System and method for providing health information by user of sound

Abstract

A system may capture sound samples of a user and determine the presence of one or more musculoskeletal conditions in the user based on a comparison to other sound samples stored in a database. Analysis of sound samples includes determining various sound characteristics of the samples from samples stored in a database to determine matches to conditions and their related severity. The result is presented by an application that may present the location and severity on the 3D avatar model.

Description

[0001] This application claims priority to U.S. Provisional Application No. 63 / 319,368, filed on March 13, 2022. The entire contents of U.S. Provisional Application No. 63 / 319,368 and all other external citations contained herein are incorporated herein by reference. Technical Field

[0002] The field of the invention is the identification of physiological problems by sound analysis. Background Art

[0003] Typically, people discover health information by thinking about pain in their own body and / or by contacting a medical professional who will assess objective signs and subjective symptoms. Unfortunately, many people do not have enough background in anatomy and physiology to perform a proper self-assessment, and medical professionals are often unable to test or can only perform testing at high cost, and often only one client at a time.

[0004] Even when medical professionals are available, and have access to their various diagnostic tools and treatment modalities, they tend to focus on acute problems rather than the underlying causes. Even when a correct diagnosis is made, assessing the progress and effectiveness of any treatment can be problematic. Progression may be subtle and last for weeks, months, or even years.

[0005] Non-traditional health professionals may be more inclined to focus on the root causes, but they may also not be able to use or do not have the knowledge or tools needed. One tool that is available to almost everyone is self-assessment of health information through voice. Research scientist Sharry Edwards has been pioneering work in this field for at least three decades, but her work is more focused on education and has not yet matured into a product that is readily available. There are also some other players in this field. For example, BeyondVerbal uses voice patterns to identify emotional health parameters, and Sonde uses voice patterns to identify mental health parameters. US10,932,714 to Sanderson et al. teaches the identification of useful biometric signals from a person's voice and the compensation of the person's body with electronic signals.

[0006] US 10,932,714 and all other cited external materials are incorporated herein by reference in their entirety. If a definition or use of a term in a citation incorporated by reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein is deemed to control.

[0007] One of the difficulties with all known sound analysis techniques is that they do not focus on the musculoskeletal system of the body and therefore are difficult to translate into corrective exercise recommendations that a person can implement themselves, such as through stretching, foam rolling, stabilization, corrective exercises, strength training. For example, a gym member may not be able to afford a personal trainer and therefore may not have a good understanding of exercise techniques, specific muscles and / or muscle groups that need to be targeted, and methods to target these muscles and muscle groups. This can certainly apply to child athletes and even all the way up to professional athletes.

[0008] What is needed is a sound analysis technology that can be implemented on a cell phone or other commonly used electronic device that links sound patterns to problematic musculoskeletal problems and provides even inexperienced users with suggestions for exercises and other corrective exercises that they can implement themselves. It is also needed that the technology is sensitive and accurate enough so that users can determine their progress by easily retesting after following the recommendations. Summary of the invention

[0009] The present subject matter provides an apparatus, system, and method in which computer analysis of a person's voice is used to provide health information to the person.

[0010] The client device captures a voice sample of the user, which is transmitted to the server, which can determine one or more voice features from the voice sample. The voice features can then be matched with voice features of voice samples stored in a database that are associated with one or more conditions. In addition, the server can determine the severity of any associated conditions.

[0011] The analysis results are returned to the client device, which can present these results in the form of markers on portions of the three-dimensional (3D) avatar. The user client device can present additional information about the markers, including a more detailed description of the condition and recommended exercises or other treatments.

[0012] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments and the accompanying drawings, in which like reference numerals represent like components.

[0013] All publications mentioned herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. If a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

[0014] The following description includes information that may be helpful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, nor is it an admission that any publication specifically or implicitly referenced is prior art.

[0015] In some embodiments, the amount of the expression component, the digits of the property such as concentration, reaction conditions, etc., used to describe and claim certain embodiments of the present invention should be understood as "approximately" in some cases. Therefore, in some embodiments, the numerical parameters set forth in the written description and the appended claims are approximate values, which can be varied according to the desired properties sought to be obtained in a particular embodiment. In some embodiments, the numerical parameters should be interpreted according to the significant digits reported and by applying common rounding techniques. Although the numerical ranges and parameters of the wide range of some embodiments of the present invention are approximate values, the numerical values ​​set forth in the specific examples are reported as accurately as possible. The numerical values ​​presented in some embodiments of the present invention may contain certain errors, which are necessarily caused by the standard deviations found in their respective detection measurements.

