (121) in the system 100, including, for the embodiment of FIG. 3, each of the prosthetic devices, e.g., the "elbow," "wrist," and "hand" of the prosthetic device 124. EMG signal data for the calibration may be measured during a period or calibration session indicated by an orange (or other color) circle(s) on the virtual user interface 102, and the user is requested to perform the indicated action / prompted behavior on the virtual user interface 102 during the period in which the orange circle appears or otherwise completes its animation.

[0054] The virtual user interface 102 may present the signal data 127 as data quality requirements 114, and a message 118 may be provided to the user 123 along with recommended steps for optimization (133). For example, as shown in Figure 3, message 118 (117) may include a "Lift-off message" indicating that "significant loss of electrode skin contact has been detected." The lift-off message corresponding to message 118 of Figure 1 (as well as associated issues, explanations, recommendations, and advice), as well as additional sample messages, are shown in Table 6 below. [Table 6-1] [Table 6-2] [Table 6-3]

[0055] In Table 6 above, various indicator actions / prompted actions (e.g., as shown by FIG. 3, elbow actions ("bend" and / or "extend"), wrist actions ("pronate" and / or "supinate"), and / or hand actions ("open", "close", "use a tool", "keystroke", and / or "pinch")) are provided to or otherwise represented by "[X]", "[Y]", and / or "[Z]", and the various indicator actions / prompted actions may be written into a message and presented to the user (via "[X]", "[Y]", and / or "[Z]" as placeholder locations). In addition, each message type (calibration problem class type) shown in Table 6 above may be associated with a data quality requirement criterion 114 penalty, and the data quality requirement criterion 114 may be decreased or otherwise changed or updated when the message type (calibration problem class type) is detected. For example, detection of a TOO_WEAK message type (calibration problem class type) may cause the loss of up to four data quality requirement criteria 114, e.g., the loss of a corresponding up to four star rating displayed in a user interface (e.g., 125 or 126) as described herein. As another example, detection of a MISSING_LATE message type (calibration problem class type) may cause the loss of up to two data quality requirement criteria 114, e.g., the loss of a corresponding up to two star rating displayed in a user interface (e.g., 125 or 126) as described herein. It should be appreciated that changes to the data quality requirement criteria 114 / star rating may be adjusted or otherwise set based on the severity or otherwise impact that a user's insufficient calibration session, as indicated by the corresponding message type (calibration issue class type), will cause the collection of EMG data for calibration, and / or the resulting calibration of the prosthetic device, e.g., the prosthetic device 124 itself.

[0056] The message 118 may further include a recommended procedure 133 for optimization of the signal data 127. A given recommended procedure 133 for optimization corresponds to each connected prosthetic device (121) connected to the system and its associated indicator action 134. For example, as shown in FIG. 3, since the "open" action of the "hand" device has the lowest signal data 127 quality or quality requirement 114 (e.g., a three-star quality requirement), the recommended procedure for optimization is to calibrate the "hand" prosthetic device (121) (e.g., of the prosthetic device 124) and its associated "open" indicator action 134. Furthermore, as shown in FIG. 3, the recommended procedure 133 for optimization may be an advisory or cause an advisory to be generated based on the message class. For example, as shown in FIG. 3, the message class is "lift off" and an advisory to the user 123 is generated and / or displayed, such as "good control performance is not expected until the electrode skin contact issue is resolved...".

[0057] 1, the system 100 may then terminate (108) the calibration protocol 109. The recommended procedures for optimization 133 may be recorded in a system memory 135 (e.g., a system memory of or communicatively coupled to a computing or electronic device such as the prosthetic device 124, the computer 125a, the mobile device 126a, the connectivity module 132, the myoelectric prosthesis controller, or otherwise the system 100) so that the user 123 can later access the recommended procedures for optimization 133 and review their collected signal data 127 in relation to a given prosthesis or index operation 134 as shown and described herein and / or as illustrated by any of the figures herein.

[0058] Aspects of the Disclosure 1. A system for instructing an extracorporeal prosthesis user, the system comprising a device for collecting signal data, a button, and a software component.

[0059] 2. A system for inputting signal data comprising a plurality of electrodes.

