Fitness monitoring systems and methods based on continuous collection of physiological data

Abstract

A system for assessing back pain, comprising a wearable device designed to be worn on or around the torso of a user, equipped with one or more sensors positioned on the back portion of the wearable. The system includes one or more processors configured to measure movement characteristics of the user's back, issue prompts to a computing device requesting input on the user's psychological perception of pain levels, and receive corresponding user input data. The system analyzes the movement characteristics and psychological pain perception data to generate an assessment of the user's back pain. The assessment is then output to an associated output component. This system is particularly beneficial for monitoring and evaluating back pain in real-time, providing actionable insights for healthcare applications.

Description

RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 676,752, filed Jul. 29, 2024, the entire contents of which are incorporated herein by reference.TECHNICAL FIELD

[0002] The disclosure relates to health and fitness monitoring.BACKGROUND OF THE INVENTION

[0003] Back pain is the world's leading cause of disability. 80% of adults in the United States will experience lower back pain in their lives, and 15-19% of adults in the United States will experience thoracic back pain in their lives. More than 50% of people with back pain spend most of their workdays sitting down. However, existing systems and methods have numerous drawbacks.

[0004] Monitoring back movement is important for various uses. For example, monitoring back movement is critical for people with back pain and people providing back pain care to identify the back pain, to understand what is causing the back pain and identify a course of action to mitigate the back pain. However, many existing health or fitness devices that monitor back movement involve a user wearing bulky equipment, which can cause discomfort. This presents a challenge to adoption and use of such devices. For example, desk workers have not been targeted for back movement monitoring, because existing devices are either too obtrusive to be commercially viable and require a chest strap and / or generated for use over short periods of time.SUMMARY OF THE INVENTION

[0005] In general, the disclosure describes a wearable that collects various physiological data or signals from a wearer. As used herein, a wearable may refer to any component that may contain sensors that can be worn around or attached to a torso of a user, removed from the user, and then reapplied at a later time, including an undergarment, an article worn on the torso, kinesiology tape systems, or some other type of lightweight garment worn on a torso of the user. In some examples, the wearable may monitor back movement without an undergarment. The wearable may further collect data for monitoring movement of other body parts, and other physiological parameters.

[0006] For this system, it is appreciated that existing wearable fitness monitors do not provide the ability for continuous data collection over a period of time of interest in understanding back pain, thereby preventing continuous fitness monitoring and reliable analysis of physiological data. It is further appreciated that another challenge affecting adoption of fitness devices by people with back pain may be the lack of a vibrant and interactive online community to view physiological data and share with other users.

[0007] According to one aspect of the present disclosure, it is appreciated that a health or fitness monitor that includes bulky components may hinder continuous or prolonged wear.

[0008] Accordingly, one aspect of the present disclosure is directed to providing a health or fitness wearable that may not include bulky components, thereby making the wearable slimmer, unobtrusive and appropriate for continuous or prolonged wear. In some examples, the wearable may not include light emitting diodes (LEDs), or any other components to deliver visual cues. In other examples, the wearable may include a display or light emitting diodes (LEDs) so as to provide feedback to a user. The ability to continuously wear the wearable, or to wear the wearable for a prolonged amount of time, further allows continuous, substantially continuous (e.g., data collected once every millisecond, every ten milliseconds, every fifty milliseconds, every second, or any other periodic amount of time that provides accurate analysis of a user's motion), or periodic collection of physiological data, as well as more reliable health or fitness monitoring. For the purposes of this disclosure, “continuous” may mean truly continuous, substantially continuous (e.g., data collected once every millisecond, every ten milliseconds, every fifty milliseconds, every second, or any other periodic amount of time that provides accurate analysis of a user's motion), or reasonably periodic to collect meaningful data to properly assess back movement and pain for a user. For example, examples of the wearable disclosed herein will allow a person with back pain to monitor data at all times, not just during a physiotherapy session.

[0009] According to another aspect of the present disclosure, some examples of the wearable may have the ability to stream collected or measured data wirelessly to an online application, for example, using either cellular data, an internet connection, or a Bluetooth connection to a cellular phone.

[0010] Another aspect of the present disclosure is directed to providing an application for health and fitness monitoring. In some examples, the application may be a social networking site. The application may allow users, such as people with back pain, to monitor their own back pain-related data, share information with their friends and back pain care providers, compete with other users, and win prizes. A user may include an individual with back pain who is monitoring data related to their back pain, such as an individual wearing a wearable disclosed herein, a desk worker, a member of the public, a back pain care provider. In some examples, a user may pick their own back pain care provider from a list to comment on their data.

[0011] Another aspect of the present disclosure is directed to methods of health and fitness monitoring, including methods for analyzing physiological data, for example using data received from the wearable disclosed herein. Data may be reported to a user via an application, such as an example of the application disclosed herein.

[0012] In some examples, the application may be configured to provide an interactive user interface. The application may be configured to display results based on analysis on physiological data received from one or more devices. The application may be configured to provide competitive ways to compare one user to another, and ultimately a more interactive experience for the user. For example, in some examples, instead of merely comparing a user's physiological data and performance relative to that user's past performances, the user may be allowed to compete with other users and the user's performance may be compared to that of other users.

[0013] In some examples, the application may be a mobile application or a website. In some examples, the application may be configured to communicate data to other websites or applications.

[0014] Systems and methods for assessing back pain using wearable sensor technology are described. These systems include a wearable device configured to be worn on or around a user's torso, with one or more sensors attached to a back portion of the wearable. One or more processors are operably coupled to the sensors and configured to measure movement characteristics of the user's back, issue prompts to a computing device for input indicative of the user's psychological perception of pain level, receive user-provided pain perception data, analyze the movement characteristics together with the perception data to produce an assessment of back pain, and output the assessment via an output component.

[0015] The described systems offer several benefits, including continuous and unobtrusive monitoring of back movement and pain perception, integration of psychological factors into pain assessment, and the ability to provide personalized feedback and comparative metrics. The wearable device is designed for comfort and prolonged use, enabling reliable data collection and analysis. Wireless communication and onboard memory allow for seamless data transmission and storage, while artificial intelligence models enhance the accuracy and relevance of pain assessments.

[0016] The system may include sensors such as inertial measurement units to capture movement variability, range of motion, angular velocity, and movement patterns. Additional features may include a user-actuated button to initiate a timestamp, a photoresistor to detect when the wearable is not being worn and disable sensor power, onboard memory to store measurements when out of wireless range and upload them when connectivity is restored, and wireless communication capability to transmit assessments to a remote server over Internet or cellular networks. The processors may derive an aggregate activity level, compute a user score based on both movement and perception data, and generate tactile, audible, or visual notifications; activity indicators; leaderboards or group comparisons; graphical representations such as graphs, 2D models, or 3D avatars; and calendar-based performance comparisons via a graphical user interface. The wearable may take the form of an undergarment, shirt, vest, bodysuit, compression garment, sweatshirt, jacket, or one or more strips of kinesiology tape with dedicated multi-use and single-use portions.

[0017] In one example, the disclosure is directed to a system including a wearable to be worn on or around a torso of a user, one or more sensors attached to a back portion the wearable, and one or more processors. The one or more processors may be configured to control the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable. The one or more processors may further be configured to issue one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user. The one or more processors may also be configured to receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. The one or more processors may further be configured to analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. The one or more processors may also be configured to output, to an output component, the assessment of the back pain for the user.

[0018] In another example, a method is provided that includes supplying a wearable device with back-mounted sensors configured to be worn on a user's torso, controlling the sensors to measure movement characteristics of the user's back while the user wears the wearable, issuing prompts to a computing device to obtain user input indicative of psychological pain perception, receiving the user-provided perception data, analyzing the measured movement characteristics together with the perception data to produce an assessment of back pain, and outputting that assessment via an output component.

[0019] In another example, the disclosure is directed to a non-transitory computer-readable storage medium which may include instructions that, when executed by one or more processors, cause the processors to control back-mounted sensors on a wearable device to measure movement characteristics, issue prompts for psychological pain perception input, receive user input, analyze both movement and perception data to produce a back pain assessment, and output the assessment to an output component.

[0020] In another example, the application is directed to a system comprising a garment to be worn on or around a torso of a user, a flexible printed circuit board including a plurality of sensors attached to a back portion the garment, and one or more processors configured to control each of the plurality of sensors to measure one or more movement characteristics of a back of the user wearing the wearable, analyze the one or more movement characteristics of the back of the user to produce an assessment of back pain for the user, and output, to an output component, the assessment of the back pain for the user.

[0021] The details of one or more examples of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF DRAWINGS

[0022] The following drawings are illustrative of particular examples of the present disclosure and therefore do not limit the scope of the invention. The drawings are not necessarily to scale, though examples can include the scale illustrated, and are intended for use in conjunction with the explanations in the following detailed description wherein like reference characters denote like elements. Examples of the present disclosure will hereinafter be described in conjunction with the appended drawings.

[0023] FIG. 1 is a back perspective views of an example wearable according to aspects of the present disclosure.

[0024] FIG. 2 is a block diagram illustrating a more detailed example of a computing device configured to perform the techniques described herein.

[0025] FIG. 3 illustrates the wearable of FIG. 1 with an example of the materials and placement used in the wearable, in accordance with one or more aspects of the present disclosure.

[0026] FIG. 4 is a block diagram illustrating a wearable according to aspects of the present disclosure.

[0027] FIGS. 5A and 5B illustrate an exemplary application-based user interface of a wearable according to aspects of the present disclosure.

[0028] FIG. 6 illustrates an example of scenario where the wearable and application-based interface may be used, according to aspects of the present invention.

[0029] FIG. 7 illustrates an example of the wearable being a number of sensors both attachable to a user and protected from the outside environment using kinesiology tape, in accordance with one or more aspects of the present disclosure.

[0030] FIG. 8 is a flow diagram illustrating an example method for analyzing data indicative of back pain for a user, in accordance with one or more aspects of the present disclosure.DETAILED DESCRIPTION

[0031] The following detailed description is exemplary in nature and is not intended to limit the scope, applicability, or configuration of the techniques or systems described herein in any way. Rather, the following description provides some practical illustrations for implementing examples of the techniques or systems described herein. Those skilled in the art will recognize that many of the noted examples have a variety of suitable alternatives.

[0032] FIG. 1 shows both front and back perspective views of one example of a wearable 102 in accordance with aspects of the present disclosure. As used herein, a wearable, such as wearable 102, may refer to any component that may contain sensors (e.g., sensors 106) that can be worn around or attached to a torso of a user, removed from the user, and then reapplied at a later time, including an undergarment, an article worn on the torso, kinesiology tape systems, or some other type of lightweight garment worn on a torso of the user. In some examples, the wearable 102 may be sleek and lightweight, thereby making it appropriate for continuous or prolonged wear. In other examples, the wearable 102 may include an indicator such as a light-emitting diode (LED) display. Examples of the wearable 102 may be configured to offer different sizes for those with different heights and body types. Examples may be provided in different sizes to fit different user sizes. Examples of the wearable may be in any variety or shade of color based on user preference.

[0033] Wearable 102 may serve as a garment designed to be worn on or around the torso of a user, the garment providing structural support and housing for embedded electronics and sensors 106. For example, wearable 102 is configured to fit snugly against the user's body to ensure that the embedded components maintain close contact with target areas of the back, thereby minimizing movement artifacts and ensuring consistent sensor placement, both factors that significantly influence accurate data collection. In some embodiments, wearable 102 may be implemented as various types of garments, including but not limited to undergarments, shirts, vests, bodysuits, compression garments, sweatshirts, or jackets. In an alternative example, wearable 102 may also include strips of kinesiology tape that adhere directly to the user's skin (see FIG. 7) to provide additional support for the embedded components. The material of wearable 102 may be lightweight and unobtrusive, allowing for prolonged or continuous wear without causing discomfort. Accordingly, in certain examples, wearable 102 may be waterproof to enable use in various environmental conditions and to reduce the need for frequent removal.

[0034] Back motion monitor 104 may be integrated into wearable 102 and positioned to cover a target area on the user's back. For example, back motion monitor 104 facilitates measurement of back movement characteristics, such as movement variability, range of motion (ROM), angular velocity, longitudinal data, and movement patterns, by ensuring that sensors 106 embedded within wearable 102 remain securely positioned against the user's back. In some embodiments, back motion monitor 104 may include a flexible circuit board, such as a flexible printed circuit board, that houses sensors 106, thereby maintaining a slim and unobtrusive system profile. The design of back motion monitor 104 may be optimized to maintain sensor alignment and stability during various user activities (e.g., sitting, standing, walking, or exercising). As a result, back motion monitor 104 significantly contributes to enabling the system to analyze back movement and to provide insights into back pain assessment.

[0035] Sensor(s) 106 are embedded within back motion monitor 104 and strategically positioned to measure movement characteristics of the user's back. For example, sensors 106 may include inertial measurement units (IMUs) capable of detecting acceleration, angular velocity, longitudinal data, and orientation, wherein the IMUs utilize changes in capacitance between contacts to calculate acceleration and derive movement data for the target area of the back. In some embodiments, multiple sensors 106 are distributed across different regions of the back to capture comprehensive data, enabling analysis of movement patterns and variability across the entire spine. In an alternative example, sensors 106 may be housed within electronic islands on the flexible circuit board. In general, data collected by sensors 106 is transmitted to computing device 110 for further analysis, either via flexible wired communication or wireless communication.

[0036] Computing device 110 is connected to sensor(s) 106 and serves as the processing unit for the system. For example, computing device 110 controls sensors 106, receives collected movement data, and performs advanced analyses to assess back pain. In some embodiments, computing device 110 may be implemented as a microcontroller embedded within wearable 102 or as a communication unit configured to communicate with an external device (e.g., a smartphone, tablet, or computer). In an alternative example, when computing device 110 is communicating with an external device, computing device 110 may transmit data wirelessly via Bluetooth, cellular networks, internet / Wi-Fi, or other communication protocols. Accordingly, computing device 110 may be equipped with any one or more of one or more processors, memory components, and communication modules to execute system functions or to provide data to other external devices to perform those functions. In some embodiments, computing device 110 applies artificial intelligence (AI) models to analyze movement data and psychological input from the user, producing a comprehensive assessment of back pain. Furthermore, computing device 110 may issue prompts requesting user input indicative of psychological perception of pain levels and integrate this data with movement characteristics for enhanced analysis. The results of the analysis are output to the user via an application or other output components, thereby providing actionable insights and recommendations for back pain management.

[0037] In accordance with one or more techniques of this disclosure, computing device 110 (or an external device in communication with computing device 110) may control the one or more sensors 106 to measure one or more movement characteristics of a back of the user wearing the wearable. Computing device 110 (or an external device in communication with computing device 110) may issue one or more prompts to a user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user. Computing device 110 (or an external device in communication with computing device 110) may receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. Computing device 110 (or an external device in communication with computing device 110) may analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. Computing device 110 (or an external device in communication with computing device 110) may output, to an output component, the assessment of the back pain for the user.

[0038] As shown in FIG. 1, the wearable may include a relatively small head portion configured to provide various functions such as data collection and streaming functions of the wearable. In some examples, the wearable may include a button underneath a portion of the fabric of the wearable. In some examples, the button may be held to initiate a time stamp and held again to end a time stamp, which may be transmitted to an application as a time stamp. Time stamp information may be used, for example, as an activity indicator to alert when periods of specific activity begin. In some examples, the wearable may be waterproof so that users never need to remove it, thereby further allowing for continuous wear.

[0039] The wearable may include a back motion monitor. Thus, the wearable may include a motion sensor portion of the wearable which covers a target area on the back. The wearable may be configured such that when a user wears the wearable, the fabric sits tightly against the user's body, so the sensors of the wearable are secured flush / close to the target area of the back. A tight fit and flush placement of the motion sensor on the target area of the back allow measurement of back movement. In some examples, back movement might be taken using motion sensors all directly in contact with the user's back. The motion sensors may include an inertial measurement unit (IMU). As the user moves, the capacitance between contacts in the IMU changes and will be used to calculate the acceleration of the IMU. The acceleration will be used to calculate the movement of the target area of the user's back. The outlined process will be carried on multiple area on the user's back. The data from each sensor will be combined and analyzed to gain insight into the user's back pain.

[0040] In some examples, the wearable may be configured to record back movement, stress, and other physiological metrics.

[0041] In some examples, the wearable may further be configured such that a button on the outside portion may be pressed, thus triggering the device to begin one or more of collecting data, calculating metrics and communicating the information to a network. In some examples, a photoresistor may be used to indicate whether the user is wearing the wearable or not. In some examples, power from the one or more of the IMUs may be cut off as soon as there is a spike in the signal, and reset once the user has put the wearable back on their body.