[0016] Unless the context dictates otherwise, all ranges set forth herein should be interpreted as including their endpoints, and open ranges should be understood to include only commercially practical values. Likewise, unless the context dictates otherwise, all lists of values ​​should be interpreted as including intermediate values.

[0017] As used in the specification and claims below, the meanings of "a", "an", and "the" include plural references unless the context clearly dictates otherwise. In addition, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.

[0018] The description of the value range herein is intended only as a shorthand method for quoting each individual value falling within the range individually. Unless otherwise specified herein, each individual value is incorporated into the specification as if it were quoted individually herein. Unless otherwise specified herein or clearly contradictory to the context, all methods described herein can be performed in any suitable order. The use of any and all examples or exemplary language (e.g., "such as") provided for some embodiments herein is intended only to better illustrate the present invention and does not limit the scope of the present invention otherwise claimed. The language in the specification should not be interpreted as indicating any element that is not claimed for protection and is essential to the practice of the present invention.

[0019] The grouping of the alternative elements of the present invention disclosed herein or embodiments should not be construed as limiting.Each group member can be quoted and claimed separately, and can also be quoted and claimed in any combination with other members of the group or other elements herein.For convenience and / or patentability reasons, one or more members of the group can be included in the group or deleted from the group.When any such inclusion or deletion occurs, this specification is deemed to include the modified group at this, so as to meet the written description of all Markush (Markush) groups used in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic overview of a system according to an embodiment of the inventive subject matter.

[0021] Figure 2 is a flow chart of a process by which a user may obtain information about his or her physical condition in accordance with an embodiment of the present subject matter.

[0022] Figure 3 It shows the composition Figure 2 A flowchart of the detailed steps of step 220 is shown.

[0023] Figure 4 is an example of a dashboard screenshot of a mobile device application running on a client device.

[0024] Figure 5 It is a representation of a login page triggered by a user clicking on a two-dimensional (2D) avatar.

[0025] Figure 6 Is a representation of the mobile device application page that provides more details about the condition. DETAILED DESCRIPTION

[0026] In the following discussion, there will be a lot of references to servers, services, interfaces, engines, modules, clients, peers, portals, platforms, or other systems formed by computing devices. It should be understood that the use of these terms is considered to mean one or more computing devices with at least one processor (e.g., Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), Digital Signal Processor (DSP), x86, ARM, ColdFire, Graphics Processing Unit (GPU), multi-core processor, etc.), which is programmed to execute software instructions stored on a computer-readable tangible, non-temporary medium (e.g., hard drive, solid-state drive, random-access memory (RAM), flash memory, read-only memory (ROM), etc.). For example, a server may include one or more computers that operate as a network server, database server, or other type of computer server in a manner that performs the described roles, responsibilities, or functions. It should be further understood that the disclosed computer-based algorithms, processes, methods or other types of instruction sets may be embodied as a computer program product, which includes a non-temporary tangible computer-readable medium storing instructions that cause a processor to perform the disclosed steps. Various servers, systems, databases or interfaces may exchange data using standardized protocols or algorithms (possibly based on Hypertext Transfer Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS), Advanced Encryption Standard (AES), public key-private key exchange, web service application programming interface (API), known financial transaction protocols or other electronic information exchange methods). Data exchange may be performed through a packet switching network, the Internet, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN) or other types of packet switching networks.

[0027] The following discussion provides many example embodiments of the subject matter of the present invention. Although each embodiment represents a single combination of elements of the present invention, the subject matter of the present invention is considered to include all possible combinations of the disclosed elements. Therefore, if one embodiment includes elements A, B, and C, and a second embodiment includes elements B and D, then even if not explicitly disclosed, the subject matter of the present invention is also considered to include other remaining combinations of A, B, C, or D.

[0028] As used herein, unless the context dictates otherwise, the term "coupled to" is intended to include direct coupling (where two elements coupled to each other are in contact with each other) and indirect coupling (where at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with..." are synonymous.

