Patent Application
20230039042
It is well known that athletes, whether professional or otherwise, are subject to injuries resulting from over-exertion, improper training, or insufficient training or conditioning. In some cases, such injuries are preceded with signs of fatigue. When a trainer (e.g., an athletic trainer or coach) detects signs of fatigue, the trainer can intervene to reduce the likelihood of fatigue-related injury. For example, when a trainer detects fatigue, the trainer may instruct the athlete to slow down or focus on technique or the trainer may pull the athlete from a game or a practice session for rest and recovery. Additionally or alternatively, the trainer may provide recommended exercise that is typically less strenuous than the normal exercise. Such observation, assessment, and intervention are typically provided based on the trainer's intimate long-term knowledge about a specific athlete and intuition built on years of experience and training.
Measurement of activity during periods of physical exertion may include measurement of heart rate by heart rate monitors, for example, as wrist-borne electronic peripherals. Heart rate alone, however, does not provide insight into cardiorespiratory or neuromuscular fatigue, which are the two main types of fatigue experienced by athletes during activity. Instead, heart rate is typically used as a heuristic to guide the athlete, for example, against benchmarks for endurance or training intensity that are reinforced by expert feedback from a trainer.
There currently exists no system or method for determination of neuromuscular work done by an athlete during a period of exertion. Similarly, quantification or algorithmic determination of neuromuscular fatigue, a useful indicator of training efficacy and injury risk, is currently unavailable.
The foregoing aspects and many of the attendant advantages of this invention will become more readily appreciated with reference to the following detailed description, when taken in conjunction with the accompanying drawings, where:
Inventive technology is directed to determining neuromuscular work done by an athlete during a period of exertion, also encompassing quantification or algorithmic determination of neuromuscular fatigue. In the context of this application, the term athlete encompasses professional and amateur athletes, as well as hobbyists, people who exercise, either regular or irregular basis, and others who engage in sports or exercise. All such categories of people (professional, amateur, consumers, etc.) are referred to as “athletes” in this application for simplicity and brevity.
In some embodiments, the athlete's uniform or other exercise clothing may be equipped with suitable sensors and/or data acquisition controllers that collect and interpret muscle activity data (e.g., muscle amplitude and frequency, heart rate, etc.). Such sensors may measure electrical impulses of the muscles representing muscle activity data. Collected data may be algorithmically processed to indicate muscle amplitude and/or frequency for one or more muscle groups of the user. In some embodiments, the algorithmic processing may include artificial intelligence and/or machine learning.
In some embodiments, determination of neuromuscular work done by an athlete during a period of exertion may be based on data collected using a wearable sensor platform incorporating the sensors and/or data acquisition controllers. Similarly, quantification or algorithmic determination of neuromuscular fatigue and/or cardiovascular fatigue, a useful indicator of training efficacy and injury risk, may be provided by computer systems (e.g., servers, client computing devices, and/or edge devices) in communication with the wearable sensor platform. Algorithmic approaches may include rules-based procedural models, heuristic models, object models, or machine-learning models, developed for the athlete and/or the activity being monitored.
Collectively, neuromuscular and cardiorespiratory monitoring may provide improved quantitative and/or qualitative training and assessment of performance, attainment of training targets, and/or fatigue assessment. In many embodiments, monitoring of neuromuscular work, as well as cardiorespiratory work, may protect an athlete from fatigue or injury, while being significantly more cost effective than conventional methods where the athlete is repeatedly evaluated by an expert, such as a trainer, physical therapist, or nutritionist.