[0042] The system of FIG. 1 may be an IoT system including of a wearable which tracks back motion and, using inertial measurement units (IMUs), provides users personalized advice / suggestions to relieve their back pain, using artificial intelligence (AI). The sensors may be connected to a microcontroller via embedded circuitry and the data may transmitted using to a computing device, such as a smartphone, via Bluetooth or any other adequate communication protocol. The data is displayed on the smartphone in an app. This data is used to provide personalized back pain care. We measure acceleration data and then process it using proprietary algorithms to build proprietary metrics.

[0043] IMUs are strategically embedded onto different parts of the wearable so that they lie on parts of the back relevant to back pain. IMUs are designed on a flexible circuit board which is embedded / attached to the wearable. This design may be thin.

[0044] In essence, the wearable is one portion of the system described herein, and the sensors in the wearable measure a number of movement characteristics, including movement variability. Movement variability is an indication of how variable a patient's spine movement is over a period of time e.g. a working day or during sleep.

[0045] The sensors in the wearable may also measure range of motion (ROM) of any part of the spine. For instance, the one or more sensors may measure a lumbar ROM, which is usually measured from disc T12 or L1. However, in other instances, any segment of the spine, or the entirety of the spine, may be analyzed for ROM characteristics throughout use.

[0046] The sensors in the wearable may also measure angular velocity of any part of the spine. This could be a lumbar angular velocity, or angular velocity of any segment of the spine, or the entirety of the spine.

[0047] The sensors in the wearable may also measure movement patterns in the spine. The system may be looking for patterns of movement which indicate the wearer is guarding themselves from pain. Computing device 110 may use angular velocity and ROM to decide whether a pattern of movement is indicative of guarding, and the system will track and describe to the user how they are currently moving, and why it shows they are guarding themselves from pain. This may be represented as a 2d, side-on view of their spine, or even as a 3d digital model / avatar.

[0048] On the app, the user is prompted to answer questions about their back pain to provide a wider, contextual understanding on how back pain affects their life, and other factors. This is based on Cognitive Functional Therapy.

[0049] Back pain is related to psychology. One model of care, Cognitive Functional Therapy (CFT) has been proposed to address the psychological causes of back pain, although the techniques described herein can be used with any model of care connecting psychological causes of back pain with back pain perception levels and references to CFT are by example only. CFT has been shown to be significantly better than traditional physiotherapy. However, it is expensive to train CFT practitioners, and the sessions are time-consuming. This techniques of this disclosure use AI to deliver a technology-only platform which delivers a version of CFT to overcome the barriers of delivering CFT at scale. The technology may be used to support practitioners or as a stand-alone system.

[0050] Psychological perception of pain has been strongly linked to back pain and is believed to be a cause of chronic pain, and CFT is the most successful proposed model of care to take psychological perception of pain into account. Technology has not yet been used to implement CFT, meaning a) the proliferation of CFT practice around the globe has been slow, and b) the collection of a large dataset on the psychological assessment of pain has been inhibited. We are addressing this problem by collecting information about psychological pain perception in conjunction with movement information. For instance, the below Table 1 indicates potential psychological factors that could affect how a patient perceives pain, prompts that could be asked that are associated with that particular psychological factor, and examples of replies that could indicate a higher perception or awareness of the pain, potentially exacerbating the effects of the pain experienced by the user:TABLE 1Example Psychological Factors, Interview Prompts, and Negative RepliesPsychologicalFactorsInterview PromptsExamples of RepliesCognitive factors (thoughts about pain and coping with pain)Cause / meaningWhat do you think isThere is something damaged.the cause of the pain?ConsequencesWhere do you seeI will always have a weakness thatyourself in the future?I need to protect. It will get worseas I get older.VigilanceHow much is yourI can't stop thinking about the pain.mind on your pain?How confident are youI have no confidence in my back.with your back?Self-efficacyHow confident are youI have no confidence to play withto do the things in lifemy kids.that you value?PainHow has the painI can't garden, work, or socializeinterference / disabilityimpacted your life?because of my pain.How do you cope withThere is nothing that I can do foryour pain?my pain.Coping with painHave you avoidedI avoid anything that hurts myimportant activities orback.modified the way youI always protect my back when Ido them because ofliftyour pain?Catastrophic thoughtsWhat do you think willI fear my back is going to break.happen if you bendyour back?Where do you seeI fear I am going to end up in ayourself in the future?wheelchair.Emotional factors (feelings about pain)Emotional response toHow does the painIt's so intense I can't think.painmake you feel?How does the painI panic when I get the pain andaffect you emotionally?become hopeless about getting outof it.AnxietyDo you worry aboutThe pain makes me feel anxious allthe pain?the time. I worry it won't getbetter.Depressed moodDoes it get you down?I am in a dark place; I have lostIn what way?hope, and I see no way out.Frustration / angerDoes the pain makeI feel so frustrated and angry thatyou feel frustrated?this has happened to me.What is it thatfrustrates you?Influence of emotionsDoes how you feelMy pain gets worse when I amon pain(mood, worry, stress,stressed / anxious / down / tired.fatigue) influence yourpain?Fear of damageHow do you feel whenEvery time I bend I am terrified Iyou bend and lift?will prolapse my disk.Fear of painHow do you feel aboutI am just frightened of the pain andthe pain?the suffering.When I get the pain I can't do whatI need for hours.Pain predictabilityDoes the pain feelI can't predict it.predictable to you?Pain controllabilityDo you feel in controlI have little control over my pain.of the pain? Are therethings you can do tocontrol your pain?

[0051] It is to be recognized that the above examples of psychological factors, interview prompts, and negative replies are only examples, and other psychological factors, interview prompts, and replies could be utilized by the system described herein. For instance, other psychological factors not listed above could be analyzed, different prompts could be issued to test the listed psychological factors, and other replies to the prompts may be deemed negative. The above list is not exhaustive and only provided as one example of the factors, prompts, and replies that could be used by this system.

[0052] Back pain is a widespread issue, recognized as a leading cause of disability globally, affecting a substantial portion of the population. Despite the prevalence of back pain, existing systems and methods for monitoring and managing back pain face numerous challenges. Conventional wearable devices designed to monitor back movement are often bulky, obtrusive, and uncomfortable, making them impractical for continuous or prolonged use. These devices frequently rely on chest straps or other cumbersome components, which discourage adoption, particularly among desk workers or individuals requiring extended monitoring. Additionally, current systems do not adequately incorporate psychological factors into the assessment of back pain, despite growing evidence that psychological perceptions, such as anxiety, depression, and catastrophic thoughts, significantly influence the experience and management of chronic pain. The absence of an integrated approach combining physiological and psychological data reduces the effectiveness of existing solutions in delivering actionable insights for back pain care.

[0053] The present system addresses these shortcomings by introducing a lightweight, unobtrusive wearable configuration designed for continuous or prolonged use. The wearable incorporates one or more sensors, such as inertial measurement units (IMUs), strategically placed on the back portion of the wearable to measure movement characteristics, including movement variability, range of motion, angular velocity, longitudinal data, and movement patterns. These sensors are embedded in a flexible circuit design, ensuring a slim profile that enhances user comfort and wearability. The system further integrates advanced algorithms and artificial intelligence (AI) models to analyze the collected movement data in conjunction with user-provided psychological inputs. Prompts issued to a computing device associated with the user gather data on psychological perceptions of pain, such as emotional responses, catastrophic thoughts, and coping mechanisms. By combining physiological metrics with psychological factors, the system produces a comprehensive assessment of back pain, offering personalized insights and recommendations.

[0054] Additionally, the described system leverages modern connectivity features, such as Bluetooth and cellular communication, to enable seamless data transmission to a computing device or remote server. This facilitates real-time analysis and feedback, as well as the ability to store data locally when the wearable is out of range. The system also supports interactive user interfaces, including mobile applications and web platforms, which allow users to visualize their back movement and pain data through graphs, 2D or 3D digital avatars, and calendar-based comparisons. Social and competitive features, such as leaderboards and group performance metrics, further enhance user engagement and motivation.

[0055] By addressing both the physiological and psychological dimensions of back pain, the described system represents a notable advancement over previous approaches. This system enhances the accuracy and reliability of back pain assessments while broadening access to Cognitive Functional Therapy (CFT) principles through technology, addressing challenges such as cost and scalability. The combination of AI-driven analysis, modern wearable design, and interactive user interfaces provides a thorough and accessible solution for managing and alleviating back pain.

[0056] The subject matter described herein is directed to a practical application of technology for assessing and managing back pain through a combination of wearable sensor systems, data analysis, and user interaction. The techniques described herein include a specific and concrete implementation involving physical components, such as a wearable device, sensors, processors, and communication units, configured to collect, process, and transmit physiological and psychological data. The system utilizes tangible hardware to measure movement characteristics of a user's back, issues prompts to obtain psychological pain perception data, analyzes the combined data using advanced algorithms and artificial intelligence models, and outputs actionable assessments via output components.

[0057] This integration of hardware and software produces a technical improvement in the field of health and fitness monitoring by enabling continuous, unobtrusive, and personalized assessment of back pain. The invention solves longstanding problems associated with bulky, impractical monitoring devices and the lack of psychological data integration in pain assessment. The described system provides a specific technological solution that enhances the accuracy, reliability, and accessibility of back pain management, and is capable of real-world use in clinical, occupational, and personal health settings.

[0058] Accordingly, the techniques described herein are directed to a process, machine, manufacture, or composition of matter, and it provides a technical solution to a technical problem using concrete and tangible components. The techniques described herein are rooted in technological improvements and do not preempt any abstract idea, law of nature, or natural phenomenon. Instead, the techniques described herein apply specific technological tools to achieve a useful result.

[0059] The techniques described herein include a wearable system for assessing back pain that uniquely integrates continuous physiological monitoring with psychological pain perception analysis. Unlike conventional devices, the system features a lightweight, unobtrusive wearable, such as a garment or kinesiology tape strips, embedded with one or more sensors positioned on the back to measure movement characteristics including variability, range of motion, angular velocity, longitudinal data, and movement patterns. The system's processors are configured not only to collect and analyze this movement data, but also to issue prompts to a user's computing device to gather input on psychological factors affecting pain, such as emotional responses, anxiety, depression, and catastrophic thoughts.

[0060] One aspect of the current disclosure is the combination of real-time sensor data with user-reported psychological perceptions, enabling a comprehensive assessment of back pain that reflects both physical and mental dimensions. The system further incorporates advanced features such as artificial intelligence models for data analysis, wireless communication for seamless data transfer, and interactive user interfaces for feedback, visualization, and social engagement. The modular design, including multi-use and single-use tape portions for secure and hygienic sensor placement, enhances usability and comfort for prolonged wear.

[0061] By addressing the limitations of other devices, such as bulkiness, lack of psychological integration, and limited data accessibility, the techniques described herein provides a scalable and user-friendly solution for back pain management. This concept enables continuous, personalized, and actionable insights for users and care providers, representing a significant advancement in the field of health and fitness monitoring.

[0062] An example real-life implementation of this system may involve an office worker named Alex who experiences chronic lower back pain due to prolonged periods of sitting at a desk. Alex receives a wearable device in the form of a lightweight compression garment embedded with multiple inertial measurement units (IMUs) strategically positioned along the back portion of the garment. The wearable also includes a button for manual input and a photoresistor to detect when the garment is being worn.

[0063] Each morning, Alex puts on the garment before heading to work. Throughout the day, the sensors continuously measure movement characteristics such as range of motion, angular velocity, longitudinal data, and movement variability as Alex sits, stands, and moves around the office. The wearable streams this data wirelessly to Alex's smartphone via Bluetooth. If Alex steps away from the phone, the garment's onboard memory stores the data until connectivity is restored.

[0064] At regular intervals, the companion mobile application issues prompts to Alex, asking questions about pain levels and psychological factors, such as “How confident are you with your back today?” or “Has your pain affected your mood or activities?” Alex responds to these prompts, providing valuable context about psychological perceptions of pain.

[0065] The system's processors analyze both the sensor data and Alex's responses using proprietary algorithms and artificial intelligence models. The application then generates a personalized assessment of Alex's back pain, highlighting correlations between movement patterns and psychological factors. The results are displayed in the app as graphs, a 3D digital avatar of Alex's spine, and calendar-based comparisons to track progress over time.

[0066] If the assessment indicates increased pain or risk factors, the app may send a tactile or visual notification, suggesting posture adjustments or short exercises. Alex can also view a leaderboard comparing his activity and pain scores with other users, join support groups, and share data with a physical therapist for remote monitoring.

[0067] At the end of the week, Alex reviews his performance and pain trends, gaining actionable insights to improve his posture, activity levels, and psychological well-being. The system's unobtrusive design and integrated feedback loop empower Alex to manage his back pain proactively, both at work and at home, demonstrating the practical benefits and versatility of the disclosed invention.

[0068] FIG. 2 is a block diagram illustrating a more detailed example of a computing device configured to perform the techniques described herein. FIG. 2 illustrates only one particular example of computing device 210, and many other examples of computing device 210 may be used in other instances and may include a subset of the components included in example computing device 210 or may include additional components not shown in FIG. 2.

[0069] Computing device 210 may be any computer with the processing power required to adequately execute the techniques described herein. For instance, computing device 210 may be any one or more of a mobile computing device (e.g., a smartphone, a tablet computer, a laptop computer, etc.), a desktop computer, a smarthome component (e.g., a computerized appliance, a home security system, a control panel for home components, a lighting system, a smart power outlet, etc.), an integrated computer system, a distributed computer system, a vehicle, a wearable computing device (e.g., a smart watch, computerized glasses, a heart monitor, a glucose monitor, smart headphones, etc.), a virtual reality / augmented reality / extended reality (VR / AR / XR) system, a video game or streaming system, a network modem, router, or server system, or any other computerized device that may be configured to perform the techniques described herein. For the purposes of this disclosure, computing device 210 may be integrated into a wearable system, such as in computing device 110 or electronics 710, or may be an external computing device in communication with computing device 110 or electronics 710 to receive data from the wearable system.

[0070] As shown in the example of FIG. 2, computing device 210 includes user interface components (UIC) 212, one or more processors 240, one or more communication units 242, one or more input components 244, one or more output components 246, and one or more storage components 248. UIC 212 includes display component 202 and presence-sensitive input component 204. Storage components 248 of computing device 210 include communication module 220, analysis module 222, and data store 226, which may store artificial intelligence models, activity history, prompt information, and any other data utilized by computing device 210.

[0071] One or more processors 240 may implement functionality and / or execute instructions associated with computing device 210 to perform physiological and psychological assessments of a person's pain levels. That is, processors 240 may implement functionality and / or execute instructions associated with computing device 210 to track data recorded by sensors and answers to prompts to assess back pain.

[0072] Examples of processors 240 include any combination of application processors, display controllers, auxiliary processors, one or more sensor hubs, and any other hardware configured to function as a processor, a processing unit, or a processing device, including dedicated graphical processing units (GPUs). Modules 220 and 222 may be operable by processors 240 to perform various actions, operations, or functions of computing device 210. For example, processors 240 of computing device 210 may retrieve and execute instructions stored by storage components 248 that cause processors 240 to perform the operations described with respect to modules 220 and 222. The instructions, when executed by processors 240, may cause computing device 210 to perform physiological and psychological assessments of a person's pain levels.

[0073] Communication module 220 may execute locally (e.g., at processors 240) to provide functions associated with managing user interfaces, controlling sensors, and communicating between computing devices (such as in distributed systems). In some examples, communication module 220 may act as an interface to a remote service accessible to computing device 210. For example, communication module 220 may be an interface or application programming interface (API) to a remote server that manages user interfaces, controlling sensors, and communicating between computing devices (such as in distributed systems).

[0074] In some examples, analysis module 222 may execute locally (e.g., at processors 240) to provide functions associated with analyzing the physiological and psychological data measured from the user to provide an assessment of back pain for the user. In some examples, analysis module 222 may act as an interface to a remote service accessible to computing device 210. For example, analysis module 222 may be an interface or application programming interface (API) to a remote server that analyzes the physiological and psychological data measured from the user to provide an assessment of back pain for the user.

[0075] One or more storage components 248 within computing device 210 may store information for processing during operation of computing device 210 (e.g., computing device 210 may store data accessed by modules 220 and 222 during execution at computing device 210). In some examples, storage component 248 is a temporary memory, meaning that a primary purpose of storage component 248 is not long-term storage. Storage components 248 on computing device 210 may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.