[0029] In a preferred embodiment, a person's voice is recorded using a cell phone, tablet or other common consumer electronic device. The recording is sent to a receiving portal for processing, which involves using the recording to identify musculoskeletal biomarkers and corresponding intensities. This information is then returned to the sending device, which displays the biomarkers at the corresponding body locations of an avatar. The avatar is preferably a 3D representation of the person, enabling the viewer to envision the front, side and back views of the avatar.

[0030] Biomarkers are used to represent specific muscles, muscle groups or bones and are preferably displayed using color coding, with different colors representing different intensities.

[0031] Biomarkers and intensities are also described in tabular form, with the tabular links associated with corrective exercises and possibly other nutritional or other recommendations.

[0032] The recordings are preferably analyzed by comparison with previously collected correlations between the sound samples and a plurality of musculoskeletal biomarkers of other individuals. In some embodiments, the previously collected correlations may be derived from amplitude frequency scans of the collected sound samples, and

[0033] Verification can be accomplished by applying a transcutaneous electrical nerve stimulation (Tens) unit or other electrical stimulation to a specific muscle or muscle group to verify the data in the correlation set. Verification can additionally or alternatively be accomplished by using exercises or manually stimulating a specific muscle or muscle group.

[0034] Figure 1 is a schematic overview of a system 100 according to an embodiment of the inventive subject matter.

[0035] The system 100 may include a server or other computing device 110 that may perform the server-side processes discussed herein. The server 110 may be one or more computing devices located in one or more locations that include at least one processor and at least one non-transitory computer-readable storage medium (e.g., hard drive, RAM, etc.) that may store data and executable instructions as described herein.

[0036] exist Figure 1 A plurality of client devices 120 can also be seen in the figure. Device 120 is a computing device that includes at least one processor, at least one data exchange module (e.g., Wi-Fi, cellular, Ethernet, etc.), a memory for storing program instructions and related data (e.g., hard drive, RAM, ROM, etc.), and an input / output ("I / O") interface, which includes an audio sensor (e.g., microphone) and optional other I / O interfaces (e.g., monitor with mouse and keyboard, touch screen, stylus, etc.). Examples of suitable client devices 120 may include, but are not limited to, mobile phones, tablet computers, desktop computers, laptop computers, game consoles, and in-vehicle computers, etc.

[0037] To access the functionality discussed herein, a user may download an application to a client device 120, such as from an app store. The user may use the application to interact with the system 100, including visually and audibly inputting information and receiving information (e.g., by displaying an avatar with relevant biomarkers and locations, presenting information about a condition, etc.). In an embodiment, these functions may be accessed through a web portal accessed by an Internet browser.

[0038] Server 110 may include or be in communication with database 130, which includes a collection of voice samples 131. Voice samples 131 may include voice samples of a user saying certain words or phrases, and may also or alternatively include a collection of voice samples from other users saying those words or phrases. The voice samples stored in database 130 are specific words, phrases, or sentences that the user will later be prompted to say when using the application.

[0039] The sound sample 131 includes a plurality of sound features. Sound features are characteristics of the sound quality of the sound sample 131 that do not include language data. As used herein, "language data" includes any information that requires knowledge of a language to decipher and / or understand, such as English, Spanish, and Mandarin, etc. In other words, certain words can be used to elicit certain audio clues from a speaker, but not the meaning of the word itself. Examples of measurable sound features may include frequency, pitch, amplitude, volume, vocal fluidity, and tone.

[0040] The sound samples 131 may correspond to sound samples collected for the user and / or other users when the user / other users were healthy (i.e., not suffering from any condition that the system can determine.) Thus, the sound characteristics of the sound samples will reflect the sound characteristics of healthy users.

[0041] In embodiments of the present subject matter, sound samples 131 may include sound samples collected for the user and / or other users when the user is diagnosed or otherwise determined to have a particular condition. Thus, in these embodiments, the sound features reflect characteristic changes caused by the particular condition.

[0042] The words, phrases or sentences used in the stored voice samples 131 and the words, phrases or sentences that the user is asked to speak are selected because they contain enough features when spoken, which can then be analyzed and decomposed.

[0043] In an embodiment of the inventive subject matter, database 130 also stores a plurality of conditions that include one or more sound features known to be affected by the condition. As will be discussed in further detail below, a condition may include a condition signature (in some embodiments). In an embodiment, the stored conditions may include thresholds of sound features that need to be met in order for the stored condition to be returned as a potentially recognized condition. Database 130 may also store a severity or scale of each condition with the condition.