[0060] 3. A button comprising a housing, an indicator, and a tactile interface.

[0061] 4. A software component comprising a user interface and a pattern recognition component.

[0062] 5. The system for inputting signal data according to embodiment 2, wherein the plurality of electrodes comprises a composition of conductive material.

[0063] 6. A system for inputting signal data as described in aspect 2, further comprising a method for communicating the signal data to the software component as described in aspect 4.

[0064] 7. A button indicator as described in aspect 3, wherein the indicator comprises a device for visual stimulation, a device for auditory stimulation, and / or a device for tactile stimulation.

[0065] 8. The software component of embodiment 4, wherein the user interface further comprises a system for feedback of information to the user.

[0066] 9. The user interface of embodiment 8, wherein the system for informational feedback to the user further comprises quality requirement criteria identifying an objective level of calibration quality, and / or messages identifying possible causes of insufficient signal data.

[0067] 10. The system for informational feedback to a user of embodiment 9, wherein the message further comprises an indication of the cause of the non-optimal signal data input and recommended steps for optimizing the signal data input.

[0068] 11. The software component of aspect 4, wherein the user interface further comprises a virtual application that can be interacted with by the user.

[0069] 12. The software component of aspect 4, wherein the user interface further comprises a calibration procedure and a set of instructions that guide a user through the calibration procedure.

[0070] 13. The software component of aspect 4, wherein the user interface further comprises a selection of a prosthesis connection, a selection of one or more operations, an indication of a signal data input, or an indication of a signal data output.

[0071] 14. The user interface of aspect 13, wherein the indication of the signal data input further comprises an identification of the connected hardware and an identification of the status of the connected hardware.

[0072] 15. The software component of aspect 4, wherein the user interface further comprises a system for a user to directly monitor the signal data in real time.

[0073] 16. The software component of embodiment 4, wherein the pattern recognition component further comprises a system for receiving signal data, a system for analyzing the signal data, and a system for outputting the signal data.

[0074] 17. The software component of aspect 4, wherein the pattern recognition component further comprises an adaptive machine learning system for recognizing user-specific signal data.

[0075] 18. The pattern recognition component of aspect 17, wherein the adaptive machine learning system further comprises a system for recognizing user-specific signal data that references a particular action by a user.

[0076] 19. The software component of aspect 4, wherein the pattern recognition component further comprises a system for identifying and classifying signal data from a user.

[0077] 20. The software component of embodiment 4, wherein the pattern recognition component further comprises a system for communicating the classified signal data to a user.

[0078] 21. The user interface of aspect 11, wherein the virtual application further includes an installation method with content downloaded from the Internet, an installation method with content uploadable from a physical disk or drive, and an installation method compatible with a digital operating system.

[0079] 22. The button of aspect 3, wherein the tactile interface further comprises a system for initiating a calibration procedure of aspect 12 from the prosthesis.

[0080] 23. The user interface of aspect 11, wherein the virtual application further comprises a system for initiating the calibration procedure of aspect 12 from the virtual application.

[0081] The foregoing aspects of the present disclosure are merely illustrative and are not intended to limit the scope of the present disclosure.

[0082] Additional Aspects of the Disclosure 1. An electromyographic control system configured to guide a prosthetic user to calibrate a prosthetic device, the electromyographic control system comprising: a myoelectric prosthetic controller configured to control the prosthetic device; an electromyographic software component communicatively coupled to a plurality of electrodes in myoelectric contact with the user, the electromyographic software component configured to perform an analysis of the user's electromyographic (EMG) signal data, the EMG signal data received from the plurality of electrodes; and a user interface configured to provide a feedback indication to the user regarding a calibration quality of the EMG signal data based on the analysis of the EMG signal data, wherein the user interface is configured to initiate a calibration procedure to calibrate the myoelectric prosthetic controller, and the user interface comprises at least one of: (i) a button user interface including a calibration button; or (ii) a virtual user interface configured to display a feedback indication as at least one of (a) a quality requirement corresponding to the calibration quality of the EMG signal data, or (b) a message corresponding to the calibration quality of the EMG signal data.

