JP2020127743A - Computing system, method and program - Google Patents

Abstract

To provide systems and methods of determining an ergonomic assessment for a user.SOLUTION: Sensor data is received from one or more sensors 106 implemented in an ergonomic assessment garment 102 worn by a user. Corporeal data associated with at least one body segment of the user is determined based on the sensor data. The corporeal data is associated with a bend angle associated with the at least one body segment. An ergonomic assessment associated with the user is determined based on the corporeal data. The ergonomic assessment includes an indication of one or more ergonomic zones associated with the user, the one or more ergonomic zones being determined based on the bend angle associated with the at least one body segment.SELECTED DRAWING: Figure 2

Description

FIELD The present disclosure relates generally to determining an ergonomic rating associated with a user.

Background Current methods for measuring a person's posture and movement include coarse and imprecise methods (eg visual observation and estimation) or methods that involve invasive and cumbersome or awkward measurements (eg goniometers). ). While such an approach is sufficient in some cases, it can be inaccurate and/or difficult to implement. For example, obtaining posture and/or movement information associated with factory workers performing daily tasks can be difficult due to the cumbersome and awkward nature of the measurements used to obtain such information. is there.

Overview Aspects and advantages of embodiments of the present disclosure will be set forth in part in the description below. Alternatively, it may be learned from the description, or through practice of the embodiment.

One exemplary aspect of the disclosure is directed to a computer-implemented method of determining an ergonomic rating associated with a user. The method includes receiving sensor data by one or more computing devices from one or more sensors implemented on an ergonomic evaluation garment worn by a user. The method further includes determining, by at least one computing device, physical data associated with at least one physical segment of the user based at least in part on the sensor data. The body data is associated with a bend angle associated with at least one body segment. The method further includes determining an ergonomic rating associated with the user by the one or more computing devices based at least in part on the body data. The ergonomic assessment includes a display of one or more ergonomic zones associated with the user. The one or more ergonomic zones are determined based at least in part on a bend angle associated with the at least one body segment.

Other exemplary aspects of this disclosure are directed to systems, apparatus, tangible, non-transitory computer-readable media, user interfaces, memory devices, and electronic devices for determining an ergonomic rating for a user. ..

These and other features, aspects and advantages of various embodiments will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and serve to explain related principles together with the description.

BRIEF DESCRIPTION OF THE DRAWINGS A detailed description of embodiments directed to those of ordinary skill in the art is set forth in the specification with reference to the accompanying drawings.

FIG. 4 illustrates an exemplary system for determining an ergonomic rating for a user in accordance with exemplary aspects of the present disclosure. FIG. 3 illustrates an exemplary ergonomic evaluation garment according to an exemplary aspect of the present disclosure. FIG. 6 is a flow diagram of an exemplary method of determining an ergonomic rating according to an exemplary aspect of the present disclosure. FIG. 1 illustrates an exemplary system in accordance with exemplary aspects of this disclosure.

DETAILED DESCRIPTION Reference will now be made in detail to the embodiments. One or more examples are shown in the drawings. Each example is provided by way of explanation of the embodiments, not as a limitation of the disclosure. Indeed, it will be apparent to those skilled in the art that various modifications and changes can be made to the embodiments without departing from the scope or spirit of the disclosure. For example, features illustrated or described as part of one embodiment, can be used on another embodiment to yield a still further embodiment. As such, aspects of the disclosure are intended to cover such modifications and variations.

Example aspects of the disclosure are directed to determining ergonomic ratings using ergonomic rating garments that may be worn by a user. For example, sensor data may be received from one or more sensors implemented on an ergonomic evaluation garment worn by a user. Physical data associated with one or more body segments of the user may be determined based at least in part on the sensor data. The body data includes bending angle of the at least one body segment of the user, bending speed of the at least one body segment of the user, acceleration of bending of the at least one body segment of the user, duration of movement associated with the user, and/or Or it may include data associated with motion repeatability. An ergonomic rating may be determined based at least in part on the physical data. The ergonomic assessment may include an indication of one or more ergonomic zones in which the user is located, based at least in part on a bend angle associated with the at least one body segment.