[0044] The conditions in database 130 also include information related to one or more musculoskeletal biomarkers. The musculoskeletal biomarkers may include the name of the condition and the location on the body that will be displayed on the avatar by the application installed on client device 120.

[0045] In an embodiment of the present subject matter, the conditions stored in database 130 may be conditions related to the categories of digestion, hydration, sleep, stress, energy, etc. Other suitable categories may include muscle, structure, flexibility, and strength, etc.

[0046] As described elsewhere herein, the system 100 can build the content of the database by collecting voice samples from a population and establishing a set of correlations between the collected voice samples and various conditions (e.g., musculoskeletal biomarkers). This can include manually entering known conditions to help the system 100 (e.g., via the server 110 and / or other computing device) establish correlations between the voice samples and the conditions themselves. As described herein, these voice samples can be used as a basis for comparison with user-provided voice samples to determine applicable conditions / biomarkers.

[0047] Figure 2is a flow chart of a process by which a user may obtain information about his or her physical condition in accordance with an embodiment of the present subject matter.

[0048] At step 210, the client device 120 receives voice input from the user via an audio sensor such as a microphone. The system 100 may receive enough information via the client device 120 to perform its processing using only the audio capture capabilities of the client device 120 (e.g., via an onboard microphone or via a connected microphone). This means that the system does not require any additional external accessories, such as specialized biometric or physiological sensors.

[0049] In an embodiment of the inventive subject matter, client device 120 may present a prompt of one or more words, phrases, or sentences that the user must repeat. As the user repeats the sentences, client device 120 captures the user's reading of the prompt via an audio sensor.

[0050] At step 220, the system 100 analyzes one or more features of the obtained vocal input. In embodiments such as those discussed herein, some or all of the analysis may be performed by the server 110. In these embodiments, the vocal input is transmitted from the client device 120 to the server 110, which performs the analysis. In some embodiments, the analysis may be performed by the client device 120. In these embodiments, the client device 120 must have sufficient processing power and sufficient memory to store the data it needs to perform the processes discussed herein.

[0051] The analysis of step 220 is Figure 3 The detailed breakdown is as follows:

[0052] In step 221 , the server 110 measures at least one sound feature from the sound input.

[0053] At step 222 , the server 110 maps at least one sound feature from the sound input to at least one reference sound feature from one or more reference sound samples 131 stored in the database 130 .

[0054] At step 223 , the server 110 identifies a condition and a severity based on at least one sound feature of a mapping from the sound input to at least one reference sound feature of one or more reference sound samples 131 .

[0055] The server 110 can identify the applicable situation among the multiple situations in a variety of ways.

[0056] In embodiments of the present subject matter, server 110 may first determine the variance of each of the one or more sound features from the corresponding mapped sound features of sound sample 131. In these embodiments, the stored sound features of sound sample 131 may be considered a baseline for measuring the sound features of the sound input, as the sound feature baseline values ​​are the sound feature values ​​that correspond to a person who does not suffer from any identifiable condition.

[0057] In these embodiments, the server 110 then compares the variance of each sound feature with one or more condition signatures stored in the database 130. A condition signature is considered to be a set of variances of multiple sound features that represent the presence of a condition within a person. The server 110 then identifies one or more conditions based on the comparison of the variances in the sound sample with the condition signature. The identified condition can be based on one or more of the variances that match one or more variances in the corresponding condition signature (e.g., one or more variance amounts match one or more threshold variance amounts for the corresponding sound features of a particular condition signature). The threshold for expecting a condition to be identified can be that all variances of the sound features from the sound input must match or exceed the variance of the condition signature for the condition to be returned.

[0058] For example, if a condition signature associated with a particular condition has a certain amount of variation in pitch, frequency, and tone, then if the variation in pitch, frequency, and tone in the sound characteristics of the provided sound sample meets or exceeds the variation in the condition signature, server 110 will match the state associated with the condition signature.

[0059] In other embodiments, server 110 can calculate a confidence score based on the amount of measured variance that exceeds the variance in the condition signature (e.g., based on a percentage of how many variances exceed the amount of condition signature variance). In these embodiments, the server returns the confidence associated with the condition to device 120, as discussed further below.