[0083] 2. An electromyography control system as described in additional aspect 1, wherein the message includes at least one of (a) an indication of a cause of non-optimal signal data input of the EMG signal data, or (b) recommended steps for optimizing the signal data input.

[0084] 3. An electromyographic control system according to any of additional aspects 1 or 2, wherein a calibration procedure is initiated during a calibration session, and the virtual user interface provides recommended procedures for optimizing the signal data input, and a further calibration procedure is initiated from the user interface during the further calibration session to recalibrate the myoelectric prosthesis controller based on the recommended procedures.

[0085] 4. The electromyography control system of additional aspect 3, wherein the further calibration session is configured to facilitate at least one of: (a) deleting EMG signal data corresponding to one or more data sets or movements; (b) adding EMG signal data corresponding to one or more data sets or movements; and (c) replacing EMG signal data corresponding to one or more data sets or movements with new EMG signal data.

[0086] 5. The electromyographic control system of any of the previous additional aspects, wherein the myoelectric prosthetic controller is calibrated to control the prosthetic device based on the EMG signal data.

[0087] 6. The electromyography control system of any of the previous additional aspects, wherein the calibration button is configured to provide a feedback indication by at least one of an auditory stimulus, a tactile stimulus, or a visual stimulus.

[0088] 7. The electromyography control system of any of the previous additional aspects, wherein the virtual user interface displays a visualization of the EMG signal data in real time.

[0089] 8. The electromyography control system of any of the previous additional aspects, wherein the calibration procedure comprises a virtual user interface instructing a user to perform one or more index actions associated with the prosthetic device, the one or more index actions generating EMG signal data received from the multiple electrodes.

[0090] 9. The electromyography control system of additional aspect 8, wherein the virtual user interface is configured to receive one or more selections indicating at least one of the one or more indicator actions to be performed by the user.

[0091] 10. The electromyography control system of any of the preceding additional aspects, wherein the electromyography software component further comprises a pattern recognition component configured to analyze the user's EMG signal data, the pattern recognition component further configured to identify or classify the user's EMG signal data based on a particular action performed by the user.

[0092] 11. The electromyography control system of additional aspect 10, wherein the pattern recognition component comprises an adaptive machine learning component configured to determine a particular action to be performed by a user based on the user's EMG signal data.

[0093] 12. The electromyography control system of additional aspect 11, wherein the adaptive machine learning component is further configured to determine appropriate feedback indicators based on the user's EMG signal data.

[0094] 13. The electromyographic control system of any of the previous additional aspects, wherein the user interface is configured to reset a user's calibration data to calibrate the myoelectric prosthesis controller.

[0095] 14. An electromyographic control method for guiding a prosthetic device user to calibrate a prosthetic device, the electromyographic control method comprising: receiving electromyographic (EMG) signal data from a plurality of electrodes by an electromyographic software component communicatively coupled to the plurality of electrodes in myoelectric contact with the user; analyzing the user's EMG signal data by the electromyographic software component; providing a user interface with a feedback indication to the user regarding a calibration quality of the EMG signal data based on the analysis of the EMG signal data; and initiating a calibration procedure to calibrate a myoelectric prosthetic controller based on the calibration quality of the EMG signal data, the myoelectric prosthetic controller configured to control the prosthetic device, the user interface comprising at least one of: (i) a button user interface including a calibration button; or (ii) a virtual user interface configured to display the feedback indication as at least one of: (a) a quality requirement corresponding to the calibration quality of the EMG signal data; or (b) a message corresponding to the calibration quality of the EMG signal data.

[0096] 15. The electromyography control method of additional aspect 14, wherein a calibration procedure is initiated during a calibration session, and the virtual user interface provides recommended procedures for optimizing the signal data input, and a further calibration procedure is initiated from the user interface during the further calibration session to recalibrate the myoelectric prosthesis controller based on the recommended procedures.

[0097] 16. The electromyography control method of additional aspect 14 or 15, wherein the calibration procedure includes a virtual user interface that instructs a user to perform one or more index actions associated with the prosthetic device, the one or more index actions generating EMG signal data received from the multiple electrodes.