More specifically, the ergonomic evaluation garment may be any suitable garment worn by the user and configured to monitor data associated with the user's movements and/or postures. In some implementations, the ergonomic evaluation garment may be upper body garments such as shirts, jackets, sweaters, and the like. Thus, the ergonomic evaluation garment may be as loose as a typical upper body garment and need not be a snug fit to the user. The ergonomic evaluation garment may be implemented with one or more sensor devices configured to monitor data associated with a user's movement and/or posture while being worn by the user. For example, the sensor may be integrated within the fabric of the ergonomic evaluation garment, or otherwise attached to the ergonomic evaluation garment. Such sensors are associated with one or more accelerometers, gyroscopes, inertial measurement units, force gauges, tachometers, electromyographic sensors, heart rate monitors, and/or user motion and/or posture. Other suitable sensors capable of measuring physiological data may be included.

In some implementations, the ergonomic evaluation garment can be a smart garment constructed using a plurality of electrically conductive threads. For example, electrically conductive threads can be woven into the fabric structure of a garment to form one or more circuits. In some implementations, the conductive threads may be combined with non-conductive threads to form an ergonomic evaluation garment. In such implementations, the garment may include a fabric having typical fabric texture, drape properties, and other properties used to make garments and the like. Thus, the conductive thread can be incorporated into the fabric of the ergonomic evaluation garment without unnecessarily increasing stiffness or imparting any other undesirable properties to the fabric.

In such an implementation, one or more sensors of the ergonomic evaluation garment can be coupled to one or more electrically conductive threads to form one or more circuits. For example, electrically conductive threads can be placed to electrically couple one or more sensors to one or more processing devices configured to implement various exemplary aspects of the present disclosure.

Sensor data may be used to determine physical data associated with a user. For example, such body data may include data associated with bend angles of one or more joints or body segments of the user. For example, the physical data may include data indicating bending angles of the user's shoulders, elbows, back, neck, knees, and the like. In some implementations, the body data is data associated with a range of movement of the one or more body segments, a velocity of movement of the one or more body segments, an acceleration of movement of the one or more body segments, And/or other suitable physical data associated with the user's movements and/or postures. In some implementations, the body data may include data indicating timing of bend angles for one or more body segments. In some implementations, the body data may include relative bend angles (eg, for one or more bend angle thresholds). In some implementations, the body data includes data indicating a number of times a particular body segment was bent at an angle greater than a corresponding bend angle threshold for that body segment, within a particular time period. obtain. Additionally or alternatively, the physical data may include behavioral data. Behavioral data may be indicative of work activity performed, productivity estimates (eg, number of towels folded per minute).

Such body data may be used in determining an ergonomic rating associated with the user. The ergonomic rating may include various attributes associated with the user's movements and/or postures. For example, a user's movement and/or posture may be categorized into one or more ergonomic zones associated with the user's posture and/or movement during one or more periods. The ergonomic zone can identify various qualities of a user's posture and/or movement during one or more periods. Each target body segment (eg, shoulder, back, knee, etc.) may have multiple associated ergonomic zones in which the movement and/or posture of the body segment may be categorized. The ergonomic zone may be defined based at least in part on a bend angle threshold associated with the target body segment. In some implementations, each zone may be defined based at least in part on the number of times the bend angle of the body segment of interest has risen above a threshold during one or more time periods. For example, the first zone may indicate that the bending angle of the subject body segment averaged less than once per minute (or other suitable period) during the measurement period and above the threshold. The second zone may indicate that the bending angle of the target body part was 1-2 times per minute on average over the threshold during the measurement period. The third zone may indicate that the bending angle of the body part of interest was on average greater than or equal to three times per minute above the threshold during the measurement period. In this way, the first zone may exhibit a higher quality of posture and/or movement as compared to the second and third zones. In such an implementation, the bend angle of the body part of interest may be monitored over multiple periodic blocks of time (eg, 1 minute blocks) during the measurement period.

In some implementations, the ergonomic zone may be defined based at least in part on the percentage of time during which the bend angle of the target body segment was greater than a threshold during the measurement period. For example, the first zone may indicate that the bend angle of the body segment of interest was greater than the threshold for less than 1/5 of the measurement period (or other suitable ratio). The second zone may indicate that the bend angle of the body segment of interest was greater than the threshold during 1/5 to 1/3 of the measurement period. The third zone may indicate that the bend angle of the body segment of interest was above the threshold for more than 1/3 of the measurement period. In this way, the first zone may exhibit a higher quality of posture and/or movement for the target body segment as compared to the second and third zones.

In some implementations, the ergonomic zone may be determined based at least in part on the velocity and/or acceleration of movement of the target body segment. For example, zones may be defined based at least in part on acceleration or velocity thresholds. More specifically, the ergonomic zone is the number of times the velocity and/or acceleration of the body segment of interest has risen above a threshold and/or the velocity and/or acceleration has risen above a threshold. It may be defined based at least in part on the proportion of time.