[0076] Storage components 248, in some examples, also include one or more computer-readable storage media. Storage components 248 in some examples include one or more non-transitory computer-readable storage mediums. Storage components 248 may be configured to store larger amounts of information than typically stored by volatile memory. Storage components 248 may further be configured for long-term storage of information as non-volatile memory space and retain information after power on / off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage components 248 may store program instructions and / or information (e.g., data) associated with modules 220 and 222 and data store 226. Storage components 248 may include a memory configured to store data or other information associated with modules 220 and 222 and data store 226.

[0077] Communication channels 250 may interconnect each of the components 212, 240, 242, 244, 246, and 248 for inter-component communications (physically, communicatively, and / or operatively). In some examples, communication channels 250 may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

[0078] One or more communication units 242 of computing device 210 may communicate with external devices via one or more wired and / or wireless networks by transmitting and / or receiving network signals on one or more networks. Examples of communication units 242 include a network interface card (e.g., such as an Ethernet card), an optical transceiver, a radio frequency transceiver, a GPS receiver, a radio-frequency identification (RFID) transceiver, a near-field communication (NFC) transceiver, or any other type of device that can send and / or receive information. Other examples of communication units 242 may include short wave radios, cellular data radios, wireless network radios, as well as universal serial bus (USB) controllers.

[0079] One or more input components 244 of computing device 210 may receive input. Examples of input are tactile, audio, and video input. Input components 244 of computing device 210, in one example, include a presence-sensitive input device (e.g., a touch sensitive screen, a PSD), mouse, keyboard, voice responsive system, camera, microphone or any other type of device for detecting input from a human or machine. In some examples, input components 244 may include one or more sensor components (e.g., sensors 252). Sensors 252 may include one or more biometric sensors (e.g., fingerprint sensors, retina scanners, vocal input sensors / microphones, facial recognition sensors, cameras), one or more location sensors (e.g., GPS components, Wi-Fi components, cellular components), one or more temperature sensors, one or more movement sensors (e.g., accelerometers, gyros), one or more pressure sensors (e.g., barometer), one or more ambient light sensors, and one or more other sensors (e.g., infrared proximity sensor, hygrometer sensor, and the like). Other sensors, to name a few other non-limiting examples, may include a radar sensor, a lidar sensor, a sonar sensor, a heart rate sensor, magnetometer, glucose sensor, olfactory sensor, compass sensor, or a step counter sensor.

[0080] One or more output components 246 of computing device 210 may generate output in a selected modality. Examples of modalities may include a tactile notification, audible notification, visual notification, machine generated voice notification, or other modalities. Output components 246 of computing device 210, in one example, include a presence-sensitive display, a sound card, a video graphics adapter card, a speaker, a cathode ray tube (CRT) monitor, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic LED (OLED) display, a virtual / augmented / extended reality (VR / AR / XR) system, a three-dimensional display, or any other type of device for generating output to a human or machine in a selected modality.

[0081] UIC 212 of computing device 210 may include display component 202 and presence-sensitive input component 204. Display component 202 may be a screen, such as any of the displays or systems described with respect to output components 246, at which information (e.g., a visual indication) is displayed by UIC 212 while presence-sensitive input component 204 may detect an object at and / or near display component 202.

[0082] While illustrated as an internal component of computing device 210, UIC 212 may also represent an external component that shares a data path with computing device 210 for transmitting and / or receiving input and output. For instance, in one example, UIC 212 represents a built-in component of computing device 210 located within and physically connected to the external packaging of computing device 210 (e.g., a screen on a mobile phone). In another example, UIC 212 represents an external component of computing device 210 located outside and physically separated from the packaging or housing of computing device 210 (e.g., a monitor, a projector, etc. that shares a wired and / or wireless data path with computing device 210).

[0083] UIC 212 of computing device 210 may detect two-dimensional and / or three-dimensional gestures as input from a user of computing device 210. For instance, a sensor of UIC 212 may detect a user's movement (e.g., moving a hand, an arm, a pen, a stylus, a tactile object, etc.) within a threshold distance of the sensor of UIC 212. UIC 212 may determine a two or three-dimensional vector representation of the movement and correlate the vector representation to a gesture input (e.g., a hand-wave, a pinch, a clap, a pen stroke, etc.) that has multiple dimensions. In other words, UIC 212 can detect a multi-dimension gesture without requiring the user to gesture at or near a screen or surface at which UIC 212 outputs information for display. Instead, UIC 212 can detect a multi-dimensional gesture performed at or near a sensor which may or may not be located near the screen or surface at which UIC 212 outputs information for display.

[0084] In accordance with the techniques described herein, communication module 220 may control the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable. Communication module 220 may issue one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user, such as for over a same period of time as the measured movement characteristics. Communication module 220 may receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. Analysis module 222 may analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. Communication module 220 may outputs, to an output component, the assessment of the back pain for the user.

[0085] Computing device 210 and any of its components may further be configured to perform any methods or techniques described throughout this disclosure.

[0086] In accordance with one or more techniques of this disclosure, communication module 220 may control the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable. In some instances, the one or more sensors include one or more inertial measurement units. The one or more movement characteristics may include any one or more of movement variability, range of motion, angular velocity, longitudinal data, and movement patterns of the back of the user. The wearable, in some instances, may be any of an undergarment, a shirt, a vest, a bodysuit, a compression garment, a sweatshirt, or a jacket.

[0087] In other instances, the wearable may include one or more strips of kinesiology tape that adhere to the torso of the user, the one or more strips of kinesiology tape housing the one or more sensors. In such instances, the wearable may include a multi-use portion and a single use portion. The multi-use portion may include the one or more sensors housed between a first strip of kinesiology tape and a second strip of kinesiology tape. The single use portion may include a strip of double-sided tape and one or more kinesiology tape islands. A first side of the double-sided tape adheres to the multi-use portion, and at least a portion of a second side of the double-sided tape adheres to skin of the user.

[0088] Communication module 220 may issue one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user. The one or more prompts may include interview prompts associated with at least one psychological factor selected from the group consisting of cognitions, emotional responses, anxiety, depression, and catastrophic thoughts.

[0089] Communication module 220 may receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. Analysis module 222 may analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. In some instances, analysis module 222 may apply an artificial intelligence model to the one or more movement characteristics and the user input to produce the assessment of back pain.

[0090] Communication module 220 may output, to an output component, the assessment of the back pain for the user. In some instances, communication module 220 may additionally output, via the output component, one or more of a notification selected from the group consisting of a tactile notification, an audible notification, or a visual notification in response to the assessment of back pain, an activity indicator marking periods of specific activity based on user input or sensor data, a graphical user interface of a leaderboard or group performance metric comparing the user's assessment to other users, a graphical representation of back movement and pain data, including at least one of a graph, a 2D model, or a 3D digital avatar of the user's spine, and a graphical user interface of calendar-based comparisons of the user's performance over different time periods.

[0091] In some instances, the wearable further includes a button disposed on an outer surface of the wearable. In such instances, communication module 220 may initiate a time stamp in response to user actuation of the button. In other instances, communication module 220 may, in response to actuation of the button, initiate at least one of collecting movement data, calculating physiological metrics, or communicating information to a network.

[0092] In some instances, the wearable further includes a photoresistor configured to detect whether the wearable is being worn by the user. Communication module 220 may disable power to the one or more sensors when the wearable is not worn.

[0093] In some instances, analysis module 222 may derive an aggregate activity level for the user based on the one or more measured movement characteristics over a period of time.

[0094] In some instances, storage components 247 may store measured movement characteristics when the wearable is out of wireless range of an external computing device, and communication module 220 may upload the stored movement characteristics to the external computing device when the wearable returns into range.

[0095] In some instances, communication module 220 may transmit the assessment of back pain to a remote server via an internet connection or a cellular connection.

[0096] In some instances, analysis module 222 may compute a user score based on the one or more movement characteristics and the data indicative of the psychological perception of the pain level. In such instances, communication module 220 may output the user score for display via the output component.

[0097] While shown as a single computing device, computing device 210 may be a distributed computing system, with other computing devices having similar components or certain portions of the components described in FIG. 2. In such instances, different portions of the actions performed by computing device 210 may be performed by different computing devices in the distributed computing system. For instance, a first computing device integrated into a wearable may control the sensors to gather the movement data and send that data to a second computing device, such as a smartphone, which issues the prompts to the user, analyzes all of the received data, and outputs the assessment.

[0098] FIG. 3 illustrates the wearable of FIG. 1 with an example of the materials and placement used in the wearable, in accordance with one or more aspects of the present disclosure. Garment 320, as depicted in FIG. 3, may be worn on or around the torso of a user 650. In general, garment 320 serves as the foundational structure for a clothing version of the wearable system described herein, and garment 320 is configured to house and support various components, such as sensors 106 and computing device 110, that enable the monitoring of back movement and the assessment of back pain. For example, garment 320 may be tailored to fit snugly against the user's body, thereby ensuring that sensors 106 maintain close contact with the back for accurate data collection.

[0099] Garment 320 may be implemented in various forms, including but not limited to an undergarment, shirt, vest, bodysuit, compression garment, sweatshirt, or jacket. In some embodiments, the material of garment 320 is selected to provide comfort during prolonged wear while maintaining durability and flexibility. For instance, garment 320 may be constructed from lightweight, breathable fabrics such as spandex, polyester, or a blend of synthetic and natural fibers. These materials enable garment 320 to conform to the user's body shape, promoting a secure fit that minimizes movement of sensors 106 relative to the user's back.

[0100] In an alternative example, garment 320 may include additional design features to enhance the functionality of garment 320. For example, elastic bands or adjustable straps may be incorporated to accommodate users of different body sizes and shapes. Furthermore, reinforced sections or pockets may be provided to securely house sensors 106 and computing device 110, preventing displacement during movement. In some embodiments, garment 320 may be designed to be washable, with detachable or water-resistant components to protect embedded electronics.

[0101] The back portion of garment 320 is specifically configured to support the placement of sensors 106 that measure movement characteristics of the user's back. Sensors 106 may include inertial measurement units (IMUs) or other motion-sensing devices. The snug fit of garment 320 helps ensure that sensors 106 remain in close proximity to the user's back, enabling the accurate measurement of parameters such as movement variability, range of motion, angular velocity, longitudinal data, and movement patterns.

[0102] Garment 320 may also include integrated features such as a button or a photoresistor. For instance, the button, if present, may be disposed on the outer surface of garment 320 and used to initiate specific functions, such as starting data collection or creating a time stamp. In another example, the photoresistor may detect whether garment 320 is being worn by user 650 and enable the system to conserve power by disabling sensors 106 when garment 320 is not in use.

[0103] Garment 320 may also be designed to facilitate the integration of advanced features, such as wireless communication modules for transmitting data to a computing device or remote server. In some embodiments, garment 320 may include pathways or channels for routing wires or flexible circuits, ensuring that these components do not interfere with the user's comfort or the accuracy of the sensor readings. Garment 320 provides the structural and functional support required for the accurate and continuous monitoring of back movement and the assessment of back pain.

[0104] FIG. 4 shows a block diagram corresponding to one example of a wearable according to aspects disclosed herein. The wearable includes a system 400 configured to provide continuous collection and monitoring of physiological data. As described above under computer system and in conjunction with the exemplary example of FIG. 1, the system 400 includes a controller 402, such as a microcontroller, and multi-axis accelerometers 404A-404N. Still referring to FIG. 4, system 400 includes a link to computing device 410, which may have similar components to and function similarly to computing device 210 of FIG. 2. In some examples, the link may be a Bluetooth Low Energy link, but in other examples, the link may be any wired or wireless connection that enables the transmission of data between controller 402 and computing device 410. The Bluetooth Low Energy link may be configured to wirelessly communicate data to an external network. Some examples of the wearable may be configured to stream information wirelessly to a computing device 410. In some examples, data streamed from a user's wearable to an external network may be accessed by the user via an application. In some examples, analysis module 422 may apply various analytical functions, such as using AI model 428, to the data, stored in data store 426, before the data is displayed on the app GUI.

[0105] In some examples, a method for monitoring a health or fitness status may include providing an application. Aspects of the application may be implemented, for example, using one or more computer systems as previously described. In some examples, a system for health or fitness monitoring may include the wearable and one or more computer systems configured to generate an application for tracking health or fitness information. In some examples, the application may be provided to users of the wearable disclosed herein. The application may be configured to allow a user to create an account and to log in to their account. In some examples, the sign up process may include one or more of asking for name, nickname, email, home address, height, weight and more specific questions to assess the psychological parameters of a user, and a code associated with the wearable. In some examples of the wearable, the code may be provided as a label disposed on the interior of the wearable. In some examples, a code associated with a wearable may be used to register with the application. Once a user has subscribed to the application, all the recorded data may be available there for perusal. Additionally, the application may be configured to allow users to add information to their profiles. For example, users may add pictures, favorite activities, sports team(s), and may search for teammates / friends to share information with. This application may also communicate with other applications or servers from applications, such as existing health or activity monitoring applications, to get even more data for the pain analysis provided herein.

[0106] Referring again to FIG. 4, the network interface connecting controller 402 and computing device 410 may be configured such that data collected by the wearable may be streamed wirelessly. In some examples, data may be transmitted automatically, without the need to manually press any buttons. In some examples, the wearable may include a cellular chip built into the wearable. In one example, the network interface may be configured to stream data using Bluetooth technology. In another example, the network interface may be configured to stream data using a cellular data service, such as via a 3G, 4G, or 5G cellular network.

[0107] In some examples, a built in Bluetooth chip will allow the wearable to communicate with a cell phone or a computer in order to stream data to the application. The wearable may be further configured to store data when it is not within close proximity of a streaming outlet such as a cellular phone. The wearable may further be configured to send data to a cellular phone via Bluetooth when it is nearby a linked phone, and to the Internet via cellular data. This example may provide a convenient method of transmitting data based on utilizing existing cellular data plans.

[0108] In another example, the wearable may include a cellular communication chip that wirelessly uploads all metrics to the application without utilizing any other device. In one example, the chip may be a 20 chip that is relatively cheaper and uses less power, but transfers at a slower speed. In another example, a 30 chip may use negligibly more power compared with a 20 chip for almost twice the transfer speed, thus half the transfer time, using much less energy from the battery.

[0109] In some examples, the wearable may be configured to store fitness metrics or physiological data, such as in storage 418, until the fitness metrics or physiological data are uploaded. Data may be uploaded on a frequency suitable to save power while retaining ease of use.

[0110] System 400 further includes one or more sensors 404A-404N (collectively, “sensors 404”). As discussed above, the sensors 404 may include a motion sensor. In some examples, the wearable may further include one or more of movement variability, range of motion and activity monitors. Movement variability may be based on a user's back movement over time. A movement variability measurement may be improved if a user chooses to enter the beginning and end period of wear on the application. In some examples, manual entering of data may not be performed in order to derive movement variability; however, data entry may be used to improve the accuracy of the results. In some examples, if a user has forgotten to enter data, he or she can enter it for past weeks and the relevant metrics may be updated accordingly.

[0111] In some examples, the wearable may use the IMU data to calculate motion and calculate distance, whether it be in real terms as steps or miles or as a converted number.

[0112] Activity sensors may be used, for example, to classify or categorize activity, such as walking, running, performing another sport, standing, sitting, or lying down. In some examples, one or more of collected physiological data may be aggregated to generate an aggregate activity level. For example, back movement variability and range of motion may be used to derive an aggregate activity level. The level may have no absolute meaning, but may be compared to or evaluated relative to previous recordings of the user's aggregate activity level, as well as the aggregate activity levels of other users.

[0113] In some examples, the aggregate activity level may correspond to points, rewards or other incentives provided to a user. In one example, points, rewards or other incentives may be stored in a computer system and accessed via an application, such as the application described above. A currency as used herein may include points, rewards, money or other incentives. In some examples, a method of health or fitness monitoring may include deriving a currency based on health or fitness performance metrics. In some examples, currency may be used to unlock new features of the application which will allow users, for example, to compare metrics with more advanced athletes. For example, currency may be used to unlock special, elite trainers, such as acclaimed physiotherapists. In some examples, currency may be used to promote rank and status in the application. The application may be configured to include different classes, each class providing access to unique features. A user may be classified based on how much currency has been acquired by the user. In some examples, the application may be configured such that currency may be wagered for fitness challenges among users.