[0098] 17. The electromyography control method of any of additional aspects 14-17, wherein the electromyography software component further comprises a pattern recognition component configured to analyze the user's EMG signal data, the pattern recognition component further configured to identify or classify the user's EMG signal data based on a particular motion performed by the user.

[0099] 18. The electromyography control method of additional aspect 17, wherein the pattern recognition component comprises an adaptive machine learning component configured to determine a particular action to be performed by a user based on the user's EMG signal data.

[0100] 19. A tangible, non-transitory computer readable medium storing instructions for instructing a prosthetic user to calibrate a prosthetic device, the instructions, when executed by one or more processors, causing the one or more processors to: receive, by an EMG software component communicatively coupled to a plurality of electrodes in myoelectric contact with the user, electromyogram (EMG) data from a plurality of electrodes; analyze, by the EMG software component, the user's EMG signal data; based on analyzing the EMG signal data, provide a user interface with a feedback indication to the user regarding a calibration quality of the EMG signal data; and based on the calibration quality of the EMG signal data, initiate a calibration procedure to calibrate a myoelectric prosthetic controller, the myoelectric prosthetic controller configured to control the prosthetic device, wherein the user interface comprises at least one of: (i) a button user interface including a calibration button; or (ii) a virtual user interface configured to display a feedback indication as at least one of: (a) a quality requirement criterion corresponding to the calibration quality of the EMG signal data; or (b) a message corresponding to the calibration quality of the EMG signal data.

[0101] 20. The tangible, non-transitory computer-readable medium of additional aspect 19, wherein a calibration procedure is initiated during a calibration session, and the virtual user interface provides recommended procedures for optimizing the signal data input, and a further calibration procedure is initiated from the user interface during the further calibration session to recalibrate the myoelectric prosthesis controller based on the recommended procedures.

[0102] The foregoing additional aspects of the present disclosure are illustrative and not intended to limit the scope of the present disclosure.

[0103] Further considerations Although the disclosure herein describes detailed descriptions of many different embodiments, it should be understood that the legal scope of the description is defined by the language of the claims and equivalents set forth at the end of this patent. The detailed description should be construed as exemplary only and does not describe all possible embodiments, as it would be impractical to do so. Many alternative embodiments may be implemented using either current technology or technology developed after the filing date of this patent, and still fall within the scope of the claims.

[0104] The following additional considerations apply to the above discussion. Throughout this specification, multiple instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods have been illustrated and described as separate operations, one or more of the individual operations may be performed simultaneously, and the operations need not be performed in the order illustrated. Structures and functions presented as separate components in an example configuration may be implemented as a combined structure or component. Similarly, structures and functions presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements are included within the scope of the subject matter of this specification.

[0105] Additionally, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a particular way. In an exemplary embodiment, one or more computer systems (e.g., standalone, client, or server computer systems), or one or more hardware modules of a computer system (e.g., a processor or processors), may be configured as hardware modules that operate by software (e.g., an application or portion of an application) to perform certain operations described herein.

[0106] In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured to perform certain operations (e.g., as a dedicated processor, such as a field programmable gate array (FPGA) or application specific integrated circuit (ASIC)). A hardware module may also comprise programmable logic or circuitry that is temporarily configured by software to perform certain operations (e.g., contained within a general-purpose processor or other programmable processor). It will be appreciated that the decision to implement a hardware module mechanically, with dedicated and permanently configured circuitry, or with temporarily configured circuitry (e.g., configured by software) may be determined by cost and time considerations.

[0107] Thus, the term "hardware module" or "hardware component" should be understood to encompass a tangible entity that is physically constructed, permanently configured (e.g., implemented in hardware), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Given an embodiment in which the hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one time. For example, if the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as different hardware modules at different times. Thus, the software may configure the processor, for example, to configure a particular hardware module at one time and another hardware module at a different time.

[0108] Hardware modules may provide information to and receive information from other hardware modules. Thus, the described hardware modules may be considered to be communicatively coupled. When multiple such hardware modules are present simultaneously, communication may be achieved via signal transmission (e.g., through appropriate circuits and buses) connecting the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communication between such hardware modules may be achieved, for example, via storage and retrieval of information in a memory structure accessed by the multiple hardware modules. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. An additional hardware module may then later access the memory device to retrieve and process the stored output. Hardware modules may also initiate communication with input or output devices and operate on resources (e.g., collect information).