It will be appreciated that the exemplary ergonomic zones described above are intended for exemplary purposes only. More specifically, it will be appreciated that any suitable metric for defining one or more ergonomic zones may be used without departing from the scope of the present disclosure. For example, the ergonomic zone is greater than one or more suitable bend angle thresholds; velocity thresholds; acceleration thresholds; It can be defined based on any combination of: the number of times the target body segment is bent; the ratio of the time when the bending angle, acceleration, speed, etc. of the target body segment becomes larger than the corresponding threshold value, and the like.

In some implementations, the ergonomic rating can identify an overall ergonomic zone to which the user is categorized. The overall ergonomic zone may be determined based at least in part on the one or more ergonomic zones determined for one or more body segment of interest during the measurement period. For example, if ergonomic zones for multiple target body segments were determined during the measurement period, the overall ergonomic zone would be at least partially for each ergonomic zone determined for each target body segment. It can be judged based on. In some implementations, the overall ergonomic zone may correspond to the ergonomic zone for the target body segment exhibiting the lowest quality of posture and/or movement.

The ergonomic evaluation may further include power consumption metrics for one or more target body segments. The power consumption metric may provide an estimate of the power produced by the target body segment during the measurement period. For example, the power consumption metric may be determined based at least in part on velocity and/or acceleration of movement, range of movement, and/or other parameters associated with one target body segment. In some implementations, the ergonomic assessment may include an overall power consumption metric that identifies a set of power consumption metrics for each target body segment. The power consumption metric can be determined against the maximum power consumption value. The maximum value can be any suitable value that specifies the maximum suitable amount of power that can be safely consumed by the user. In some implementations, the maximum value may be personalized for individual users.

The ergonomic assessment may further include the duration of the monitored work, the duration of the break, the number of breaks (eg, average number of breaks), the duration of the break (eg, average duration of breaks), It may include a type, an estimated amount of output per unit time for a work mission, and/or a productivity rating that may indicate any other suitable productivity indicator. Such productivity assessments can be determined from sensor data and/or physical data. For example, sensor data can be used to determine when a user is active and when the user is working. The sensor data can further be used to categorize the type of work being performed by the user.

In some implementations, ergonomic evaluation may be used to provide tactile feedback to the user. For example, one or more feedback devices (eg, vibration motors, actuators, etc.) may be implemented in the ergonomic evaluation garment. Such feedback device may be used to provide tactile feedback to a user based at least in part on ergonomic evaluation. Tactile feedback may be provided to indicate information regarding the user's posture and/or motion data. For example, haptic feedback may be provided to the user to indicate that the user's movement and/or posture correspond to a particular ergonomic zone, that the user is consuming too much power, and so on.

The ergonomic rating may be provided to the user or other entity, for example, via a user device associated with the user (eg, smartphone, tablet, laptop, desktop, smartwatch, fitness band, etc.). In this way, the ergonomic assessment can provide a report to the user or other entity that identifies information associated with the user's posture and/or movement during one or more measurement periods. In some implementations, ergonomic evaluations of multiple users may be performed by one or more remote computing devices (eg, to determine broader trends regarding posture and/or movement habits of multiple users). Server device).

Referring now to the drawings, exemplary aspects of the disclosure are provided in more detail. For example, FIG. 1 illustrates an exemplary system 100 for determining an ergonomic rating associated with a user in accordance with exemplary aspects of this disclosure. The system 100 includes an ergonomic evaluation garment 102 and a computing device 104. In some implementations, the computing device 104 may be integrated or implemented within the ergonomic evaluation garment 102, or otherwise attached to the ergonomic evaluation garment 102. In some implementations, the computing device 104 may be a separate device separate from the ergonomic evaluation garment 102. In such an implementation, computing device 104 may be communicatively coupled to ergonomic evaluation garment 102, eg, via a network. For example, in such implementations, computing device 104 may be a self-contained device, attached, affixed, or otherwise connected to any suitable garment worn by the user. Good.