[0114] FIGS. 5A, 5B, and 6 show various examples of a wearable according to aspects disclosed herein. In some examples, a method for monitoring a health or fitness status may include providing an application configured to allow a user to view back movement and pain data in a graph.

[0115] For example, the application may be configured to allow with multiple users including individuals with back pain, individuals who want to prevent back pain, or back pain care givers to compare back movement metrics.

[0116] Some examples of the health or fitness monitoring method may include calculating one or more metrics or scores based on collected physiological data such as movement quality, movement variability, and psychological parameters. Psychological parameters may be assessed using a questionnaire where the user inputs the answers via the app. In other examples, the method may include calculating an overall score based on one or more of movement quantity, movement variability, self-assessment of pain and comparison to other users with similar physiological metrics.

[0117] The method may further include providing the ability for users to create groups using the application. A group may have a group owner and one or more members. A group may have one or more of a message board, calendar upload, photos, a group performance metric and average metrics of group individuals. In some examples, the calendar upload may allow users to compare past performance to current performance with regard to time of week, month and year. The calendar upload may allow a group owner, such as a coach, to input indicators of what type or level of physical exertion may be performed.

[0118] Some examples of the method may include providing a “Leaderboard” for listing best performing users in a feed of updates from users that a current user is connected to.

[0119] Graphical user interface 502 may comprise a digital display element configured to present posture analytics to the user 650 in a visually intuitive and interactive manner. For example, graphical user interface 502 may be part of an application associated with the wearable 102 system or tape system 702 described herein. In general, graphical user interface 502 is designed to provide a summary of the user's 650 posture quality, which is derived from movement characteristics measured by sensors 106 integrated into the wearable 102. Specifically, the posture quality score, prominently displayed as a numerical value within a circular progress indicator, reflects the user's 650 overall posture performance over a defined period, such as a day. The circular progress indicator may be color-coded to provide immediate visual feedback, with different colors representing varying levels of posture quality (e.g., excellent, moderate, or poor).

[0120] Accordingly, beneath the posture quality score, graphical user interface 502 may include a time-series graph that illustrates the user's 650 posture quality throughout the day. This graph may be generated based on continuous or periodic data collected by the wearable's 102 sensors 106, such as inertial measurement units (IMUs), and analyzed by the system's processors 240. For instance, the graph may allow the user 650 to identify trends, fluctuations, and specific time periods during which posture quality was suboptimal. In this example, the x-axis of the graph represents time, while the y-axis represents the posture quality score, thereby enabling users 650 to correlate their posture performance with daily activities or routines.

[0121] Furthermore, graphical user interface 502 may include navigation options, such as “Tracking” and “Analytics,” which allow the user 650 to switch between different views or functionalities within the application. The “Tracking” option may provide real-time data on the user's 650 posture, whereas the “Analytics” option may offer detailed insights and historical data analysis. In some examples, the interface is designed to be user-friendly, ensuring that users 650 of varying technical proficiency can easily interpret and act upon the information presented.

[0122] Graphical user interface 504 may comprise another digital display element within the application, specifically tailored to present movement variability analytics to the user 650. Movement variability may be an important metric derived from the wearable's 102 sensor 106 data, representing the consistency or variability in the user's 650 back movements over time. Similar to graphical user interface 502, graphical user interface 504 may feature a circular progress indicator that prominently displays the movement variability score as a numerical value. For example, lower variability scores may indicate more consistent and controlled movements, while higher scores may suggest irregular or erratic movement patterns.

[0123] Accordingly, below the movement variability score, graphical user interface 504 may include a time-series graph that visualizes the user's 650 movement variability throughout the day. This graph may be generated by analyzing data collected by the wearable's 102 sensors 106—such as angular velocity, range of motion, and movement patterns—and processed by the system's processors 240. In an alternative example, the x-axis of the graph represents time, while the y-axis represents the movement variability score, thereby helping users 650 identify periods of high or low movement variability and correlate these patterns with specific activities or events.

[0124] Moreover, graphical user interface 504 may include the same navigation options, e.g., “Tracking” and “Analytics”, as graphical user interface 502, allowing users 650 to seamlessly switch between different views or functionalities. The interface may be optimized for clarity and ease of use, providing actionable insights that enable users 650 to understand how their movement variability impacts overall back health and to make informed decisions about their posture and activity levels. In general, graphical user interface 504 is configured to ensure that users 650 can effectively engage with the data and derive meaningful conclusions.

[0125] User 650 may be depicted seated at a workstation, representing an individual engaging in a typical sedentary activity such as working at a desk or using a computer. For example, user 650 is shown in a posture that emphasizes alignment of the torso, back, and limbs, which is relevant for monitoring back movement using back motion monitor 104 and assessing back pain. In general, the posture-related information collected from user 650 serves as a baseline for detecting deviations in back alignment during prolonged seated activities.

[0126] Wearable 102 is configured to be worn around the torso of user 650. The wearable 102 includes one or more sensors 106 positioned on a back portion of the device to measure movement characteristics of the user's back. For instance, these sensors 106 collect data on parameters such as movement variability, range of motion, angular velocity, and movement patterns, all of which are significant for analyzing back health. In some examples, the sensors 106 may be inertial measurement units (IMUs) or strain gauges, each operable to detect slight changes in back posture and motion.

[0127] The sensor data may be transmitted to computing device 110, which may analyze this data or forward the data to an external computing device for further processing. For example, an external computing device may receive prompts generated based on sensor-derived movement metrics and may provide input interfaces related to the psychological perception of pain. In an alternative example, the external computing device may display graphical feedback to user 650 and record subjective pain ratings, thereby contributing to a comprehensive assessment of back pain that integrates both biomechanical and perceptual data.

[0128] FIG. 7 illustrates an example of the wearable being a number of sensors both attachable to a user and protected from the outside environment using kinesiology tape, in accordance with one or more aspects of the present disclosure. Tape system 702 may represent a layered structure that integrates multiple portions to form a wearable device configured for secure adhesion to a user's skin 718 while housing electronics 710 for physiological monitoring. For example, tape system 702 may provide a foundation for both functionality and comfort by combining reusable and single-use components in a modular arrangement. In general, tape system 702 is constructed to be lightweight and unobtrusive, enabling prolonged wear without discomfort, and to ensure that electronics 710 remain securely housed and properly aligned with the user's body.

[0129] Multi-use portion 704 may comprise a reusable section of tape system 702 configured to house important components of the wearable device. For instance, multi-use portion 704 may include a first kinesiology tape layer 708, electronics 710, and a second kinesiology tape layer 712 stacked to form a cohesive unit. In some examples, multi-use portion 704 may be designed to endure repeated use by embedding electronics 710 between flexible, breathable tape layers, thereby providing protection and structural stability while conforming to the user's movements.

[0130] Single-use portion 706 may comprise a disposable section of tape system 702 configured to adhere directly to the user's skin 718. For example, single-use portion 706 may include double-sided tape 714 and kinesiology tape island layer 716 arranged to provide secure attachment and to facilitate hygienic replacement after each use. In an alternative example, single-use portion 706 may be replaced upon removal of the wearable device, thereby maintaining optimal adhesion characteristics and preventing interference with the embedded electronics 710.

[0131] Kinesiology tape layer 708 may serve as the uppermost layer of multi-use portion 704, providing a protective and supportive element for electronics 710. For instance, kinesiology tape layer 708 may be fabricated from a breathable, stretchable material that conforms to the user's back and accommodates natural movement without impeding sensor performance. In some examples, kinesiology tape layer 708 may also contribute to the overall adhesion of tape system 702, ensuring that the multi-use portion 704 remains securely positioned.

[0132] Electronics 710 may be embedded within multi-use portion 704 and may represent primary functional components of the wearable device. For example, electronics 710 may include one or more inertial measurement units (IMUs) and associated circuitry for capturing movement characteristics of the user's back. In general, electronics 710 may be strategically placed between kinesiology tape layers 708 and 712 to minimize mechanical stress and to ensure accurate data acquisition, and may communicate with external computing devices via wired or wireless protocols for real-time analysis and feedback.

[0133] Electronics 710 may include one or more processors configured to analyze the movement characteristics measured by the sensors embedded within the wearable. These processors may execute instructions to process raw sensor data, derive relevant metrics such as movement variability, range of motion, angular velocity, longitudinal data, and movement patterns, and generate an assessment of back pain for the user. Additionally or alternatively, electronics 710 may include one or more communication units, such as wireless transceivers, that are configured to transmit the measured movement characteristics and assessment data to a secondary computing device, for example, a smartphone, tablet, or remote server. The communication units may utilize protocols such as Bluetooth, Wi-Fi, or cellular connectivity to enable real-time or periodic data transfer, thereby facilitating further analysis, user feedback, or integration with application-based interfaces.

[0134] Kinesiology tape layer 712 may be positioned beneath electronics 710 within multi-use portion 704 to serve as a secondary protective layer. For instance, kinesiology tape layer 712 may be fabricated from a flexible, breathable material that provides a stable base for electronics 710, preventing displacement during use. In some examples, kinesiology tape layer 712 may enhance the structural integrity of multi-use portion 704 and contribute to the durability of the wearable device under repeated application.

[0135] Double-sided tape 714 may be incorporated into single-use portion 706 to provide adhesive coupling between multi-use portion 704 and kinesiology tape island layer 716. For example, double-sided tape 714 may include adhesive surfaces on both sides, one surface bonding to the kinesiology tape layer 712 of multi-use portion 704 and the other surface bonding to kinesiology tape island layer 716. In general, double-sided tape 714 may be engineered to be skin-friendly and easily replaceable to maintain stable attachment and user comfort.

[0136] Kinesiology tape island layer 716 may form part of single-use portion 706 and may be configured to manage movement of wires and fabric within tape system 702. For instance, kinesiology tape island layer 716 may comprise discrete islands of tape strategically placed beneath electronics 710 to prevent external factors, such as fabric movement or user activity, from affecting sensor readings. In some examples, kinesiology tape island layer 716 may provide localized support and may be disposable after each use to preserve hygiene and sensor performance.

[0137] The user's skin 718 may represent the interface between the wearable device and the user. For example, single-use portion 706, including double-sided tape 714 and kinesiology tape island layer 716, may adhere securely to the user's skin 718 while minimizing discomfort and irritation. The interaction between tape system 702 and the user's skin 718 may play an important role in ensuring reliable sensor readings and overall device functionality.

[0138] FIG. 8 is a flow chart illustrating an example mode of operation. The techniques of FIG. 8 may be performed by one or more processors of a computing device, such as the system of FIG. 1 and / or computing device 210 illustrated in FIG. 2. For purposes of illustration only, the techniques of FIG. 8 are described within the context of computing device 210 of FIG. 2, although computing devices having configurations different than that of computing device 210 may perform the techniques of FIG. 8.

[0139] In accordance with the techniques described herein, communication module 220 controls the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable (802). Communication module 220 issues one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user (804). Communication module 220 receives one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user (806). Analysis module 222 analyzes the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user (808). Communication module 220 outputs, to an output component, the assessment of the back pain for the user (810).

[0140] Although the various examples have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

[0141] It is to be recognized that depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

[0142] In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and / or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium.

[0143] It is contemplated that the various aspects, features, processes, and operations from the various embodiments may be used in any of the other embodiments unless expressly stated to the contrary. Certain operations illustrated may be implemented by a computer executing a computer program product on a non-transient, computer-readable storage medium, where the computer program product includes instructions causing the computer to execute one or more of the operations, or to issue commands to other devices to execute one or more operations.

[0144] By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are instead directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0145] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated in a combined codec. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0146] The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a codec hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and / or firmware.

[0147] Various embodiments of the invention may be implemented at least in part in any conventional computer programming language. For example, some embodiments may be implemented in a procedural programming language (e.g., “C”), or in an object oriented programming language (e.g., “C++”). Other embodiments of the invention may be implemented as a pre-configured, stand-alone hardware element and / or as preprogrammed hardware elements (e.g., application specific integrated circuits, FPGAs, and digital signal processors), or other related components.

[0148] Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies.

[0149] Among other ways, such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the network (e.g., the Internet or World Wide Web). In fact, some embodiments may be implemented in a software-as-a-service model (“SAAS”) or cloud computing model. Of course, some embodiments of the invention may be implemented as a combination of both software (e.g., a computer program product) and hardware. Still other embodiments of the invention are implemented as entirely hardware, or entirely software.

[0150] While the various systems described above are separate implementations, any of the individual components, mechanisms, or devices, and related features and functionality, within the various system embodiments described in detail above can be incorporated into any of the other system embodiments herein.

[0151] The terms “about” and “substantially,” as used herein, refers to variation that can occur (including in numerical quantity or structure), for example, through typical measuring techniques and equipment, with respect to any quantifiable variable, including, but not limited to, mass, volume, time, distance, wavelength, frequency, voltage, current, and electromagnetic field. Further, there is certain inadvertent error and variation in the real world that is likely through differences in the manufacture, source, or precision of the components used to make the various components or carry out the methods and the like. The terms “about” and “substantially” also encompass these variations. The term “about” and “substantially” can include any variation of 5% or 10%, or any amount-including any integer-between 0% and 10%. Further, whether or not modified by the term “about” or “substantially,” the claims include equivalents to the quantities or amounts.

[0152] Numeric ranges recited within the specification are inclusive of the numbers defining the range and include each integer within the defined range. Throughout this disclosure, various aspects of this disclosure are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges, fractions, and individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6, and decimals and fractions, for example, 1.2, 3.8, 11 / 2, and 43 / 4 This applies regardless of the breadth of the range. Although the various embodiments have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

[0153] Various examples of the disclosure have been described. Any combination of the described systems, operations, or functions is contemplated. These and other examples are within the scope of the following claims.

Examples

Embodiment Construction

[0031]The following detailed description is exemplary in nature and is not intended to limit the scope, applicability, or configuration of the techniques or systems described herein in any way. Rather, the following description provides some practical illustrations for implementing examples of the techniques or systems described herein. Those skilled in the art will recognize that many of the noted examples have a variety of suitable alternatives.

[0032]FIG. 1 shows both front and back perspective views of one example of a wearable 102 in accordance with aspects of the present disclosure. As used herein, a wearable, such as wearable 102, may refer to any component that may contain sensors (e.g., sensors 106) that can be worn around or attached to a torso of a user, removed from the user, and then reapplied at a later time, including an undergarment, an article worn on the torso, kinesiology tape systems, or some other type of lightweight garment worn on a torso of the user. In some e...

RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 676,752, filed Jul. 29, 2024, the entire contents of which are incorporated herein by reference.TECHNICAL FIELD

[0002] The disclosure relates to health and fitness monitoring.BACKGROUND OF THE INVENTION

[0003] Back pain is the world's leading cause of disability. 80% of adults in the United States will experience lower back pain in their lives, and 15-19% of adults in the United States will experience thoracic back pain in their lives. More than 50% of people with back pain spend most of their workdays sitting down. However, existing systems and methods have numerous drawbacks.

[0004] Monitoring back movement is important for various uses. For example, monitoring back movement is critical for people with back pain and people providing back pain care to identify the back pain, to understand what is causing the back pain and identify a course of action to mitigate the back pain. However, many existing health or fitness devices that monitor back movement involve a user wearing bulky equipment, which can cause discomfort. This presents a challenge to adoption and use of such devices. For example, desk workers have not been targeted for back movement monitoring, because existing devices are either too obtrusive to be commercially viable and require a chest strap and / or generated for use over short periods of time.SUMMARY OF THE INVENTION

[0005] In general, the disclosure describes a wearable that collects various physiological data or signals from a wearer. As used herein, a wearable may refer to any component that may contain sensors that can be worn around or attached to a torso of a user, removed from the user, and then reapplied at a later time, including an undergarment, an article worn on the torso, kinesiology tape systems, or some other type of lightweight garment worn on a torso of the user. In some examples, the wearable may monitor back movement without an undergarment. The wearable may further collect data for monitoring movement of other body parts, and other physiological parameters.

[0006] For this system, it is appreciated that existing wearable fitness monitors do not provide the ability for continuous data collection over a period of time of interest in understanding back pain, thereby preventing continuous fitness monitoring and reliable analysis of physiological data. It is further appreciated that another challenge affecting adoption of fitness devices by people with back pain may be the lack of a vibrant and interactive online community to view physiological data and share with other users.

[0007] According to one aspect of the present disclosure, it is appreciated that a health or fitness monitor that includes bulky components may hinder continuous or prolonged wear.