[0060] For example, if the condition signature associated with a particular condition has a certain amount of variation in pitch, frequency, and tone, but the variation in the sound characteristics only reaches or exceeds pitch and frequency in addition to tone, the server 110 can return this condition as a possible condition with a certain confidence level (in this simplified example, 66%).

[0061] In the above embodiment, the server 110 may determine the severity of the condition by determining the magnitude by which the variance of the sound features of the sound sample provided by the user exceeds the difference threshold of the corresponding sound features of the condition signature.

[0062] As described above, in embodiments of the inventive subject matter, the voice samples 131 stored by database 130 may include voice samples from users having a known condition. Thus, in these embodiments, the voice samples 131 have voice characteristics that reflect having the condition. Furthermore, the voice samples 131 may include multiple samples for multiple conditions of varying severity. Thus, database 130 stores voice samples 131 that reflect various degrees of severity for a given condition.

[0063] In these embodiments, server 110 may determine the presence and severity of a condition by matching a sound sample received from a user with one or more sound samples 131 stored in database 130 .

[0064] The server 110 may match the sound features of the provided sound sample with the sound features of the sound sample 131 in the database 130. The server 110 may perform such matching by a cluster recognition algorithm or other statistical matching techniques.

[0065] Because a well-populated database 130 will include many sound samples 131 of varying severity for any given condition, the server 110 may return many potential sound samples 131 for a matching particular condition (and potentially identify multiple conditions if the sound signatures are close enough), thereby providing greater confidence in the matched condition and the determined severity.

[0066] Based on these matches, server 110 may determine a confidence score based on the distribution of the matches.

[0067] One suitable method of extracting information from voice samples and analyzing them to identify user conditions can be found in the applicant's own issued patent US 10,932,714, which is incorporated herein by reference.

[0068] The severity of a particular condition / musculoskeletal biomarker may be considered as the "incoherence strength", which refers to the difference between the actual condition of a user's body part or segment and the "normal" or "healthy" condition.

[0069] At step 230, the server 110 transmits information about the identified condition back to the client device 120. The information includes identification data of the condition itself (e.g., clinical name, common name (if applicable), severity, body location, and one or more remedial measures.

[0070] At step 240 , the client device 120 receives the information and presents the information to the user.

[0071] The system 100 may also validate the information presented to the user based on the attempted treatment of the condition returned at step 240. Thus, at step 250, the client device 120 may ask the user to provide their feedback after attempting to treat the condition. The attempted treatment may include applying Tens units or other electrical stimulation to the affected area (e.g., if it is a muscle condition). The attempted treatment may include specific types of exercises and / or manual stimulation of specific muscles or muscle groups.

[0072] Feedback can be a simple yes or no question, such as "Did the treatment improve the condition?" It can include the ability for the user to provide the degree of improvement, if any (e.g., "On a scale of 1-10, how much improvement?")

[0073] This validation is then passed to the server 110 to further train the system so that the accuracy of the analysis can be improved.

[0074] Figure 4 is an example of a screenshot 400 of a dashboard of a mobile device application running on a client device 120. The dashboard shows an avatar display box 410 with a "Cell Coherence Score" 420 in the upper left corner, and a generic 2D avatar representation 431 that serves as a link to a 3D muscle avatar that is specific to the user, as will be shown further below. Below the avatar display box 430 are icons 441-445 that serve as links to 5 different categories of health information that can be analyzed: Digestion, Hydration, Sleep, Stress, and Energy. In an embodiment, the Cell Coherence Score can be a sum of conditions and their severity as a deviation from a completely healthy individual.

[0075] Figure 5 is a representation of a login page 500 triggered by a user clicking on a 2D avatar 431. Herein, the 3D avatar 520 has visually distinct markers 531, 532, 533 that highlight specific musculoskeletal biomarkers determined by the sound analysis to have the highest incoherence intensity. The 3D avatar is rotatable so that the user can see the highlighted musculoskeletal biomarkers from the front, side, and back of the avatar.