[0109] Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily (e.g., by software) or permanently configured to perform the associated operations. Such processors, whether temporarily or permanently configured, may constitute processor-implemented modules that operate to perform one or more operations or functions. Modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

[0110] Similarly, methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. The performance of certain operations may not only reside within a single machine, but may be distributed among one or more processors spread across several machines. In some embodiments, one or more processors may be located at a single location, while in other embodiments, the processors may be distributed across multiple locations.

[0111] Execution of certain operations may reside not only in a single machine, but may be distributed among one or more processors spread across several machines. In some exemplary embodiments, one or more processors or processor-implemented modules may be located in a single geographic location (e.g., in a home environment, in a work environment, or in a server farm). In other embodiments, one or more processors or processor-implemented modules may be distributed across multiple geographic locations.

[0112] This detailed description should be construed as merely exemplary and does not describe every possible embodiment, as doing so would be impractical, if not impossible. Those skilled in the art will be able to implement many alternative embodiments, using either current technology or technology developed after the filing date of this application.

[0113] Those skilled in the art will recognize that numerous modifications, changes, and combinations can be made to the above-described embodiments without departing from the scope of the present invention, and such modifications, changes, and combinations should be considered within the scope of the inventive concept.

[0114] Additionally, the claims at the end of this patent application are not intended to be construed under 35 U.S.C. 112(f) unless traditional means-plus-function language, such as "means for" or "step for," are expressly recited in the claim(s).The systems and methods described herein are directed to improvements in computer functionality, as well as improvements over the functionality of conventional computers.

Claims

1. 1. An electromyographic control system configured to guide a prosthetic user to calibrate a prosthetic device, the electromyographic control system comprising: a myoelectric prosthetic controller configured to control the prosthetic device; an electromyography (EMG) software component communicatively coupled to a plurality of electrodes in electromyographic contact with a user, the electromyography software component configured to perform an analysis of electromyography (EMG) signal data of the user, the EMG signal data received from the plurality of electrodes; a user interface configured to provide a feedback indication to the user regarding a calibration quality of the EMG signal data based on the analysis of the EMG signal data; the user interface is configured to initiate a calibration procedure to calibrate the myoelectric prosthesis controller; The user interface, (i) a button user interface that includes a calibration button; or (ii) an electromyography control system comprising at least one of a virtual user interface configured to display the feedback indicator as at least one of: (a) a quality requirement criterion corresponding to the calibration quality of the EMG signal data; or (b) a message corresponding to the calibration quality of the EMG signal data.

2. 2. The electromyography control system of claim 1, wherein the message comprises at least one of: (a) an indication of a cause of non-optimal signal data input of the EMG signal data; or (b) a recommended procedure for optimizing signal data input.

3. The calibration procedure is initiated during a calibration session; the virtual user interface providing recommended procedures for optimizing signal data input; The electromyographic control system of claim 1 , wherein a further calibration procedure is initiated from the user interface during a further calibration session to recalibrate the myoelectric prosthesis controller based on the recommended procedure.

4. 4. The electromyographic control system of claim 3, wherein the further calibration session is configured to facilitate at least one of: (a) deleting EMG signal data corresponding to one or more data sets or movements; (b) adding EMG signal data corresponding to one or more data sets or movements; and (c) replacing EMG signal data corresponding to one or more data sets or movements with new EMG signal data.

5. The electromyographic control system of claim 1 , wherein the myoelectric prosthetic controller is calibrated to control the prosthetic device based on the EMG signal data.

6. The electromyographic control system of claim 1 , wherein the calibration button is configured to provide the feedback indication by at least one of an auditory stimulus, a tactile stimulus, or a visual stimulus.

7. The electromyography control system of claim 1 , wherein a virtual user interface displays a visualization of the EMG signal data in real time.

8. the calibration procedure comprising the virtual user interface instructing the user to perform one or more indexing actions associated with the prosthetic device; The electromyographic control system of claim 1 , wherein the one or more index actions generate the EMG signal data received from the plurality of electrodes.