[0008] Accordingly, one aspect of the present disclosure is directed to providing a health or fitness wearable that may not include bulky components, thereby making the wearable slimmer, unobtrusive and appropriate for continuous or prolonged wear. In some examples, the wearable may not include light emitting diodes (LEDs), or any other components to deliver visual cues. In other examples, the wearable may include a display or light emitting diodes (LEDs) so as to provide feedback to a user. The ability to continuously wear the wearable, or to wear the wearable for a prolonged amount of time, further allows continuous, substantially continuous (e.g., data collected once every millisecond, every ten milliseconds, every fifty milliseconds, every second, or any other periodic amount of time that provides accurate analysis of a user's motion), or periodic collection of physiological data, as well as more reliable health or fitness monitoring. For the purposes of this disclosure, “continuous” may mean truly continuous, substantially continuous (e.g., data collected once every millisecond, every ten milliseconds, every fifty milliseconds, every second, or any other periodic amount of time that provides accurate analysis of a user's motion), or reasonably periodic to collect meaningful data to properly assess back movement and pain for a user. For example, examples of the wearable disclosed herein will allow a person with back pain to monitor data at all times, not just during a physiotherapy session.

[0009] According to another aspect of the present disclosure, some examples of the wearable may have the ability to stream collected or measured data wirelessly to an online application, for example, using either cellular data, an internet connection, or a Bluetooth connection to a cellular phone.

[0010] Another aspect of the present disclosure is directed to providing an application for health and fitness monitoring. In some examples, the application may be a social networking site. The application may allow users, such as people with back pain, to monitor their own back pain-related data, share information with their friends and back pain care providers, compete with other users, and win prizes. A user may include an individual with back pain who is monitoring data related to their back pain, such as an individual wearing a wearable disclosed herein, a desk worker, a member of the public, a back pain care provider. In some examples, a user may pick their own back pain care provider from a list to comment on their data.

[0011] Another aspect of the present disclosure is directed to methods of health and fitness monitoring, including methods for analyzing physiological data, for example using data received from the wearable disclosed herein. Data may be reported to a user via an application, such as an example of the application disclosed herein.

[0012] In some examples, the application may be configured to provide an interactive user interface. The application may be configured to display results based on analysis on physiological data received from one or more devices. The application may be configured to provide competitive ways to compare one user to another, and ultimately a more interactive experience for the user. For example, in some examples, instead of merely comparing a user's physiological data and performance relative to that user's past performances, the user may be allowed to compete with other users and the user's performance may be compared to that of other users.

[0013] In some examples, the application may be a mobile application or a website. In some examples, the application may be configured to communicate data to other websites or applications.

[0014] Systems and methods for assessing back pain using wearable sensor technology are described. These systems include a wearable device configured to be worn on or around a user's torso, with one or more sensors attached to a back portion of the wearable. One or more processors are operably coupled to the sensors and configured to measure movement characteristics of the user's back, issue prompts to a computing device for input indicative of the user's psychological perception of pain level, receive user-provided pain perception data, analyze the movement characteristics together with the perception data to produce an assessment of back pain, and output the assessment via an output component.

[0015] The described systems offer several benefits, including continuous and unobtrusive monitoring of back movement and pain perception, integration of psychological factors into pain assessment, and the ability to provide personalized feedback and comparative metrics. The wearable device is designed for comfort and prolonged use, enabling reliable data collection and analysis. Wireless communication and onboard memory allow for seamless data transmission and storage, while artificial intelligence models enhance the accuracy and relevance of pain assessments.

[0016] The system may include sensors such as inertial measurement units to capture movement variability, range of motion, angular velocity, and movement patterns. Additional features may include a user-actuated button to initiate a timestamp, a photoresistor to detect when the wearable is not being worn and disable sensor power, onboard memory to store measurements when out of wireless range and upload them when connectivity is restored, and wireless communication capability to transmit assessments to a remote server over Internet or cellular networks. The processors may derive an aggregate activity level, compute a user score based on both movement and perception data, and generate tactile, audible, or visual notifications; activity indicators; leaderboards or group comparisons; graphical representations such as graphs, 2D models, or 3D avatars; and calendar-based performance comparisons via a graphical user interface. The wearable may take the form of an undergarment, shirt, vest, bodysuit, compression garment, sweatshirt, jacket, or one or more strips of kinesiology tape with dedicated multi-use and single-use portions.

[0017] In one example, the disclosure is directed to a system including a wearable to be worn on or around a torso of a user, one or more sensors attached to a back portion the wearable, and one or more processors. The one or more processors may be configured to control the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable. The one or more processors may further be configured to issue one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user. The one or more processors may also be configured to receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. The one or more processors may further be configured to analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. The one or more processors may also be configured to output, to an output component, the assessment of the back pain for the user.

[0018] In another example, a method is provided that includes supplying a wearable device with back-mounted sensors configured to be worn on a user's torso, controlling the sensors to measure movement characteristics of the user's back while the user wears the wearable, issuing prompts to a computing device to obtain user input indicative of psychological pain perception, receiving the user-provided perception data, analyzing the measured movement characteristics together with the perception data to produce an assessment of back pain, and outputting that assessment via an output component.

[0019] In another example, the disclosure is directed to a non-transitory computer-readable storage medium which may include instructions that, when executed by one or more processors, cause the processors to control back-mounted sensors on a wearable device to measure movement characteristics, issue prompts for psychological pain perception input, receive user input, analyze both movement and perception data to produce a back pain assessment, and output the assessment to an output component.

[0020] In another example, the application is directed to a system comprising a garment to be worn on or around a torso of a user, a flexible printed circuit board including a plurality of sensors attached to a back portion the garment, and one or more processors configured to control each of the plurality of sensors to measure one or more movement characteristics of a back of the user wearing the wearable, analyze the one or more movement characteristics of the back of the user to produce an assessment of back pain for the user, and output, to an output component, the assessment of the back pain for the user.

[0021] The details of one or more examples of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF DRAWINGS

[0022] The following drawings are illustrative of particular examples of the present disclosure and therefore do not limit the scope of the invention. The drawings are not necessarily to scale, though examples can include the scale illustrated, and are intended for use in conjunction with the explanations in the following detailed description wherein like reference characters denote like elements. Examples of the present disclosure will hereinafter be described in conjunction with the appended drawings.

[0023] FIG. 1 is a back perspective views of an example wearable according to aspects of the present disclosure.

[0024] FIG. 2 is a block diagram illustrating a more detailed example of a computing device configured to perform the techniques described herein.

[0025] FIG. 3 illustrates the wearable of FIG. 1 with an example of the materials and placement used in the wearable, in accordance with one or more aspects of the present disclosure.

[0026] FIG. 4 is a block diagram illustrating a wearable according to aspects of the present disclosure.

[0027] FIGS. 5A and 5B illustrate an exemplary application-based user interface of a wearable according to aspects of the present disclosure.

[0028] FIG. 6 illustrates an example of scenario where the wearable and application-based interface may be used, according to aspects of the present invention.

[0029] FIG. 7 illustrates an example of the wearable being a number of sensors both attachable to a user and protected from the outside environment using kinesiology tape, in accordance with one or more aspects of the present disclosure.

[0030] FIG. 8 is a flow diagram illustrating an example method for analyzing data indicative of back pain for a user, in accordance with one or more aspects of the present disclosure.DETAILED DESCRIPTION

[0031] The following detailed description is exemplary in nature and is not intended to limit the scope, applicability, or configuration of the techniques or systems described herein in any way. Rather, the following description provides some practical illustrations for implementing examples of the techniques or systems described herein. Those skilled in the art will recognize that many of the noted examples have a variety of suitable alternatives.

[0032] FIG. 1 shows both front and back perspective views of one example of a wearable 102 in accordance with aspects of the present disclosure. As used herein, a wearable, such as wearable 102, may refer to any component that may contain sensors (e.g., sensors 106) that can be worn around or attached to a torso of a user, removed from the user, and then reapplied at a later time, including an undergarment, an article worn on the torso, kinesiology tape systems, or some other type of lightweight garment worn on a torso of the user. In some examples, the wearable 102 may be sleek and lightweight, thereby making it appropriate for continuous or prolonged wear. In other examples, the wearable 102 may include an indicator such as a light-emitting diode (LED) display. Examples of the wearable 102 may be configured to offer different sizes for those with different heights and body types. Examples may be provided in different sizes to fit different user sizes. Examples of the wearable may be in any variety or shade of color based on user preference.

[0033] Wearable 102 may serve as a garment designed to be worn on or around the torso of a user, the garment providing structural support and housing for embedded electronics and sensors 106. For example, wearable 102 is configured to fit snugly against the user's body to ensure that the embedded components maintain close contact with target areas of the back, thereby minimizing movement artifacts and ensuring consistent sensor placement, both factors that significantly influence accurate data collection. In some embodiments, wearable 102 may be implemented as various types of garments, including but not limited to undergarments, shirts, vests, bodysuits, compression garments, sweatshirts, or jackets. In an alternative example, wearable 102 may also include strips of kinesiology tape that adhere directly to the user's skin (see FIG. 7) to provide additional support for the embedded components. The material of wearable 102 may be lightweight and unobtrusive, allowing for prolonged or continuous wear without causing discomfort. Accordingly, in certain examples, wearable 102 may be waterproof to enable use in various environmental conditions and to reduce the need for frequent removal.

[0034] Back motion monitor 104 may be integrated into wearable 102 and positioned to cover a target area on the user's back. For example, back motion monitor 104 facilitates measurement of back movement characteristics, such as movement variability, range of motion (ROM), angular velocity, longitudinal data, and movement patterns, by ensuring that sensors 106 embedded within wearable 102 remain securely positioned against the user's back. In some embodiments, back motion monitor 104 may include a flexible circuit board, such as a flexible printed circuit board, that houses sensors 106, thereby maintaining a slim and unobtrusive system profile. The design of back motion monitor 104 may be optimized to maintain sensor alignment and stability during various user activities (e.g., sitting, standing, walking, or exercising). As a result, back motion monitor 104 significantly contributes to enabling the system to analyze back movement and to provide insights into back pain assessment.

[0035] Sensor(s) 106 are embedded within back motion monitor 104 and strategically positioned to measure movement characteristics of the user's back. For example, sensors 106 may include inertial measurement units (IMUs) capable of detecting acceleration, angular velocity, longitudinal data, and orientation, wherein the IMUs utilize changes in capacitance between contacts to calculate acceleration and derive movement data for the target area of the back. In some embodiments, multiple sensors 106 are distributed across different regions of the back to capture comprehensive data, enabling analysis of movement patterns and variability across the entire spine. In an alternative example, sensors 106 may be housed within electronic islands on the flexible circuit board. In general, data collected by sensors 106 is transmitted to computing device 110 for further analysis, either via flexible wired communication or wireless communication.

[0036] Computing device 110 is connected to sensor(s) 106 and serves as the processing unit for the system. For example, computing device 110 controls sensors 106, receives collected movement data, and performs advanced analyses to assess back pain. In some embodiments, computing device 110 may be implemented as a microcontroller embedded within wearable 102 or as a communication unit configured to communicate with an external device (e.g., a smartphone, tablet, or computer). In an alternative example, when computing device 110 is communicating with an external device, computing device 110 may transmit data wirelessly via Bluetooth, cellular networks, internet / Wi-Fi, or other communication protocols. Accordingly, computing device 110 may be equipped with any one or more of one or more processors, memory components, and communication modules to execute system functions or to provide data to other external devices to perform those functions. In some embodiments, computing device 110 applies artificial intelligence (AI) models to analyze movement data and psychological input from the user, producing a comprehensive assessment of back pain. Furthermore, computing device 110 may issue prompts requesting user input indicative of psychological perception of pain levels and integrate this data with movement characteristics for enhanced analysis. The results of the analysis are output to the user via an application or other output components, thereby providing actionable insights and recommendations for back pain management.

[0037] In accordance with one or more techniques of this disclosure, computing device 110 (or an external device in communication with computing device 110) may control the one or more sensors 106 to measure one or more movement characteristics of a back of the user wearing the wearable. Computing device 110 (or an external device in communication with computing device 110) may issue one or more prompts to a user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user. Computing device 110 (or an external device in communication with computing device 110) may receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. Computing device 110 (or an external device in communication with computing device 110) may analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. Computing device 110 (or an external device in communication with computing device 110) may output, to an output component, the assessment of the back pain for the user.

[0038] As shown in FIG. 1, the wearable may include a relatively small head portion configured to provide various functions such as data collection and streaming functions of the wearable. In some examples, the wearable may include a button underneath a portion of the fabric of the wearable. In some examples, the button may be held to initiate a time stamp and held again to end a time stamp, which may be transmitted to an application as a time stamp. Time stamp information may be used, for example, as an activity indicator to alert when periods of specific activity begin. In some examples, the wearable may be waterproof so that users never need to remove it, thereby further allowing for continuous wear.

[0039] The wearable may include a back motion monitor. Thus, the wearable may include a motion sensor portion of the wearable which covers a target area on the back. The wearable may be configured such that when a user wears the wearable, the fabric sits tightly against the user's body, so the sensors of the wearable are secured flush / close to the target area of the back. A tight fit and flush placement of the motion sensor on the target area of the back allow measurement of back movement. In some examples, back movement might be taken using motion sensors all directly in contact with the user's back. The motion sensors may include an inertial measurement unit (IMU). As the user moves, the capacitance between contacts in the IMU changes and will be used to calculate the acceleration of the IMU. The acceleration will be used to calculate the movement of the target area of the user's back. The outlined process will be carried on multiple area on the user's back. The data from each sensor will be combined and analyzed to gain insight into the user's back pain.

[0040] In some examples, the wearable may be configured to record back movement, stress, and other physiological metrics.

[0041] In some examples, the wearable may further be configured such that a button on the outside portion may be pressed, thus triggering the device to begin one or more of collecting data, calculating metrics and communicating the information to a network. In some examples, a photoresistor may be used to indicate whether the user is wearing the wearable or not. In some examples, power from the one or more of the IMUs may be cut off as soon as there is a spike in the signal, and reset once the user has put the wearable back on their body.

[0042] The system of FIG. 1 may be an IoT system including of a wearable which tracks back motion and, using inertial measurement units (IMUs), provides users personalized advice / suggestions to relieve their back pain, using artificial intelligence (AI). The sensors may be connected to a microcontroller via embedded circuitry and the data may transmitted using to a computing device, such as a smartphone, via Bluetooth or any other adequate communication protocol. The data is displayed on the smartphone in an app. This data is used to provide personalized back pain care. We measure acceleration data and then process it using proprietary algorithms to build proprietary metrics.

[0043] IMUs are strategically embedded onto different parts of the wearable so that they lie on parts of the back relevant to back pain. IMUs are designed on a flexible circuit board which is embedded / attached to the wearable. This design may be thin.

[0044] In essence, the wearable is one portion of the system described herein, and the sensors in the wearable measure a number of movement characteristics, including movement variability. Movement variability is an indication of how variable a patient's spine movement is over a period of time e.g. a working day or during sleep.

[0045] The sensors in the wearable may also measure range of motion (ROM) of any part of the spine. For instance, the one or more sensors may measure a lumbar ROM, which is usually measured from disc T12 or L1. However, in other instances, any segment of the spine, or the entirety of the spine, may be analyzed for ROM characteristics throughout use.

[0046] The sensors in the wearable may also measure angular velocity of any part of the spine. This could be a lumbar angular velocity, or angular velocity of any segment of the spine, or the entirety of the spine.

[0047] The sensors in the wearable may also measure movement patterns in the spine. The system may be looking for patterns of movement which indicate the wearer is guarding themselves from pain. Computing device 110 may use angular velocity and ROM to decide whether a pattern of movement is indicative of guarding, and the system will track and describe to the user how they are currently moving, and why it shows they are guarding themselves from pain. This may be represented as a 2d, side-on view of their spine, or even as a 3d digital model / avatar.

[0048] On the app, the user is prompted to answer questions about their back pain to provide a wider, contextual understanding on how back pain affects their life, and other factors. This is based on Cognitive Functional Therapy.

[0049] Back pain is related to psychology. One model of care, Cognitive Functional Therapy (CFT) has been proposed to address the psychological causes of back pain, although the techniques described herein can be used with any model of care connecting psychological causes of back pain with back pain perception levels and references to CFT are by example only. CFT has been shown to be significantly better than traditional physiotherapy. However, it is expensive to train CFT practitioners, and the sessions are time-consuming. This techniques of this disclosure use AI to deliver a technology-only platform which delivers a version of CFT to overcome the barriers of delivering CFT at scale. The technology may be used to support practitioners or as a stand-alone system.