[0076] Figure 6 is a representation of a mobile device application page that provides additional details. This article describes Figure 5 A small anatomical representation of the region identified by the highlighted musculoskeletal biomarkers in 610-660, along with the name of the corresponding muscle or other anatomical feature, and visually distinct dots depicting the intensity level. The list is sorted from highest to lowest intensity. Figure 6 In the example above, there are four filled dots showing relatively high intensity levels and two hollow dots showing relatively low intensity levels. This is for illustrative purposes - Figure 5-Figure 6 The points in can have different colors (e.g., red for higher intensity and green for lower intensity) or other visually distinct features, allowing the user to quickly see areas of higher and lower intensity.

[0077] Figure 7 is a representation of screen 700 from the same mobile device application, which provides the same Figure 6 Thus, the application on the client device 120 is programmed to display additional information related to a selected one of the highlighted musculoskeletal biomarkers 710 (corresponding to item 610) selected in the embodiment of the present invention. Figure 6 This information is displayed when one of the biomarkers is selected in . The additional information includes additional anatomical and physiological information 720 , as well as recommended exercises 730 and additional potential treatment information or other suggestions 740 .

[0078] Various objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description of preferred embodiments of the present invention.

[0079] Thus, specific embodiments and applications of methods for providing health information using human voice have been disclosed. However, it will be apparent to those skilled in the art that more modifications than those already described may be made without departing from the inventive concepts herein. Therefore, the subject matter of the present invention is not limited except in accordance with the spirit of the appended claims.

[0080] It will be appreciated by those skilled in the art that, without departing from the inventive concept of this article, more modifications may be made in addition to those already described. Therefore, the subject matter of the present invention is not limited unless it is in accordance with the spirit of the appended claims. In addition, when interpreting the specification and claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "include" and "comprise" should be interpreted as referring to elements, components or steps in a non-exclusive manner, indicating that the referenced elements, components or steps may exist, be used or be combined with other elements, components or steps that are not explicitly referenced. Wherein, the specification claims relate to at least one selected from the group consisting of A, B, C... and N, and the text should be interpreted as requiring only one element in the group, rather than A plus N or B plus N, etc.

Claims

1. A method for providing health information by using a person's voice, comprising: Capture recordings from people; Use at least one processor to: retrieving at least one sound feature from the recording; mapping the at least one sound feature to at least a first musculoskeletal biomarker of a plurality of musculoskeletal biomarkers; based on the mapping, identifying the at least a first musculoskeletal biomarker having a corresponding first intensity; using an avatar to display a location of a first biomarker; visually depicting an indicator of the first intensity; presenting at least one recommendation based on the location of the first biomarker and the first intensity; as well as Among them, the different recommendations include at least one of corrective exercise and corrective treatment.

2. The method of claim 1 further comprising capturing the recording using a cell phone or other mobile consumer device without any extraneous accessories.

3. The method according to claim 1 also includes collecting sound samples from the population, establishing a correlation set between the collected sound samples and multiple musculoskeletal biomarkers of different people, and comparing the recording with the data in the correlation set. 4 . The method of claim 3 , further comprising adding first intensity information derived from an amplitude frequency sweep of the collected sound samples to the set of correlations.

5. The method of claim 3 further comprising applying a transcutaneous electrical nerve stimulation unit or other electrical stimulation to specific muscles or muscle groups to verify the data in the correlation set.

6. The method of claim 3, further comprising validating the data in the set of correlations using manual stimulation of specific muscles or muscle groups.

7. The method according to claim 1, wherein: The avatar comprises a 3D representation of a person, enabling a viewer to envision front, side, and back views of the avatar.

8. The method according to claim 1, wherein: The indicator uses color coding to specify the first intensity.

9. The method of claim 1, further comprising depicting the indicator in both the avatar and a list associating the indicator with an associated one of the different suggestions.

10. The method according to claim 9, wherein: The list also associates the indicator with a representation of body anatomy located proximate to the indicator on the avatar.

11. The method of claim 9, further comprising using the recording to identify at least a second musculoskeletal biomarker having a corresponding second intensity; and using an avatar to display a location of the second biomarker; and wherein, The list ranks the first and second musculoskeletal biomarkers according to their corresponding first and second intensities.

12. The method according to claim 1, wherein: At least a processor processes the recording using artificial intelligence to at least identify the first musculoskeletal biomarker having the first intensity.

13. The method of claim 1 further comprising providing a software application through an app store, the software application collecting the recordings, transmitting the recordings to a receiving portal, receiving data from a delivering portal, and presenting the received data to a user in an interface including the avatar.

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