System Overview
The muscle monitor 105 shown in
One or more computing devices 206 can be configured to individually or collectively carry out the functions of the performance tracker 102 (
Computing Devices
The CPU 331 can be a single processing unit or multiple processing units in a device or distributed across multiple devices. The CPU 331 can be coupled to other hardware components via, e.g., a bus, such as a PCI bus or SCSI bus. Other hardware components can include communication components 332, such as a wireless transceiver (e.g., a WiFi or Bluetooth transceiver) and/or a network card. Such communication components 332 can enable communication over wired or wireless (e.g., point-to point) connections with other devices. A network card can enable the computing device 301 to communicate over the network 208 (
The CPU 331 can have access to a memory 333. The memory 333 includes volatile and non-volatile components which may be writable or read-only. For example, the memory can comprise CPU registers, random access memory (RAM), read-only memory (ROM), and writable non-volatile memory, such as flash memory, hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tape drives, device buffers, and so forth. The memory 333 stores programs and software in programming memory 334 and associated data (e.g., configuration data, settings, user options or preferences, etc.) in data memory 335. The programming memory 334 contains an operating system 336, local programs 337, and a basic input output system (BIOS) 338, all of which can be referred to collectively as general software 339. The operating system can include, for example, Microsoft Windows™, Apple iOS, Apple OS X, Linux, Android, and the like. The programming memory 334 also contains other programs and software 340 configured to perform various operations. The various programs and software can be configured to process the real-time data 107 of the athlete 111 (
Clothing and Sensors
Referring to
In some embodiments, the clothing 445 may also be equipped with electrocardiogram (ECG) sensors 423a, orientation sensors 423c (e.g., a gyroscope), and acceleration sensors 423d (e.g., an accelerometer). Orientation sensors 423c and/or acceleration sensors 423d may be carried by the athlete's feet, for example, by being integrated and/or attached to the shoes of the athlete. The sensors 423 can be connected to the controller 125 using thin, resilient flexible wires (not shown) and/or conductive thread (not shown) woven into the clothing 445. The gauge of the wire or thread can be selected to optimize signal integrity and/or reduce electrical impedance.
The sensors 423a and 423b can include dry-surface electrodes distributed throughout the athlete's clothing 445 and positioned to make skin contact beneath the clothing along predetermined locations of the body. The fit of the clothing can be selected to be sufficiently tight to provide continuous skin contact with the individual sensors, allowing for accurate readings, while still maintaining a high-level of comfort, comparable to that of traditional compression fit shirts, pants, and similar clothing. In various embodiments, the clothing 445 can be made from compressive fit materials, such as polyester and other materials (e.g., Elastaine) for increased comfort and functionality. In some embodiments, the controller 125 and the sensors 423 can have sufficient durability and water-resistance so that they can be washed with the clothing 445 in a washing machine without causing damage. In these and other embodiments, the presence of the controller 125 and/or the sensors 423 within the clothing 445 may be virtually unnoticeable to the athlete. In one aspect of the technology, the sensors 423 can be positioned on the athlete's body without the use of tight and awkward fitting sensor bands. In the context of this application, the sensors 423 and the controller 125 are referred to as “wearable” components. In general, traditional sensor bands are typically uncomfortable for an athlete, and athletes can be reluctant to wear them.
In additional or alternate embodiments, the muscle monitor 105 (
Controller Communication
In operation, the controller 125 of the muscle monitor 105 is configured to process and packetize the data it receives from the sensors 423 (e.g., the muscle response sensors 423b). The controller 125 may broadcast the packetized data for detection by the gateway devices 204, which, in turn, forward the data to the muscle monitor 105 (1A) to produce analytics (e.g., frequency and amplitude of muscle activity).
Muscle Activity Indication
Neuromuscular work and a muscle activation quotient may be determined using muscle amplitude measurements by several techniques including, but not limited to, rules-based models, object models, or by machine learning or other predictive techniques. In this way, direct monitoring of muscle activity may be correlated to the muscle activation quotient, for example, as a percentage value of a maximum muscle amplitude. In an illustrative example, longitudinal data aggregation over multiple exercise or activity sessions, such as during a training camp or over a course or regimen, may permit generating multiple characteristic parameters for an athlete including, but not limited to, muscle group-specific activation windows, temporal characteristics from which the type of activity may be estimated automatically (e.g., without explicit human identification of the type of activity), or activity patterns that can be used to derive calibration data, as described in more detail in reference to
In some embodiments, one or more parameters of a rules-based algorithm, such as a heuristic model, are developed by longitudinal data collection of an athlete during a specific exercise. For example, for the endurance exercise illustrated in paired graph 500, activation curve 505 may be derived by inputting muscle activity measurements for multiple time points into a heuristic model developed for the athlete. In this way, the muscle activation may be determined as a percentage of a nominal, maximum, average, target, or other activity value.