9. The electromyographic control system of claim 8 , wherein the virtual user interface is configured to receive one or more selections indicative of at least one of the one or more indicator actions to be performed by the user.

10. 2. The electromyography control system of claim 1, wherein the electromyography software component further comprises a pattern recognition component configured to analyze the EMG signal data of the user, the pattern recognition component further configured to identify or classify the EMG signal data of the user based on a particular movement performed by the user.

11. 11. The electromyographic control system of claim 10, wherein the pattern recognition component comprises an adaptive machine learning component configured to determine the specific motion to be performed by the user based on the EMG signal data of the user.

12. The electromyography control system of claim 11 , wherein the adaptive machine learning component is further configured to determine appropriate feedback indices based on the EMG signal data of the user.

13. The electromyographic control system of claim 1 , wherein the user interface is configured to reset the user's calibration data to calibrate the myoelectric prosthesis controller.

14. 1. An electromyographically controlled method for guiding a prosthetic user to calibrate a prosthetic device, the electromyographically controlled method comprising: receiving electromyogram (EMG) signal data from a plurality of electrodes by an electromyogram software component communicatively coupled to the plurality of electrodes in electromyographic contact with a user; analyzing the EMG signal data of the user by the electromyography software component; providing, based on analyzing the EMG signal data, a feedback indication to the user on a calibration quality of the EMG signal data in a user interface; initiating a calibration procedure to calibrate a myoelectric prosthesis controller based on the calibration quality of the EMG signal data, the myoelectric prosthesis controller being configured to control a prosthesis device; The user interface, (i) a button user interface that includes a calibration button; or (ii) an electromyography control method comprising at least one of a virtual user interface configured to display the feedback indicator as at least one of: (a) a quality requirement criterion corresponding to the calibration quality of the EMG signal data; or (b) a message corresponding to the calibration quality of the EMG signal data.

15. The calibration procedure is initiated during a calibration session; the virtual user interface providing recommended procedures for optimizing signal data input; 15. The electromyographic control method of claim 14, wherein a further calibration procedure is initiated from the user interface during a further calibration session to recalibrate the myoelectric prosthesis controller based on the recommended procedure.

16. the calibration procedure comprising the virtual user interface instructing the user to perform one or more indexing actions associated with the prosthetic device; The electromyographic control method of claim 14 , wherein the one or more index actions generate the EMG signal data received from the plurality of electrodes.

17. 15. The electromyography control method of claim 14, wherein the electromyography software component further comprises a pattern recognition component configured to analyze the EMG signal data of the user, the pattern recognition component further configured to identify or classify the EMG signal data of the user based on a particular movement performed by the user.

18. 20. The electromyographic control method of claim 17, wherein the pattern recognition component comprises an adaptive machine learning component configured to determine the specific action to be performed by the user based on the EMG signal data of the user.

19. 1. A tangible, non-transitory computer readable medium storing instructions for instructing a prosthetic user to calibrate a prosthetic device, the instructions, when executed by one or more processors, causing the one or more processors to: receiving electromyogram (EMG) data from a plurality of electrodes by an electromyogram software component communicatively coupled to the plurality of electrodes in electromyographic contact with a user; analyzing the EMG signal data of the user with the electromyography software component; providing, based on analyzing the EMG signal data, a feedback indication to the user on a calibration quality of the EMG signal data in a user interface; initiating a calibration procedure to calibrate a myoelectric prosthesis controller based on the calibration quality of the EMG signal data, the myoelectric prosthesis controller being configured to control a prosthesis device; The user interface, (i) a button user interface that includes a calibration button; or (ii) a tangible, non-transitory computer-readable medium comprising at least one of: (a) a quality requirement criterion corresponding to the calibration quality of the EMG signal data; or (b) a virtual user interface configured to display the feedback indicator as at least one of a message corresponding to the calibration quality of the EMG signal data.

20. The calibration procedure is initiated during a calibration session; the virtual user interface providing recommended procedures for optimizing signal data input; 20. The tangible, non-transitory computer readable medium of claim 19, wherein a further calibration procedure is initiated from the user interface during a further calibration session to recalibrate the myoelectric prosthesis controller based on the recommended procedure.

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