The ergonomic evaluation garment 102 may include one or more sensor devices 106. The sensor device 106 may be configured to measure data indicative of movement and/or posture of a user wearing the ergonomic evaluation garment 102. The sensor device 106 includes one or more accelerometers, gyroscopes, inertial measurement units, force gauges, tachometers, electromyography sensors, heart rate monitors, and/or data associated with the user's movements and/or postures. Other suitable sensors capable of measuring Although only two sensor devices 106 are shown in FIG. 1, it will be appreciated that the ergonomic evaluation garment 102 may include any suitable number of sensor devices. In addition, although the sensor device 106 is positioned on the sleeve of the ergonomic evaluation garment 102 (near the user's shoulder), the sensor device 106 measures the desired motion and/or posture data associated with the user. It will be appreciated that the ergonomic evaluation garment 102 may be positioned in any suitable manner to facilitate

In some implementations, the ergonomic evaluation garment 102 can be a smart garment constructed using one or more conductive threads. In such implementations, sensor device 106 may be coupled to such electrically conductive threads to form a circuit that implements exemplary aspects of the present disclosure. For example, the sensor devices 106 can be coupled to each other via such electrically conductive threads. In some implementations, sensor device 106 may be coupled to computing device 104 and/or other suitable computing device via electrically conductive threads.

The ergonomic evaluation garment 102 may include a fabric structure that is generally formed from threads that are woven or knit together. In the implementation where the ergonomic evaluation garment 102 is a smart garment, at least some of the threads are conductive. Conductive threads can be woven into the fabric structure to form a variety of different electronic circuits. A variety of different types of electrical devices can be attached to the thread and controlled by a controller such as a microprocessor. In one embodiment, the entire fabric structure can be made from electrically conductive threads. However, in alternative embodiments, the fabric structure may be a combination of conductive and non-conductive threads. When conductive and non-conductive yarns are combined, a fabric can be produced that has the texture, drape properties, and other properties of typical fabrics used to make garments and the like. Thus, the conductive threads can be incorporated into the fabric without unnecessarily increasing stiffness or imparting any other undesirable property to the fabric.

In general, the conductive threads used in the fabrics of the present disclosure can be made from any suitable conductive material. The conductive material may include, for example, metals, metal compounds, conductive polymers, or mixtures thereof. The yarn may include monofilament yarn, multifilament yarn, and possibly spun yarn. In one embodiment, for example, the electrically conductive yarn comprises a monofilament yarn. The entire thread can be made from a conductive material. Alternatively, the yarn may include a multi-component yarn containing a conductive component and a non-conductive component. For example, in one embodiment, the multi-component yarn may include a bi-component yarn in which the conductive component constitutes a core surrounded by a non-conductive sheath. Alternatively, the conductive component may form the sheath while the non-conductive component forms the core. In yet another embodiment, the conductive component and the non-conductive component can be in an adjacent relationship within the yarn.

In one embodiment, the conductive yarn comprises core-sheath type conductive fibers, such as monofilament fibers containing a core made of a conductive polymer. For example, the conductive polymers used to make the core may include acetylene conductive polymers, pyrrole conductive polymers, thiophene-based conductive polymers, phenylene conductive polymers, aniline conductive polymers, and the like.

For example, the electrically conductive portion of the fiber may include an acetylene-based, 5-membered heterocyclic system. Monomers that can be used to form the conductive polymer include, for example, 3-methylpyrrole, 3-ethylpyrrole, 3-dodecylpyrrole 3-alkylpyrrole, 3,4-dimethylpyrrole, 3-methyl-4-3, N-alkylpyrroles such as 4-dialkylpyrrole, dodecylpyrrole, N-methylpyrrole, N-dodecylpyrrole, N-methyl-3-methylpyrrole, N-alkyl-3-alkyl such as N-ethyl-3-dodecylpyrrole Including pyrrole, 3-carboxymethylpyrrole and the like. In an alternative embodiment, the conductive polymer may include a thiophene-based polymer such as an isothianaphthene-based polymer. Another example of the thiophene-based conductive polymer includes poly-3,4-ethylenedioxythiophene. One example of a phenylene conductive polymer is poly-p-phenylene vinylene. The polymers described above can also be mixed together in forming the electrically conductive portion of the yarn.

In one embodiment, dopants may be added to the conductive polymer to improve conductivity. The dopant may include, for example, a halide ion such as chloride ion or bromide ion. Other dopants are perchlorate ion, tetrafluoroborate ion, hexafluoroarsenate ion, sulfate ion, nitrate ion, thiocyanate ion, hexafluorosilicate ion, trifluoroacetate ion, phosphate ion, phenylphosphorus ion. Including acid ions. Specific examples of dopants are alkyl benzene sulfonates such as hexafluorophosphate, tosylate, ethylbenzene sulfonate, dodecyl benzene sulfonate, methyl sulfonate, other alkyl sulfonates, polyacrylates , Polyvinyl sulfonate ion, polystyrene sulfonate ion, poly(2-acrylamido-2-methylpropane sulfonate) ion and the like. The amount of dopant added to the conductive polymer can vary depending on the particular application. For example, the dopant may be combined with the conductive polymer in an amount of about 3 wt% to about 50 wt%, such as about 10 wt% to about 30 wt%.