Analogously, object models or machine-learning models may be implemented as part of the algorithms for determining neuromuscular work and muscle activation, as an approach to account for hidden parameters or interactions between parameters of training sessions that may be difficult to identify and capture in deterministic models. For example, confounding factors including, but not limited to, time of day, altitude, frequency of training or activity sessions, donning or doffing of weights, team composition, physiological cycles, or other factors, may interact to result in fatigue or injury in ways that are difficult to predict analytically. As an illustrative example, longitudinal muscle activity data for a variety of training conditions may be combined with environmental and other metadata to constitute a training set for an artificial neural network model, which may be used to train the neural network model to predict the muscle activation quotient in a way that accounts for the role of atmospheric pressure, humidity, temperature, running surface, or other factors.
Advantageously, determining neuromuscular work, also referred to as muscle load, can permit multiple types of activities to be compared quantitatively. For example, activities can be classified based at least in part on whether physical exertion moves the athlete (free-movement type) or whether physical exertion moves an object relative to the athlete (relative-movement type). Examples include running and lifting, respectively. For activities where the athlete moves, motion data can be used to generate an external load parameter, measuring the work done to move the athlete, which in turn can be used to compare between different activities of the same type. External load, in this context refers to parameters based at least in part on motion data that provide a dimensionless metric of physic exertion used to generate motion of the athlete (e.g., rate of change of acceleration, total acceleration over a predefined distance, etc.) For relative-movement exercises, such as lifting, motion of the wearer is limited, and the same external load parameter is less meaningful. To that end, one or more statistical models can be used to generate muscle load data, developed by fitting muscle data to external load data as an approach to generating a neuromuscular work value that can be used to compare different types of activities for which external load may be unavailable or less meaningful.
In some embodiments, statistical models for generating muscle load data can include regression models developed from combined motion and muscle activity data for multiple athletes. By fitting the regression models to point data, using a linear trend line defined by equating muscle load with external load (e.g., muscle load=external load) as a goal, motion data can be used to fit models that permit muscle activity sensor data to be used to generate measures of neuromuscular work. Similarly, muscle activity data can be used to determine external load. One fitted, muscle load models can be used to generate a dedimensionalized parameter of neuromuscular work, as part of training, fatigue monitoring, and athletic performance assessment.
In an illustrative example, the regression model can include a square root of the sum of six EMG sensor signals, provided that the sum can be modified with model coefficients developed during regression fitting. In another example, the regression model can include an average (e.g., mean) of six EMG sensor signals, provided that the average can be modified with model coefficients developed during regression fitting. In another example, the regression model can include a square root of the average (e.g., mean) of six EMG sensor signals, provided that the average can be modified with model coefficients developed during regression fitting. In another example, the regression model can include model coefficients that are a function of the average (e.g., mean) of six EMG sensor signals. While these illustrative examples include six sensors, the number of sensors can be greater or fewer, depending on the muscle(s) being measured, the type of activity, or the size of the wearer.
In some embodiments, qualitative and/or quantitative information may be derived from the muscle activation quotient by defining multiple muscle activation zones 510. For example, individual muscle activation zones 510 may correspond to contiguous ranges of values of the muscle activation quotient and may be defined in one or more ways relative to the muscle activation quotient. In some cases, defining the muscle activation zones 510 includes estimating a maximum muscle activation quotient 515 using the real time muscle activation curve 505 and defining the muscle activation zones 510 in relation to the maximum muscle activation quotient 515. While the maximum muscle activation quotient 515 is illustrated as being included in the real time muscle activation curve 505, in some embodiments, the maximum muscle activation quotient 515 may reach a fraction of a maximum value that is determined by longitudinal data collection. In this way, the muscle activation zones 510 may be defined and/or refined over multiple activity sessions. In some embodiments, the maximum muscle activation quotient 515 may reach about 95% or less, about 90% or less, about 85% or less, about 80% or less, about 75% or less, about 70% or less, about 65% or less, about 60% or less, about 55% or less, about 50% or less, about 45% or less, about 40% or less, about 35% or less, of the maximum value that is determined by longitudinal data collection. In some embodiments, the maximum muscle activation quotient 515 may be defined as part of training or recovery activity.
In some embodiments, muscle activity data may be collected at a sampling rate that is optimized to conserve one or more resources of the wearable sensor system. For example, in an endurance activity lasting several hours, the sensors may be active intermittently, such that the maximum muscle activation quotient 515 may occur between sampling points. In this way, estimating the maximum muscle activation quotient 515 may include interpolating one or more estimated muscle activation quotient datapoints between the sampling time points and identifying the maximum muscle activation quotient from the interpolated muscle activation quotient datapoints. Such approaches may provide improved performance and suitability of wearable sensor platforms for assessment of both cardiorespiratory and neuromuscular work and fatigue, for a broad range of activities, including activities that take an athlete away from sources of electricity or network connectivity.