[0050] Psychological perception of pain has been strongly linked to back pain and is believed to be a cause of chronic pain, and CFT is the most successful proposed model of care to take psychological perception of pain into account. Technology has not yet been used to implement CFT, meaning a) the proliferation of CFT practice around the globe has been slow, and b) the collection of a large dataset on the psychological assessment of pain has been inhibited. We are addressing this problem by collecting information about psychological pain perception in conjunction with movement information. For instance, the below Table 1 indicates potential psychological factors that could affect how a patient perceives pain, prompts that could be asked that are associated with that particular psychological factor, and examples of replies that could indicate a higher perception or awareness of the pain, potentially exacerbating the effects of the pain experienced by the user:TABLE 1Example Psychological Factors, Interview Prompts, and Negative RepliesPsychologicalFactorsInterview PromptsExamples of RepliesCognitive factors (thoughts about pain and coping with pain)Cause / meaningWhat do you think isThere is something damaged.the cause of the pain?ConsequencesWhere do you seeI will always have a weakness thatyourself in the future?I need to protect. It will get worseas I get older.VigilanceHow much is yourI can't stop thinking about the pain.mind on your pain?How confident are youI have no confidence in my back.with your back?Self-efficacyHow confident are youI have no confidence to play withto do the things in lifemy kids.that you value?PainHow has the painI can't garden, work, or socializeinterference / disabilityimpacted your life?because of my pain.How do you cope withThere is nothing that I can do foryour pain?my pain.Coping with painHave you avoidedI avoid anything that hurts myimportant activities orback.modified the way youI always protect my back when Ido them because ofliftyour pain?Catastrophic thoughtsWhat do you think willI fear my back is going to break.happen if you bendyour back?Where do you seeI fear I am going to end up in ayourself in the future?wheelchair.Emotional factors (feelings about pain)Emotional response toHow does the painIt's so intense I can't think.painmake you feel?How does the painI panic when I get the pain andaffect you emotionally?become hopeless about getting outof it.AnxietyDo you worry aboutThe pain makes me feel anxious allthe pain?the time. I worry it won't getbetter.Depressed moodDoes it get you down?I am in a dark place; I have lostIn what way?hope, and I see no way out.Frustration / angerDoes the pain makeI feel so frustrated and angry thatyou feel frustrated?this has happened to me.What is it thatfrustrates you?Influence of emotionsDoes how you feelMy pain gets worse when I amon pain(mood, worry, stress,stressed / anxious / down / tired.fatigue) influence yourpain?Fear of damageHow do you feel whenEvery time I bend I am terrified Iyou bend and lift?will prolapse my disk.Fear of painHow do you feel aboutI am just frightened of the pain andthe pain?the suffering.When I get the pain I can't do whatI need for hours.Pain predictabilityDoes the pain feelI can't predict it.predictable to you?Pain controllabilityDo you feel in controlI have little control over my pain.of the pain? Are therethings you can do tocontrol your pain?

[0051] It is to be recognized that the above examples of psychological factors, interview prompts, and negative replies are only examples, and other psychological factors, interview prompts, and replies could be utilized by the system described herein. For instance, other psychological factors not listed above could be analyzed, different prompts could be issued to test the listed psychological factors, and other replies to the prompts may be deemed negative. The above list is not exhaustive and only provided as one example of the factors, prompts, and replies that could be used by this system.

[0052] Back pain is a widespread issue, recognized as a leading cause of disability globally, affecting a substantial portion of the population. Despite the prevalence of back pain, existing systems and methods for monitoring and managing back pain face numerous challenges. Conventional wearable devices designed to monitor back movement are often bulky, obtrusive, and uncomfortable, making them impractical for continuous or prolonged use. These devices frequently rely on chest straps or other cumbersome components, which discourage adoption, particularly among desk workers or individuals requiring extended monitoring. Additionally, current systems do not adequately incorporate psychological factors into the assessment of back pain, despite growing evidence that psychological perceptions, such as anxiety, depression, and catastrophic thoughts, significantly influence the experience and management of chronic pain. The absence of an integrated approach combining physiological and psychological data reduces the effectiveness of existing solutions in delivering actionable insights for back pain care.

[0053] The present system addresses these shortcomings by introducing a lightweight, unobtrusive wearable configuration designed for continuous or prolonged use. The wearable incorporates one or more sensors, such as inertial measurement units (IMUs), strategically placed on the back portion of the wearable to measure movement characteristics, including movement variability, range of motion, angular velocity, longitudinal data, and movement patterns. These sensors are embedded in a flexible circuit design, ensuring a slim profile that enhances user comfort and wearability. The system further integrates advanced algorithms and artificial intelligence (AI) models to analyze the collected movement data in conjunction with user-provided psychological inputs. Prompts issued to a computing device associated with the user gather data on psychological perceptions of pain, such as emotional responses, catastrophic thoughts, and coping mechanisms. By combining physiological metrics with psychological factors, the system produces a comprehensive assessment of back pain, offering personalized insights and recommendations.

[0054] Additionally, the described system leverages modern connectivity features, such as Bluetooth and cellular communication, to enable seamless data transmission to a computing device or remote server. This facilitates real-time analysis and feedback, as well as the ability to store data locally when the wearable is out of range. The system also supports interactive user interfaces, including mobile applications and web platforms, which allow users to visualize their back movement and pain data through graphs, 2D or 3D digital avatars, and calendar-based comparisons. Social and competitive features, such as leaderboards and group performance metrics, further enhance user engagement and motivation.

[0055] By addressing both the physiological and psychological dimensions of back pain, the described system represents a notable advancement over previous approaches. This system enhances the accuracy and reliability of back pain assessments while broadening access to Cognitive Functional Therapy (CFT) principles through technology, addressing challenges such as cost and scalability. The combination of AI-driven analysis, modern wearable design, and interactive user interfaces provides a thorough and accessible solution for managing and alleviating back pain.

[0056] The subject matter described herein is directed to a practical application of technology for assessing and managing back pain through a combination of wearable sensor systems, data analysis, and user interaction. The techniques described herein include a specific and concrete implementation involving physical components, such as a wearable device, sensors, processors, and communication units, configured to collect, process, and transmit physiological and psychological data. The system utilizes tangible hardware to measure movement characteristics of a user's back, issues prompts to obtain psychological pain perception data, analyzes the combined data using advanced algorithms and artificial intelligence models, and outputs actionable assessments via output components.

[0057] This integration of hardware and software produces a technical improvement in the field of health and fitness monitoring by enabling continuous, unobtrusive, and personalized assessment of back pain. The invention solves longstanding problems associated with bulky, impractical monitoring devices and the lack of psychological data integration in pain assessment. The described system provides a specific technological solution that enhances the accuracy, reliability, and accessibility of back pain management, and is capable of real-world use in clinical, occupational, and personal health settings.

[0058] Accordingly, the techniques described herein are directed to a process, machine, manufacture, or composition of matter, and it provides a technical solution to a technical problem using concrete and tangible components. The techniques described herein are rooted in technological improvements and do not preempt any abstract idea, law of nature, or natural phenomenon. Instead, the techniques described herein apply specific technological tools to achieve a useful result.

[0059] The techniques described herein include a wearable system for assessing back pain that uniquely integrates continuous physiological monitoring with psychological pain perception analysis. Unlike conventional devices, the system features a lightweight, unobtrusive wearable, such as a garment or kinesiology tape strips, embedded with one or more sensors positioned on the back to measure movement characteristics including variability, range of motion, angular velocity, longitudinal data, and movement patterns. The system's processors are configured not only to collect and analyze this movement data, but also to issue prompts to a user's computing device to gather input on psychological factors affecting pain, such as emotional responses, anxiety, depression, and catastrophic thoughts.

[0060] One aspect of the current disclosure is the combination of real-time sensor data with user-reported psychological perceptions, enabling a comprehensive assessment of back pain that reflects both physical and mental dimensions. The system further incorporates advanced features such as artificial intelligence models for data analysis, wireless communication for seamless data transfer, and interactive user interfaces for feedback, visualization, and social engagement. The modular design, including multi-use and single-use tape portions for secure and hygienic sensor placement, enhances usability and comfort for prolonged wear.

[0061] By addressing the limitations of other devices, such as bulkiness, lack of psychological integration, and limited data accessibility, the techniques described herein provides a scalable and user-friendly solution for back pain management. This concept enables continuous, personalized, and actionable insights for users and care providers, representing a significant advancement in the field of health and fitness monitoring.

[0062] An example real-life implementation of this system may involve an office worker named Alex who experiences chronic lower back pain due to prolonged periods of sitting at a desk. Alex receives a wearable device in the form of a lightweight compression garment embedded with multiple inertial measurement units (IMUs) strategically positioned along the back portion of the garment. The wearable also includes a button for manual input and a photoresistor to detect when the garment is being worn.

[0063] Each morning, Alex puts on the garment before heading to work. Throughout the day, the sensors continuously measure movement characteristics such as range of motion, angular velocity, longitudinal data, and movement variability as Alex sits, stands, and moves around the office. The wearable streams this data wirelessly to Alex's smartphone via Bluetooth. If Alex steps away from the phone, the garment's onboard memory stores the data until connectivity is restored.

[0064] At regular intervals, the companion mobile application issues prompts to Alex, asking questions about pain levels and psychological factors, such as “How confident are you with your back today?” or “Has your pain affected your mood or activities?” Alex responds to these prompts, providing valuable context about psychological perceptions of pain.

[0065] The system's processors analyze both the sensor data and Alex's responses using proprietary algorithms and artificial intelligence models. The application then generates a personalized assessment of Alex's back pain, highlighting correlations between movement patterns and psychological factors. The results are displayed in the app as graphs, a 3D digital avatar of Alex's spine, and calendar-based comparisons to track progress over time.

[0066] If the assessment indicates increased pain or risk factors, the app may send a tactile or visual notification, suggesting posture adjustments or short exercises. Alex can also view a leaderboard comparing his activity and pain scores with other users, join support groups, and share data with a physical therapist for remote monitoring.

[0067] At the end of the week, Alex reviews his performance and pain trends, gaining actionable insights to improve his posture, activity levels, and psychological well-being. The system's unobtrusive design and integrated feedback loop empower Alex to manage his back pain proactively, both at work and at home, demonstrating the practical benefits and versatility of the disclosed invention.

[0068] FIG. 2 is a block diagram illustrating a more detailed example of a computing device configured to perform the techniques described herein. FIG. 2 illustrates only one particular example of computing device 210, and many other examples of computing device 210 may be used in other instances and may include a subset of the components included in example computing device 210 or may include additional components not shown in FIG. 2.

[0069] Computing device 210 may be any computer with the processing power required to adequately execute the techniques described herein. For instance, computing device 210 may be any one or more of a mobile computing device (e.g., a smartphone, a tablet computer, a laptop computer, etc.), a desktop computer, a smarthome component (e.g., a computerized appliance, a home security system, a control panel for home components, a lighting system, a smart power outlet, etc.), an integrated computer system, a distributed computer system, a vehicle, a wearable computing device (e.g., a smart watch, computerized glasses, a heart monitor, a glucose monitor, smart headphones, etc.), a virtual reality / augmented reality / extended reality (VR / AR / XR) system, a video game or streaming system, a network modem, router, or server system, or any other computerized device that may be configured to perform the techniques described herein. For the purposes of this disclosure, computing device 210 may be integrated into a wearable system, such as in computing device 110 or electronics 710, or may be an external computing device in communication with computing device 110 or electronics 710 to receive data from the wearable system.

[0070] As shown in the example of FIG. 2, computing device 210 includes user interface components (UIC) 212, one or more processors 240, one or more communication units 242, one or more input components 244, one or more output components 246, and one or more storage components 248. UIC 212 includes display component 202 and presence-sensitive input component 204. Storage components 248 of computing device 210 include communication module 220, analysis module 222, and data store 226, which may store artificial intelligence models, activity history, prompt information, and any other data utilized by computing device 210.

[0071] One or more processors 240 may implement functionality and / or execute instructions associated with computing device 210 to perform physiological and psychological assessments of a person's pain levels. That is, processors 240 may implement functionality and / or execute instructions associated with computing device 210 to track data recorded by sensors and answers to prompts to assess back pain.

[0072] Examples of processors 240 include any combination of application processors, display controllers, auxiliary processors, one or more sensor hubs, and any other hardware configured to function as a processor, a processing unit, or a processing device, including dedicated graphical processing units (GPUs). Modules 220 and 222 may be operable by processors 240 to perform various actions, operations, or functions of computing device 210. For example, processors 240 of computing device 210 may retrieve and execute instructions stored by storage components 248 that cause processors 240 to perform the operations described with respect to modules 220 and 222. The instructions, when executed by processors 240, may cause computing device 210 to perform physiological and psychological assessments of a person's pain levels.

[0073] Communication module 220 may execute locally (e.g., at processors 240) to provide functions associated with managing user interfaces, controlling sensors, and communicating between computing devices (such as in distributed systems). In some examples, communication module 220 may act as an interface to a remote service accessible to computing device 210. For example, communication module 220 may be an interface or application programming interface (API) to a remote server that manages user interfaces, controlling sensors, and communicating between computing devices (such as in distributed systems).

[0074] In some examples, analysis module 222 may execute locally (e.g., at processors 240) to provide functions associated with analyzing the physiological and psychological data measured from the user to provide an assessment of back pain for the user. In some examples, analysis module 222 may act as an interface to a remote service accessible to computing device 210. For example, analysis module 222 may be an interface or application programming interface (API) to a remote server that analyzes the physiological and psychological data measured from the user to provide an assessment of back pain for the user.

[0075] One or more storage components 248 within computing device 210 may store information for processing during operation of computing device 210 (e.g., computing device 210 may store data accessed by modules 220 and 222 during execution at computing device 210). In some examples, storage component 248 is a temporary memory, meaning that a primary purpose of storage component 248 is not long-term storage. Storage components 248 on computing device 210 may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.

[0076] Storage components 248, in some examples, also include one or more computer-readable storage media. Storage components 248 in some examples include one or more non-transitory computer-readable storage mediums. Storage components 248 may be configured to store larger amounts of information than typically stored by volatile memory. Storage components 248 may further be configured for long-term storage of information as non-volatile memory space and retain information after power on / off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage components 248 may store program instructions and / or information (e.g., data) associated with modules 220 and 222 and data store 226. Storage components 248 may include a memory configured to store data or other information associated with modules 220 and 222 and data store 226.

[0077] Communication channels 250 may interconnect each of the components 212, 240, 242, 244, 246, and 248 for inter-component communications (physically, communicatively, and / or operatively). In some examples, communication channels 250 may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

[0078] One or more communication units 242 of computing device 210 may communicate with external devices via one or more wired and / or wireless networks by transmitting and / or receiving network signals on one or more networks. Examples of communication units 242 include a network interface card (e.g., such as an Ethernet card), an optical transceiver, a radio frequency transceiver, a GPS receiver, a radio-frequency identification (RFID) transceiver, a near-field communication (NFC) transceiver, or any other type of device that can send and / or receive information. Other examples of communication units 242 may include short wave radios, cellular data radios, wireless network radios, as well as universal serial bus (USB) controllers.

[0079] One or more input components 244 of computing device 210 may receive input. Examples of input are tactile, audio, and video input. Input components 244 of computing device 210, in one example, include a presence-sensitive input device (e.g., a touch sensitive screen, a PSD), mouse, keyboard, voice responsive system, camera, microphone or any other type of device for detecting input from a human or machine. In some examples, input components 244 may include one or more sensor components (e.g., sensors 252). Sensors 252 may include one or more biometric sensors (e.g., fingerprint sensors, retina scanners, vocal input sensors / microphones, facial recognition sensors, cameras), one or more location sensors (e.g., GPS components, Wi-Fi components, cellular components), one or more temperature sensors, one or more movement sensors (e.g., accelerometers, gyros), one or more pressure sensors (e.g., barometer), one or more ambient light sensors, and one or more other sensors (e.g., infrared proximity sensor, hygrometer sensor, and the like). Other sensors, to name a few other non-limiting examples, may include a radar sensor, a lidar sensor, a sonar sensor, a heart rate sensor, magnetometer, glucose sensor, olfactory sensor, compass sensor, or a step counter sensor.

[0080] One or more output components 246 of computing device 210 may generate output in a selected modality. Examples of modalities may include a tactile notification, audible notification, visual notification, machine generated voice notification, or other modalities. Output components 246 of computing device 210, in one example, include a presence-sensitive display, a sound card, a video graphics adapter card, a speaker, a cathode ray tube (CRT) monitor, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic LED (OLED) display, a virtual / augmented / extended reality (VR / AR / XR) system, a three-dimensional display, or any other type of device for generating output to a human or machine in a selected modality.