In one embodiment, the conductive portion of the multi-component fiber can be formed by applying a metal coating to the polymer resin. The polymer resin may include any of the conductive polymers described above, or may include a non-conductive polymer. In an alternative embodiment, a conductive filler may be loaded into the thermoplastic. The thermoplastic resin may include a conductive polymer as described above, or a non-conductive polymer.

Metals well suited for coating polymeric materials include gold, silver, chromium, iron and the like. Conductive particles that can be used include any of the metals mentioned above and aluminum, graphite, other carbon particles, carbon fibers, carbon black, and the like.

In yet another embodiment, the conductive portion of the multi-component fiber or filament may include carbon filaments.

In one particular embodiment, the conductive composite fiber of the present disclosure is made of a thermoplastic polyamide containing from about 13% to about 60% by weight of conductive particulate material such as carbon black, graphite, boron nitride and the like. Includes a conductive polymer layer. The fibers further include a non-conductive component made of thermoplastic polyamide.

In another embodiment, the conductive thread comprises a thermoplastic polymer covered with a metal such as silver or stainless steel. The thermoplastic polymer may include, for example, a polyamide such as nylon or polyester.

Multicomponent fibers and yarns made in accordance with the present disclosure can include non-conductive components in addition to conductive components. The non-conducting component can be made from any suitable natural or synthetic polymer. For example, the non-conductive portion can be made from a polyamide such as nylon 6 or nylon 66. Alternatively, the non-conductive portion can include polyesters such as polyethylene terephthalate, polybutylene terephthalate, copolymers thereof, and the like. In yet another embodiment, the non-conducting component may include a polyolefin such as polyethylene or polypropylene (including copolymers thereof). In yet another embodiment, the non-conductive portion may comprise polyacrylonitrile or polyvinyl alcohol polymer. The relative amount of conductive component to non-conductive component can vary widely depending on a variety of different factors. The amount of conductive component may depend, for example, on the conductivity of the material and the type of material used. Generally, the conductive component may comprise about 20% to about 90%, such as about 30% to about 70%, by weight of the multicomponent fiber.

In another embodiment of the present disclosure, the conductive yarn may include a multifilament yarn containing conductive filaments. For example, a multifilament yarn can be formed in which one or more conductive filaments can be surrounded by non-conductive filaments. The non-conductive filament can be made from any of the non-conductive thermoplastic polymers described above. On the other hand, the conductive filaments can be made from any of the conductive materials described above, including conductive polymers, metallic materials and the like.

In yet another embodiment, multifilament yarns made from thermoplastic filaments can be coated with carbon nanotubes to make the yarns conductive.

Conductive threads made in accordance with the present disclosure may be woven or knit into any suitable fabric construction capable of carrying out the processes of the present disclosure. As mentioned above, the fabric structure may be made entirely of electrically conductive threads. Alternatively, the fabric may be made from a combination of conductive and non-conductive threads. For example, conductive threads can be strategically placed within the fabric to form a myriad of different electrical circuits for use in carrying out the processes of the present disclosure.

In one embodiment, the fabric structure of the present disclosure comprises a knitted fabric containing electrically conductive threads and non-conductive threads. In general, any suitable knitting machine may be used in accordance with the present disclosure. For example, the knitting machine may include a weft knitting machine, a warp knitting machine, or a seamless knitting machine. In one embodiment, for example, a Santoni circular knitting machine is used. The knitting machine used in this disclosure offers various advantages and benefits. For example, through the use of a knitting machine, a three-dimensional knitted architecture can be constructed that allows conductive yarns to be advantageously placed where they are needed. In addition, many knitting machines allow the user to electronically select needle-to-needle motion and may have a variety of different yarn feeders.

In one embodiment, for example, the fabric is formed or knitted on a circular knitting machine having a computerized electronic needle and a yarn feed selection system. Typically, cylindrical blanks are knit using both cylindrical and dial needles. The cylindrical needle can knit the first row of courses and the dial needle can knit the second row of courses.