The muscle activation zones 510 may be defined as a linear proportion of the maximum muscle activation quotient. Higher-order dynamics of physiological systems may be reflected by defining the muscle activation zones 510 by a natural-logarithmic proportion of the maximum muscle activation quotient. Similarly, a logarithmic proportion, a sigmoidal proportion, a parabolic proportion, or other converging functional proportion may be used to define the muscle activation zones 510.
Advantageously, defining muscle activation zones 510, such as those for which the range decreases as the muscle activation quotient increases, may permit discretization of physical exertion into one or more muscle activation zones as an approach to targeting particular levels of neuromuscular work. Such techniques may benefit the athlete, for example, by avoiding overwork that may result in muscle degradation, lactic acid buildup, or acute injury or strain. In this way, neuromuscular work may be managed in a manner akin to heart rate management.
The real time muscle activation curve 505 may be time-averaged over a dynamic averaging window, for example, over a portion of the period of time or over the entire period of time, from which a smoothed muscle activation curve 520 may be derived. Advantageously, smoothing the real time muscle activation curve 505 may provide improved qualitative and/or quantitative assessment or prediction of neuromuscular fatigue. For example, while real-time muscle activation data may include noise propagated from electromyography sensors, as well as rapid dynamics that may induce large changes in predictive models that include derivative terms, smoothed muscle activation data may be more intuitive for viewing on a display, and may provide more meaningful output from a model, for example, as a function of time. Smoothing may be applied to sensor data directly or may also be applied to muscle activation quotient data. For smoothed muscle activation quotient data, smoothing may reduce the likelihood that system analytics (e.g., analytics 110 of
In conjunction with the muscle activation quotient, activity may be monitored to determine cardiorespiratory work and to predict and/or identify cardiorespiratory fatigue by measuring heart rate as a function of time. As shown in the paired graph 500, a heart rate curve 530 may be generated by monitoring the amplitude of sensor configured for measuring the pulse. For example, the heart rate may be measured by a wearable sensor (e.g., sensors 423 of
While cardiorespiratory work and neuromuscular work describe different anatomical systems, the muscle activation curves 505 and/or 520 and the heart rate curve 530 may exhibit similar dynamics under some conditions. For example, for cardio or other endurance exercise, fatigue and exertion may develop with similar dynamics over the course of an activity session (e.g., on the order of seconds, minutes, or hours). In an illustrative example, in a swimming session of one kilometer or more, both the muscle activation quotient and the heart rate may rise to an initial plateau over a period of time 540. This may correspond to a time during which the athlete is training within the target parameters, and both the muscle activation quotient and the heart rate are within target zones. Following the period of time 540, however, additional exertion may be observed.
Without additional information, however, the dynamics illustrated in the paired graph 500 are not necessarily indicative of fatigue. For example, the athlete may have transitioned to a higher intensity interval. In the example of the swimming session, the dynamics observed following the period of time 540 may be attributable to fatigue, or to changing stroke styles, swimming speed, or other aspects of the exertion. Inference of fatigue may depend, therefore, on reference information, such as calibration data or historical/longitudinal data provided to a model. In a simple example, were the heart rate and the muscle activation quotient to diverge, rather than track closely, a model may indicate fatigue if data indicate the dynamics are anomalous. In some embodiments, multiple periods of time 540 may be defined to assess the time spent in each muscle activation zone 510. Advantageously, measures of time spent in each muscle activation zone 510 may improve training efficacy, for example, by providing analogous impact to timed cardio training targeting specific heart rate ranges.
The paired graph 600 describes, as a function of time (e.g., in seconds), a muscle activation quotient curve 605 and a time-averaged curve 620, where the muscle activation quotient is expressed in terms of fraction (e.g., a percentage value) of a comparison value including, but not limited to, a target value, a maximum value, or an average value. The muscle activation quotient, presented on the ordinate axis (e.g., y-axis), may be divided into one or more muscle activation zones 610, which may be determined as described for muscle activation zones 510 of
Advantageously, the same or similar quantitative feedback using the muscle activation quotient curve may improve training efficacy or athletic performance, for example, by permitting the athlete to target particular activation zones 610, to identify after how many repetitions the athlete starts to overwork, to guide incrementation of weight.