[0081] UIC 212 of computing device 210 may include display component 202 and presence-sensitive input component 204. Display component 202 may be a screen, such as any of the displays or systems described with respect to output components 246, at which information (e.g., a visual indication) is displayed by UIC 212 while presence-sensitive input component 204 may detect an object at and / or near display component 202.

[0082] While illustrated as an internal component of computing device 210, UIC 212 may also represent an external component that shares a data path with computing device 210 for transmitting and / or receiving input and output. For instance, in one example, UIC 212 represents a built-in component of computing device 210 located within and physically connected to the external packaging of computing device 210 (e.g., a screen on a mobile phone). In another example, UIC 212 represents an external component of computing device 210 located outside and physically separated from the packaging or housing of computing device 210 (e.g., a monitor, a projector, etc. that shares a wired and / or wireless data path with computing device 210).

[0083] UIC 212 of computing device 210 may detect two-dimensional and / or three-dimensional gestures as input from a user of computing device 210. For instance, a sensor of UIC 212 may detect a user's movement (e.g., moving a hand, an arm, a pen, a stylus, a tactile object, etc.) within a threshold distance of the sensor of UIC 212. UIC 212 may determine a two or three-dimensional vector representation of the movement and correlate the vector representation to a gesture input (e.g., a hand-wave, a pinch, a clap, a pen stroke, etc.) that has multiple dimensions. In other words, UIC 212 can detect a multi-dimension gesture without requiring the user to gesture at or near a screen or surface at which UIC 212 outputs information for display. Instead, UIC 212 can detect a multi-dimensional gesture performed at or near a sensor which may or may not be located near the screen or surface at which UIC 212 outputs information for display.

[0084] In accordance with the techniques described herein, communication module 220 may control the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable. Communication module 220 may issue one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user, such as for over a same period of time as the measured movement characteristics. Communication module 220 may receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. Analysis module 222 may analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. Communication module 220 may outputs, to an output component, the assessment of the back pain for the user.

[0085] Computing device 210 and any of its components may further be configured to perform any methods or techniques described throughout this disclosure.

[0086] In accordance with one or more techniques of this disclosure, communication module 220 may control the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable. In some instances, the one or more sensors include one or more inertial measurement units. The one or more movement characteristics may include any one or more of movement variability, range of motion, angular velocity, longitudinal data, and movement patterns of the back of the user. The wearable, in some instances, may be any of an undergarment, a shirt, a vest, a bodysuit, a compression garment, a sweatshirt, or a jacket.

[0087] In other instances, the wearable may include one or more strips of kinesiology tape that adhere to the torso of the user, the one or more strips of kinesiology tape housing the one or more sensors. In such instances, the wearable may include a multi-use portion and a single use portion. The multi-use portion may include the one or more sensors housed between a first strip of kinesiology tape and a second strip of kinesiology tape. The single use portion may include a strip of double-sided tape and one or more kinesiology tape islands. A first side of the double-sided tape adheres to the multi-use portion, and at least a portion of a second side of the double-sided tape adheres to skin of the user.

[0088] Communication module 220 may issue one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user. The one or more prompts may include interview prompts associated with at least one psychological factor selected from the group consisting of cognitions, emotional responses, anxiety, depression, and catastrophic thoughts.

[0089] Communication module 220 may receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user. Analysis module 222 may analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user. In some instances, analysis module 222 may apply an artificial intelligence model to the one or more movement characteristics and the user input to produce the assessment of back pain.

[0090] Communication module 220 may output, to an output component, the assessment of the back pain for the user. In some instances, communication module 220 may additionally output, via the output component, one or more of a notification selected from the group consisting of a tactile notification, an audible notification, or a visual notification in response to the assessment of back pain, an activity indicator marking periods of specific activity based on user input or sensor data, a graphical user interface of a leaderboard or group performance metric comparing the user's assessment to other users, a graphical representation of back movement and pain data, including at least one of a graph, a 2D model, or a 3D digital avatar of the user's spine, and a graphical user interface of calendar-based comparisons of the user's performance over different time periods.

[0091] In some instances, the wearable further includes a button disposed on an outer surface of the wearable. In such instances, communication module 220 may initiate a time stamp in response to user actuation of the button. In other instances, communication module 220 may, in response to actuation of the button, initiate at least one of collecting movement data, calculating physiological metrics, or communicating information to a network.

[0092] In some instances, the wearable further includes a photoresistor configured to detect whether the wearable is being worn by the user. Communication module 220 may disable power to the one or more sensors when the wearable is not worn.

[0093] In some instances, analysis module 222 may derive an aggregate activity level for the user based on the one or more measured movement characteristics over a period of time.

[0094] In some instances, storage components 247 may store measured movement characteristics when the wearable is out of wireless range of an external computing device, and communication module 220 may upload the stored movement characteristics to the external computing device when the wearable returns into range.

[0095] In some instances, communication module 220 may transmit the assessment of back pain to a remote server via an internet connection or a cellular connection.

[0096] In some instances, analysis module 222 may compute a user score based on the one or more movement characteristics and the data indicative of the psychological perception of the pain level. In such instances, communication module 220 may output the user score for display via the output component.

[0097] While shown as a single computing device, computing device 210 may be a distributed computing system, with other computing devices having similar components or certain portions of the components described in FIG. 2. In such instances, different portions of the actions performed by computing device 210 may be performed by different computing devices in the distributed computing system. For instance, a first computing device integrated into a wearable may control the sensors to gather the movement data and send that data to a second computing device, such as a smartphone, which issues the prompts to the user, analyzes all of the received data, and outputs the assessment.

[0098] FIG. 3 illustrates the wearable of FIG. 1 with an example of the materials and placement used in the wearable, in accordance with one or more aspects of the present disclosure. Garment 320, as depicted in FIG. 3, may be worn on or around the torso of a user 650. In general, garment 320 serves as the foundational structure for a clothing version of the wearable system described herein, and garment 320 is configured to house and support various components, such as sensors 106 and computing device 110, that enable the monitoring of back movement and the assessment of back pain. For example, garment 320 may be tailored to fit snugly against the user's body, thereby ensuring that sensors 106 maintain close contact with the back for accurate data collection.

[0099] Garment 320 may be implemented in various forms, including but not limited to an undergarment, shirt, vest, bodysuit, compression garment, sweatshirt, or jacket. In some embodiments, the material of garment 320 is selected to provide comfort during prolonged wear while maintaining durability and flexibility. For instance, garment 320 may be constructed from lightweight, breathable fabrics such as spandex, polyester, or a blend of synthetic and natural fibers. These materials enable garment 320 to conform to the user's body shape, promoting a secure fit that minimizes movement of sensors 106 relative to the user's back.

[0100] In an alternative example, garment 320 may include additional design features to enhance the functionality of garment 320. For example, elastic bands or adjustable straps may be incorporated to accommodate users of different body sizes and shapes. Furthermore, reinforced sections or pockets may be provided to securely house sensors 106 and computing device 110, preventing displacement during movement. In some embodiments, garment 320 may be designed to be washable, with detachable or water-resistant components to protect embedded electronics.

[0101] The back portion of garment 320 is specifically configured to support the placement of sensors 106 that measure movement characteristics of the user's back. Sensors 106 may include inertial measurement units (IMUs) or other motion-sensing devices. The snug fit of garment 320 helps ensure that sensors 106 remain in close proximity to the user's back, enabling the accurate measurement of parameters such as movement variability, range of motion, angular velocity, longitudinal data, and movement patterns.

[0102] Garment 320 may also include integrated features such as a button or a photoresistor. For instance, the button, if present, may be disposed on the outer surface of garment 320 and used to initiate specific functions, such as starting data collection or creating a time stamp. In another example, the photoresistor may detect whether garment 320 is being worn by user 650 and enable the system to conserve power by disabling sensors 106 when garment 320 is not in use.

[0103] Garment 320 may also be designed to facilitate the integration of advanced features, such as wireless communication modules for transmitting data to a computing device or remote server. In some embodiments, garment 320 may include pathways or channels for routing wires or flexible circuits, ensuring that these components do not interfere with the user's comfort or the accuracy of the sensor readings. Garment 320 provides the structural and functional support required for the accurate and continuous monitoring of back movement and the assessment of back pain.

[0104] FIG. 4 shows a block diagram corresponding to one example of a wearable according to aspects disclosed herein. The wearable includes a system 400 configured to provide continuous collection and monitoring of physiological data. As described above under computer system and in conjunction with the exemplary example of FIG. 1, the system 400 includes a controller 402, such as a microcontroller, and multi-axis accelerometers 404A-404N. Still referring to FIG. 4, system 400 includes a link to computing device 410, which may have similar components to and function similarly to computing device 210 of FIG. 2. In some examples, the link may be a Bluetooth Low Energy link, but in other examples, the link may be any wired or wireless connection that enables the transmission of data between controller 402 and computing device 410. The Bluetooth Low Energy link may be configured to wirelessly communicate data to an external network. Some examples of the wearable may be configured to stream information wirelessly to a computing device 410. In some examples, data streamed from a user's wearable to an external network may be accessed by the user via an application. In some examples, analysis module 422 may apply various analytical functions, such as using AI model 428, to the data, stored in data store 426, before the data is displayed on the app GUI.

[0105] In some examples, a method for monitoring a health or fitness status may include providing an application. Aspects of the application may be implemented, for example, using one or more computer systems as previously described. In some examples, a system for health or fitness monitoring may include the wearable and one or more computer systems configured to generate an application for tracking health or fitness information. In some examples, the application may be provided to users of the wearable disclosed herein. The application may be configured to allow a user to create an account and to log in to their account. In some examples, the sign up process may include one or more of asking for name, nickname, email, home address, height, weight and more specific questions to assess the psychological parameters of a user, and a code associated with the wearable. In some examples of the wearable, the code may be provided as a label disposed on the interior of the wearable. In some examples, a code associated with a wearable may be used to register with the application. Once a user has subscribed to the application, all the recorded data may be available there for perusal. Additionally, the application may be configured to allow users to add information to their profiles. For example, users may add pictures, favorite activities, sports team(s), and may search for teammates / friends to share information with. This application may also communicate with other applications or servers from applications, such as existing health or activity monitoring applications, to get even more data for the pain analysis provided herein.

[0106] Referring again to FIG. 4, the network interface connecting controller 402 and computing device 410 may be configured such that data collected by the wearable may be streamed wirelessly. In some examples, data may be transmitted automatically, without the need to manually press any buttons. In some examples, the wearable may include a cellular chip built into the wearable. In one example, the network interface may be configured to stream data using Bluetooth technology. In another example, the network interface may be configured to stream data using a cellular data service, such as via a 3G, 4G, or 5G cellular network.

[0107] In some examples, a built in Bluetooth chip will allow the wearable to communicate with a cell phone or a computer in order to stream data to the application. The wearable may be further configured to store data when it is not within close proximity of a streaming outlet such as a cellular phone. The wearable may further be configured to send data to a cellular phone via Bluetooth when it is nearby a linked phone, and to the Internet via cellular data. This example may provide a convenient method of transmitting data based on utilizing existing cellular data plans.

[0108] In another example, the wearable may include a cellular communication chip that wirelessly uploads all metrics to the application without utilizing any other device. In one example, the chip may be a 20 chip that is relatively cheaper and uses less power, but transfers at a slower speed. In another example, a 30 chip may use negligibly more power compared with a 20 chip for almost twice the transfer speed, thus half the transfer time, using much less energy from the battery.

[0109] In some examples, the wearable may be configured to store fitness metrics or physiological data, such as in storage 418, until the fitness metrics or physiological data are uploaded. Data may be uploaded on a frequency suitable to save power while retaining ease of use.

[0110] System 400 further includes one or more sensors 404A-404N (collectively, “sensors 404”). As discussed above, the sensors 404 may include a motion sensor. In some examples, the wearable may further include one or more of movement variability, range of motion and activity monitors. Movement variability may be based on a user's back movement over time. A movement variability measurement may be improved if a user chooses to enter the beginning and end period of wear on the application. In some examples, manual entering of data may not be performed in order to derive movement variability; however, data entry may be used to improve the accuracy of the results. In some examples, if a user has forgotten to enter data, he or she can enter it for past weeks and the relevant metrics may be updated accordingly.

[0111] In some examples, the wearable may use the IMU data to calculate motion and calculate distance, whether it be in real terms as steps or miles or as a converted number.

[0112] Activity sensors may be used, for example, to classify or categorize activity, such as walking, running, performing another sport, standing, sitting, or lying down. In some examples, one or more of collected physiological data may be aggregated to generate an aggregate activity level. For example, back movement variability and range of motion may be used to derive an aggregate activity level. The level may have no absolute meaning, but may be compared to or evaluated relative to previous recordings of the user's aggregate activity level, as well as the aggregate activity levels of other users.

[0113] In some examples, the aggregate activity level may correspond to points, rewards or other incentives provided to a user. In one example, points, rewards or other incentives may be stored in a computer system and accessed via an application, such as the application described above. A currency as used herein may include points, rewards, money or other incentives. In some examples, a method of health or fitness monitoring may include deriving a currency based on health or fitness performance metrics. In some examples, currency may be used to unlock new features of the application which will allow users, for example, to compare metrics with more advanced athletes. For example, currency may be used to unlock special, elite trainers, such as acclaimed physiotherapists. In some examples, currency may be used to promote rank and status in the application. The application may be configured to include different classes, each class providing access to unique features. A user may be classified based on how much currency has been acquired by the user. In some examples, the application may be configured such that currency may be wagered for fitness challenges among users.

[0114] FIGS. 5A, 5B, and 6 show various examples of a wearable according to aspects disclosed herein. In some examples, a method for monitoring a health or fitness status may include providing an application configured to allow a user to view back movement and pain data in a graph.

[0115] For example, the application may be configured to allow with multiple users including individuals with back pain, individuals who want to prevent back pain, or back pain care givers to compare back movement metrics.

[0116] Some examples of the health or fitness monitoring method may include calculating one or more metrics or scores based on collected physiological data such as movement quality, movement variability, and psychological parameters. Psychological parameters may be assessed using a questionnaire where the user inputs the answers via the app. In other examples, the method may include calculating an overall score based on one or more of movement quantity, movement variability, self-assessment of pain and comparison to other users with similar physiological metrics.

[0117] The method may further include providing the ability for users to create groups using the application. A group may have a group owner and one or more members. A group may have one or more of a message board, calendar upload, photos, a group performance metric and average metrics of group individuals. In some examples, the calendar upload may allow users to compare past performance to current performance with regard to time of week, month and year. The calendar upload may allow a group owner, such as a coach, to input indicators of what type or level of physical exertion may be performed.

[0118] Some examples of the method may include providing a “Leaderboard” for listing best performing users in a feed of updates from users that a current user is connected to.

[0119] Graphical user interface 502 may comprise a digital display element configured to present posture analytics to the user 650 in a visually intuitive and interactive manner. For example, graphical user interface 502 may be part of an application associated with the wearable 102 system or tape system 702 described herein. In general, graphical user interface 502 is designed to provide a summary of the user's 650 posture quality, which is derived from movement characteristics measured by sensors 106 integrated into the wearable 102. Specifically, the posture quality score, prominently displayed as a numerical value within a circular progress indicator, reflects the user's 650 overall posture performance over a defined period, such as a day. The circular progress indicator may be color-coded to provide immediate visual feedback, with different colors representing varying levels of posture quality (e.g., excellent, moderate, or poor).

[0120] Accordingly, beneath the posture quality score, graphical user interface 502 may include a time-series graph that illustrates the user's 650 posture quality throughout the day. This graph may be generated based on continuous or periodic data collected by the wearable's 102 sensors 106, such as inertial measurement units (IMUs), and analyzed by the system's processors 240. For instance, the graph may allow the user 650 to identify trends, fluctuations, and specific time periods during which posture quality was suboptimal. In this example, the x-axis of the graph represents time, while the y-axis represents the posture quality score, thereby enabling users 650 to correlate their posture performance with daily activities or routines.

[0121] Furthermore, graphical user interface 502 may include navigation options, such as “Tracking” and “Analytics,” which allow the user 650 to switch between different views or functionalities within the application. The “Tracking” option may provide real-time data on the user's 650 posture, whereas the “Analytics” option may offer detailed insights and historical data analysis. In some examples, the interface is designed to be user-friendly, ensuring that users 650 of varying technical proficiency can easily interpret and act upon the information presented.