While time spent in each muscle activation zone 610 may be relatively less meaningful for the muscle activation curve 605, in contrast to the muscle activation cure 505 of
Concurrent monitoring of cardiorespiratory work, and determination of the athlete's heartrate, may supplement the neuromuscular work measurements and may provide additional insight into training efficacy and/or athletic performance. High-intensity repetitive exercise may exhibit a time-averaged effect on heart rate, which may be attributable at least in part to the autonomic function of the heart and circulatory system. In this way, a heart rate curve 630 generated for the athlete during the course of the activity session may trend upwards as the session progresses, corresponding to increased oxygen demand at the targeted muscle groups. In this way, divergent dynamics between the time-averaged curve 620 and the heart rate curve 630 may provide similar insight into fatigue or injury, for example, where the muscle activation quotient rises acutely without corresponding changes in heart rate. Additionally or alternatively, calibration data may be collected for the athlete and for the activity, by which anomalous dynamics in either curve may be identified, and neuromuscular fatigue and/or cardiorespiratory fatigue may be determined.
As an illustrative example, calibration curve 705 describes nominal time-averaged muscle activation data for an athlete during a particular high-intensity activity, such as sprinting or lifting. In this context, “nominal” describes peak or optimal performance of the monitored muscle group. In this way, the calibration curve 705 may describe a comparison standard for the athlete, as a reference for expected or “normal” performance. In comparison, a measured activation curve 720 may describe data collected during a particular activity session. While the calibration curve 705 may describe the expected or normal performance for the athlete, the measured activation curve 720 may correspond to an activity session following a competition, a series of strenuous training sessions, or an illness or injury. In this way, a differential quantity may be defined, referred to using the Greek letter delta “A,” which may be calculated as a function of time, for example, by a simple difference between the calibration curve 705 and the muscle activation curve 720. Additional or alternative techniques for determining the differential quantity may include, but are not limited to, weighting the difference as a function of time, using proportional, integrative, and/or derivative models to account for temporal dynamics in the data, inputting both the calibration data and the muscle activation data to a model to predict and/or classify fatigue and injury etc. As part of the visualizations described in reference to
In some embodiments, the differential quantity A is used as part of an algorithm for identifying fatigue and/or predicting fatigue or injury. For example, one or more fatigue or injury zones 740 may be defined, by which the magnitude of anomalies may be determined. The injury zones 740 may be defined using algorithmic outputs that account for the same or similar meta-parameters as described above in the context of determining muscle activation zones and/or muscle activation quotient. For example, deviation from “nominal” performance may be expected in some circumstances without rising to the level of being anomalous. Such circumstances may include, but are not limited to, changes in altitude, time since most recent activity session, recover from injury, etc. In this way, a model may be integrated into an algorithm to account for meta parameters, and to more accurately define the differential quantity A and the fatigue or injury zones 740.
Similarly, dynamics in the delta curve 730 as well as amplitude of the delta curve 730 may identify respective fatigue-type (e.g., neuromuscular vs. cardiovascular) and may improve differentiation between fatigue and injury. For example, crossing from a baseline into a fatigue or injury zone 740 may indicate fatigue, while crossing into the next fatigue or injury zone 740 may indicate injury, while crossing into the next fatigue or injury zone 740 may indicate severe injury. Similarly, temporal dynamics may provide insight into the progression of an activity session from nominal performance into a fatigue or injury zone 740. For example, differential terms describing dynamics in the delta curve 730 may indicate one or more inflection or stationary points 745 that may indicate transitions from nominal performance into a fatigue onset or injury onset phase. In this way, multiple types of quantitative and/or qualitative information may be derived from differential quantity data, using longitudinal calibration data, to improve training efficacy and/or athletic performance, to predict fatigue or injury, or to guide execution of exercises or other athletic activity.
The example method 800 starts at block 805. At block 810, certain muscle groups may be selected for observation. Some examples of such muscle groups are right quad (RQ) and left quad (LQ), right hamstring (RH) and left hamstring (LH), etc. Selecting the much group may include electronically selecting, for example, through a user interface, of a sensor channel corresponding to one or more wearable muscle response sensors positioned relative to the selected muscle group. Positioning, as described in more detail in reference to
Once selected, the muscle groups are monitored to collect muscle activity data. Muscle activity may describe nerve impulse signals generated by muscle flexion, for example, during motion of a body part or during isometric exertion. In this way, monitoring muscle activity may describe monitoring the amplitude of muscle activity of an athlete for multiple time points at least partially overlapping a period of physical exertion, such as a training session, athletic performance, or competition.
At block 815, the example method 800 includes generating one or more measures of neuromuscular work for the plurality of time points using the first amplitude. In some cases, generating the measure(s) may describe measuring the amplitude of the muscle by a first wearable muscle response sensor for multiple time points. The amplitude, in turn, may describe an electrical signal measured by an electromyography sensor positioned to monitor the muscle. The electromyography sensor may be a part of a wearable sensor system and may be an example of a wearable muscle response sensor configured for monitoring the amplitude of the muscle activity of the athlete.
Subsequent generating the measure(s) of neuromuscular work, the example method 800 may include determining one or more muscle activation quotients for the time points at block 820. As described in more detail in reference to
At block 825, the example method 800 may include defining one or more muscle activation zones for the athlete using the muscle activation quotient data generated at block 820. Individual muscle activation zones may correspond to contiguous ranges of values of the plurality of muscle activation quotients. As described in more detail in reference to
Subsequent defining the muscle activation zones, the example method 800 may include generating a visualization of the muscle activation quotient data and the muscle activation zones for the activity session at block 830. Generating the visualization may describe one or more operations associated with generating datasets in one or more graphical data formats, such as comma-separated value datasets or other data formats compatible with one or more visualization software applications. The visualization data may be generated for presentation on a display that is incorporated into a mobile electronic device, such as a mobile electronic device hosting software for implementing one or more operations of the example method 800.
In some embodiments, the example method 800 may optionally include determining a fatigue status at block 835. The fatigue status may describe neuromuscular fatigue, cardiorespiratory fatigue, and/or injury indication information. Determining the fatigue status may include defining a comparison between the muscle activation quotient data and a calibration curve for the athlete and/or for the activity and determining the fatigue status for the athlete using the comparison. In some embodiments, determining a fatigue status for the athlete may include defining a second comparison between heart rate data and a cardiorespiratory calibration curve for the athlete. In this way, cardiorespiratory fatigue, as well as neuromuscular fatigue, may be determined.
As described in more detail in reference to
In some embodiments, the example method may optionally include monitoring and collection of cardiorespiratory data at block 840, as part of a series of optional operations directed at generating quantitative and/or qualitative measures of cardiorespiratory work and fatigue. Blocks 840-860, therefore, may be executed concurrently or in parallel with the operations of blocks 810-830, such that real-time output of the example method 800 may include visualizations of both muscle activation data, heart rate, and fatigue/injury status on a display at block 865. Additional or alternative outputs of the example method 800 may include storing data collected or generated as part of the operations of the example method 800 in the database 836 or in other databases in communication with the computer system implementing the example method 800.
The operations of blocks 840-860, described in more detail in reference to
In some embodiments, determining fatigue status 835 and outputting visualization data to the display at block 865 may include operations for generating one or more prompts for a user of the system or the athlete donning the wearable sensor platform. The prompt(s) may include warning information indicative of fatigue or injury, determined, for example, when muscle activation quotient data exceeds a threshold value for safe exertion. The prompt may be present using the display, or may be provided by alternative modalities, such as an auditory prompt including the warning information, which may be generated using an acoustic speaker in electronic communication with the system (e.g., an integrated speaker in a smartphone or tablet).
While various advantages associated with some embodiments of the disclosure have been described above, other embodiments may also exhibit such advantages, and not all embodiments need necessarily exhibit such advantages to fall within the scope of the invention. For example, while various embodiments are described in the context of an athlete (e.g., a professional or collegiate athlete), in some embodiments users of the system can include novice or intermediate users, such as users, trainers, and coaches associated with a high school sports team, an athletic center, a professional gym, physical therapist, etc. Accordingly, the disclosure can encompass other embodiments not expressly shown or described herein. Unless otherwise noted, in the context of this disclosure, the word “approximately” indicates a difference of +/−5% of the stated value.
This application claims benefit of provisional application number U.S. 63/230,638 filed on Aug. 6, 2021, the contents of which are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63230638 | Aug 2021 | US |