[0122] Graphical user interface 504 may comprise another digital display element within the application, specifically tailored to present movement variability analytics to the user 650. Movement variability may be an important metric derived from the wearable's 102 sensor 106 data, representing the consistency or variability in the user's 650 back movements over time. Similar to graphical user interface 502, graphical user interface 504 may feature a circular progress indicator that prominently displays the movement variability score as a numerical value. For example, lower variability scores may indicate more consistent and controlled movements, while higher scores may suggest irregular or erratic movement patterns.

[0123] Accordingly, below the movement variability score, graphical user interface 504 may include a time-series graph that visualizes the user's 650 movement variability throughout the day. This graph may be generated by analyzing data collected by the wearable's 102 sensors 106—such as angular velocity, range of motion, and movement patterns—and processed by the system's processors 240. In an alternative example, the x-axis of the graph represents time, while the y-axis represents the movement variability score, thereby helping users 650 identify periods of high or low movement variability and correlate these patterns with specific activities or events.

[0124] Moreover, graphical user interface 504 may include the same navigation options, e.g., “Tracking” and “Analytics”, as graphical user interface 502, allowing users 650 to seamlessly switch between different views or functionalities. The interface may be optimized for clarity and ease of use, providing actionable insights that enable users 650 to understand how their movement variability impacts overall back health and to make informed decisions about their posture and activity levels. In general, graphical user interface 504 is configured to ensure that users 650 can effectively engage with the data and derive meaningful conclusions.

[0125] User 650 may be depicted seated at a workstation, representing an individual engaging in a typical sedentary activity such as working at a desk or using a computer. For example, user 650 is shown in a posture that emphasizes alignment of the torso, back, and limbs, which is relevant for monitoring back movement using back motion monitor 104 and assessing back pain. In general, the posture-related information collected from user 650 serves as a baseline for detecting deviations in back alignment during prolonged seated activities.

[0126] Wearable 102 is configured to be worn around the torso of user 650. The wearable 102 includes one or more sensors 106 positioned on a back portion of the device to measure movement characteristics of the user's back. For instance, these sensors 106 collect data on parameters such as movement variability, range of motion, angular velocity, and movement patterns, all of which are significant for analyzing back health. In some examples, the sensors 106 may be inertial measurement units (IMUs) or strain gauges, each operable to detect slight changes in back posture and motion.

[0127] The sensor data may be transmitted to computing device 110, which may analyze this data or forward the data to an external computing device for further processing. For example, an external computing device may receive prompts generated based on sensor-derived movement metrics and may provide input interfaces related to the psychological perception of pain. In an alternative example, the external computing device may display graphical feedback to user 650 and record subjective pain ratings, thereby contributing to a comprehensive assessment of back pain that integrates both biomechanical and perceptual data.

[0128] FIG. 7 illustrates an example of the wearable being a number of sensors both attachable to a user and protected from the outside environment using kinesiology tape, in accordance with one or more aspects of the present disclosure. Tape system 702 may represent a layered structure that integrates multiple portions to form a wearable device configured for secure adhesion to a user's skin 718 while housing electronics 710 for physiological monitoring. For example, tape system 702 may provide a foundation for both functionality and comfort by combining reusable and single-use components in a modular arrangement. In general, tape system 702 is constructed to be lightweight and unobtrusive, enabling prolonged wear without discomfort, and to ensure that electronics 710 remain securely housed and properly aligned with the user's body.

[0129] Multi-use portion 704 may comprise a reusable section of tape system 702 configured to house important components of the wearable device. For instance, multi-use portion 704 may include a first kinesiology tape layer 708, electronics 710, and a second kinesiology tape layer 712 stacked to form a cohesive unit. In some examples, multi-use portion 704 may be designed to endure repeated use by embedding electronics 710 between flexible, breathable tape layers, thereby providing protection and structural stability while conforming to the user's movements.

[0130] Single-use portion 706 may comprise a disposable section of tape system 702 configured to adhere directly to the user's skin 718. For example, single-use portion 706 may include double-sided tape 714 and kinesiology tape island layer 716 arranged to provide secure attachment and to facilitate hygienic replacement after each use. In an alternative example, single-use portion 706 may be replaced upon removal of the wearable device, thereby maintaining optimal adhesion characteristics and preventing interference with the embedded electronics 710.

[0131] Kinesiology tape layer 708 may serve as the uppermost layer of multi-use portion 704, providing a protective and supportive element for electronics 710. For instance, kinesiology tape layer 708 may be fabricated from a breathable, stretchable material that conforms to the user's back and accommodates natural movement without impeding sensor performance. In some examples, kinesiology tape layer 708 may also contribute to the overall adhesion of tape system 702, ensuring that the multi-use portion 704 remains securely positioned.

[0132] Electronics 710 may be embedded within multi-use portion 704 and may represent primary functional components of the wearable device. For example, electronics 710 may include one or more inertial measurement units (IMUs) and associated circuitry for capturing movement characteristics of the user's back. In general, electronics 710 may be strategically placed between kinesiology tape layers 708 and 712 to minimize mechanical stress and to ensure accurate data acquisition, and may communicate with external computing devices via wired or wireless protocols for real-time analysis and feedback.

[0133] Electronics 710 may include one or more processors configured to analyze the movement characteristics measured by the sensors embedded within the wearable. These processors may execute instructions to process raw sensor data, derive relevant metrics such as movement variability, range of motion, angular velocity, longitudinal data, and movement patterns, and generate an assessment of back pain for the user. Additionally or alternatively, electronics 710 may include one or more communication units, such as wireless transceivers, that are configured to transmit the measured movement characteristics and assessment data to a secondary computing device, for example, a smartphone, tablet, or remote server. The communication units may utilize protocols such as Bluetooth, Wi-Fi, or cellular connectivity to enable real-time or periodic data transfer, thereby facilitating further analysis, user feedback, or integration with application-based interfaces.

[0134] Kinesiology tape layer 712 may be positioned beneath electronics 710 within multi-use portion 704 to serve as a secondary protective layer. For instance, kinesiology tape layer 712 may be fabricated from a flexible, breathable material that provides a stable base for electronics 710, preventing displacement during use. In some examples, kinesiology tape layer 712 may enhance the structural integrity of multi-use portion 704 and contribute to the durability of the wearable device under repeated application.

[0135] Double-sided tape 714 may be incorporated into single-use portion 706 to provide adhesive coupling between multi-use portion 704 and kinesiology tape island layer 716. For example, double-sided tape 714 may include adhesive surfaces on both sides, one surface bonding to the kinesiology tape layer 712 of multi-use portion 704 and the other surface bonding to kinesiology tape island layer 716. In general, double-sided tape 714 may be engineered to be skin-friendly and easily replaceable to maintain stable attachment and user comfort.

[0136] Kinesiology tape island layer 716 may form part of single-use portion 706 and may be configured to manage movement of wires and fabric within tape system 702. For instance, kinesiology tape island layer 716 may comprise discrete islands of tape strategically placed beneath electronics 710 to prevent external factors, such as fabric movement or user activity, from affecting sensor readings. In some examples, kinesiology tape island layer 716 may provide localized support and may be disposable after each use to preserve hygiene and sensor performance.

[0137] The user's skin 718 may represent the interface between the wearable device and the user. For example, single-use portion 706, including double-sided tape 714 and kinesiology tape island layer 716, may adhere securely to the user's skin 718 while minimizing discomfort and irritation. The interaction between tape system 702 and the user's skin 718 may play an important role in ensuring reliable sensor readings and overall device functionality.

[0138] FIG. 8 is a flow chart illustrating an example mode of operation. The techniques of FIG. 8 may be performed by one or more processors of a computing device, such as the system of FIG. 1 and / or computing device 210 illustrated in FIG. 2. For purposes of illustration only, the techniques of FIG. 8 are described within the context of computing device 210 of FIG. 2, although computing devices having configurations different than that of computing device 210 may perform the techniques of FIG. 8.

[0139] In accordance with the techniques described herein, communication module 220 controls the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable (802). Communication module 220 issues one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user (804). Communication module 220 receives one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user (806). Analysis module 222 analyzes the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user (808). Communication module 220 outputs, to an output component, the assessment of the back pain for the user (810).

[0140] Although the various examples have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

[0141] It is to be recognized that depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

[0142] In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and / or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium.

[0143] It is contemplated that the various aspects, features, processes, and operations from the various embodiments may be used in any of the other embodiments unless expressly stated to the contrary. Certain operations illustrated may be implemented by a computer executing a computer program product on a non-transient, computer-readable storage medium, where the computer program product includes instructions causing the computer to execute one or more of the operations, or to issue commands to other devices to execute one or more operations.

[0144] By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are instead directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0145] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated in a combined codec. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0146] The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a codec hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and / or firmware.

[0147] Various embodiments of the invention may be implemented at least in part in any conventional computer programming language. For example, some embodiments may be implemented in a procedural programming language (e.g., “C”), or in an object oriented programming language (e.g., “C++”). Other embodiments of the invention may be implemented as a pre-configured, stand-alone hardware element and / or as preprogrammed hardware elements (e.g., application specific integrated circuits, FPGAs, and digital signal processors), or other related components.

[0148] Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies.

[0149] Among other ways, such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the network (e.g., the Internet or World Wide Web). In fact, some embodiments may be implemented in a software-as-a-service model (“SAAS”) or cloud computing model. Of course, some embodiments of the invention may be implemented as a combination of both software (e.g., a computer program product) and hardware. Still other embodiments of the invention are implemented as entirely hardware, or entirely software.

[0150] While the various systems described above are separate implementations, any of the individual components, mechanisms, or devices, and related features and functionality, within the various system embodiments described in detail above can be incorporated into any of the other system embodiments herein.

[0151] The terms “about” and “substantially,” as used herein, refers to variation that can occur (including in numerical quantity or structure), for example, through typical measuring techniques and equipment, with respect to any quantifiable variable, including, but not limited to, mass, volume, time, distance, wavelength, frequency, voltage, current, and electromagnetic field. Further, there is certain inadvertent error and variation in the real world that is likely through differences in the manufacture, source, or precision of the components used to make the various components or carry out the methods and the like. The terms “about” and “substantially” also encompass these variations. The term “about” and “substantially” can include any variation of 5% or 10%, or any amount-including any integer-between 0% and 10%. Further, whether or not modified by the term “about” or “substantially,” the claims include equivalents to the quantities or amounts.

[0152] Numeric ranges recited within the specification are inclusive of the numbers defining the range and include each integer within the defined range. Throughout this disclosure, various aspects of this disclosure are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges, fractions, and individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6, and decimals and fractions, for example, 1.2, 3.8, 11 / 2, and 43 / 4 This applies regardless of the breadth of the range. Although the various embodiments have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

[0153] Various examples of the disclosure have been described. Any combination of the described systems, operations, or functions is contemplated. These and other examples are within the scope of the following claims.

Examples

Embodiment Construction

[0031]The following detailed description is exemplary in nature and is not intended to limit the scope, applicability, or configuration of the techniques or systems described herein in any way. Rather, the following description provides some practical illustrations for implementing examples of the techniques or systems described herein. Those skilled in the art will recognize that many of the noted examples have a variety of suitable alternatives.

[0032]FIG. 1 shows both front and back perspective views of one example of a wearable 102 in accordance with aspects of the present disclosure. As used herein, a wearable, such as wearable 102, may refer to any component that may contain sensors (e.g., sensors 106) that can be worn around or attached to a torso of a user, removed from the user, and then reapplied at a later time, including an undergarment, an article worn on the torso, kinesiology tape systems, or some other type of lightweight garment worn on a torso of the user. In some e...

Claims

1. A system comprising:a wearable to be worn on or around a torso of a user;one or more sensors attached to a back portion the wearable; andone or more processors configured to:control the one or more sensors to measure one or more movement characteristics of a back of the user wearing the wearable;issue one or more prompts to a computing device associated with the user, wherein the one or more prompts request input indicative of a psychological perception of a pain level of the user;receive one or more indications of user input in response to the one or more prompts, wherein each of the one or more indications of user input provide data indicative of the psychological perception of the pain level of the user;analyze the one or more movement characteristics of the back of the user and the data indicative of the psychological perception of the pain level of the user to produce an assessment of back pain for the user; andoutput, to an output component, the assessment of the back pain for the user.

2. The system of claim 1, wherein the one or more sensors comprise one or more inertial measurement units.

3. The system of claim 1, wherein the one or more movement characteristics include at least one of movement variability, range of motion, angular velocity, longitudinal data, and movement patterns of the back of the user.

4. The system of claim 1, further comprising a button disposed on an outer surface of the wearable, wherein the one or more processors are further configured to initiate a time stamp in response to user actuation of the button.

5. The system of claim 1, wherein the wearable further comprises a photoresistor configured to detect whether the wearable is being worn by the user, and wherein the one or more processors are further configured to disable power to the one or more sensors when the wearable is not worn.

6. The system of claim 1, wherein the one or more processors are further configured to derive an aggregate activity level for the user based on the one or more measured movement characteristics over a period of time.

7. The system of claim 1, further comprising a memory storing the measured movement characteristics when the wearable is out of wireless range of the computing device, wherein the one or more processors are further configured to upload the stored movement characteristics when the wearable returns into range.

8. The system of claim 1, wherein the one or more processors are further configured to transmit the assessment of back pain to a remote server via an internet connection or a cellular connection.

9. The system of claim 1, wherein the one or more processors are further configured to apply an artificial intelligence model to the one or more movement characteristics and the user input to produce the assessment of back pain.

10. The system of claim 1, wherein the one or more prompts comprise interview prompts associated with at least one psychological factor selected from the group consisting of cognitions, emotional responses, anxiety, depression, and catastrophic thoughts.

11. The system of claim 1, wherein the one or more processors are further configured to compute a user score based on the one or more movement characteristics and the data indicative of the psychological perception of the pain level, and to display the user score via the output component.

12. The system of claim 1, wherein the wearable comprises a garment selected from the group consisting of an undergarment, a shirt, a vest, a bodysuit, a compression garment, a sweatshirt, or a jacket.

13. The system of claim 1, wherein the wearable comprises one or more strips of kinesiology tape that adhere to the torso of the user, the one or more strips of kinesiology tape housing the one or more sensors.

14. The system of claim 13, wherein the wearable further comprises a multi-use portion and a single use portion.

15. The system of claim 14, wherein the multi-use portion comprises the one or more sensors housed between a first strip of kinesiology tape and a second strip of kinesiology tape.

16. The system of claim 14, wherein the single use portion comprises a strip of double-sided tape and one or more kinesiology tape islands, wherein a first side of the double-sided tape adheres to the multi-use portion, and wherein at least a portion of a second side of the double-sided tape adheres to skin of the user.

17. The system of claim 1, wherein the wearable comprises a button disposed on an outer surface, and the one or more processors are further configured to, in response to actuation of the button, initiate at least one of collecting movement data, calculating physiological metrics, or communicating information to a network.

18. The system of claim 1, wherein the one or more processors are further configured to output, via the output component, one or more of:a notification selected from the group consisting of a tactile notification, an audible notification, or a visual notification in response to the assessment of back pain,an activity indicator marking periods of specific activity based on user input or sensor data,a graphical user interface of a leaderboard or group performance metric comparing the user's assessment to other users,a graphical representation of back movement and pain data, including at least one of a graph, a 2D model, or a 3D digital avatar of the user's spine, anda graphical user interface of calendar-based comparisons of the user's performance over different time periods.

19. A method of assessing back pain in a user, comprising:providing a wearable configured to be worn on or around a torso of the user, the wearable comprising one or more sensors attached to a back portion of the wearable;controlling the one or more sensors to measure one or more movement characteristics of a back of the user while the user is wearing the wearable;issuing one or more prompts to a computing device associated with the user, the prompts requesting input indicative of a psychological perception of a pain level of the user;receiving, at the computing device, one or more indications of user input in response to the prompts, each indication providing data indicative of the psychological perception of the pain level of the user;analyzing, by one or more processors, the measured movement characteristics and the data indicative of the psychological perception of the pain level to produce an assessment of back pain for the user; andoutputting, to an output component, the assessment of the back pain for the user.

20. A system comprising:a garment to be worn on or around a torso of a user;a flexible printed circuit board including a plurality of sensors attached to a back portion the garment; andone or more processors configured to:control each of the plurality of sensors to measure one or more movement characteristics of a back of the user wearing the wearable;analyze the one or more movement characteristics of the back of the user to produce an assessment of back pain for the user; andoutput, to an output component, the assessment of the back pain for the user.

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