Alternatively, the knitting machine may include more than two courses. For example, the knitting machine may include about 2 to about 16 courses, such as about 6 to about 12 courses.

In one embodiment, the knitting machine can be used with eight feeders. From a knitting machine it is possible to make fabrics having a three-dimensional construction. For example, a double sided fabric can be produced. In this way, the front of the fabric may contain primarily only non-conductive threads, while the back of the fabric may contain conductive threads. For example, plating techniques can be used to produce cloth. Plating is a knit structure in which two or more yarns are simultaneously supplied. The second yarn is generally of a different type than the first yarn. During the knitting process, a second yarn is placed under the first yarn so that each yarn can be turned to a particular side of the fabric. In this way, one thread may appear primarily on the front of the fabric and the other thread may appear primarily on the back of the fabric.

In one embodiment, the fabric may include various other threads in addition to the non-conductive threads and the conductive threads. For example, the fabric may include elastic threads that return when stretched. For example, the elastic yarn may include Spandex®.

In one embodiment, for example, the knitted yarn may be formed from about 4 to about 6 courses. The first course may be made of non-conductive yarn such as polyester, cotton, nylon, acrylic polymers, etc. On the other hand, the remaining course may comprise a single thread or a combination of threads. For example, one of the courses may include electrically conductive yarn with spandex yarn. On the other hand, the third course may include non-conductive yarn combined with spandex yarn. On the other hand, the fourth course may be made exclusively of electrically conductive threads. All different combinations can be used and all different numbers of courses can be used to form the fabric. In this way, a three-dimensional fabric architecture may be constructed, which is particularly well suited for configuring electrical circuits in the fabric and for the fabric to execute user-entered commands. Float loops can be used to obtain the desired structure during knitting.

Referring again to FIG. 1, the sensor data obtained by the sensor device 106 may be provided to the computing device 104. Computing device 104 may include a body data determiner 108 and an ergonomic evaluator 110. The body data determiner 108 may be configured to extract posture and/or motion data from the sensor data provided by the sensor device 106. For example, raw sensor data obtained by computing device 104 may be analyzed by body data determiner 108 to identify, determine, and/or extract features indicative of body data. More specifically, various attributes, characteristics, or patterns of sensor data may be determined to correspond to various user movements, postures, bend angles, and the like. The physical data determiner 108 can analyze the data obtained by the sensor device 106 to identify features indicative of such attributes, characteristics, or patterns. In this way, various portions of sensor data may have such attributes, characteristics, or patterns. Such portions of sensor data may be classified by the body data determiner 108 as various types of body data.

Such body data may be associated with a bend angle of at least one body segment of the user. For example, the physical data may include shoulder angles (eg, with respect to the user's body side), back angles (eg, front-back angles), torso angles (eg, left-right angles). In some implementations, the physical data may be associated with rotation of the user's shoulder (eg, about a joint). In some implementations, the physical data may further include velocity and/or acceleration at which the motion is performed by the user, the duration of the motion associated with the user, and/or the repeatability of the motion. It will be appreciated that body data may include data associated with various other suitable movements, postures, body segments, and the like. In some implementations, the body data may include behavioral data. Behavioral data may be indicative of work activity performed, productivity estimates (eg, number of towels folded per minute).

FIG. 2 illustrates exemplary bend angles associated with a user's shoulder. As shown, the body data may include shoulder angles relative to the body side. As shown in FIG. 2, the sensor device 106 may be positioned with respect to the user's shoulder so that sensor data indicative of shoulder angle may be obtained. In this way, the shoulder angle may be determined based at least in part on the sensor data obtained by the sensor device 106. As illustrated, the shoulder angle may be any suitable angle (eg, 15 degrees, 45 degrees, 90 degrees, etc.) to the body side of the user wearing the ergonomic evaluation garment 102. The measurements can be made within a full range of 360 degrees over multiple planes. The sensor device 106 and/or other sensor device positioned elsewhere relative to the ergonomic evaluation garment 102 may also be used in various other suitable aspects of a user wearing the ergonomic evaluation garment 102. It may be configured to obtain sensor data indicative of posture and/or movement of various body segments.

The physical data determined by the physical data determiner 108 may be used by the ergonomic evaluator 110 to determine an ergonomic evaluation associated with the posture and/or movement of the user. The ergonomic assessment may include any suitable information associated with the pose and/or movement of the user. More specifically, the ergonomic assessment may include one or more decisions regarding the quality and/or safety of movement and/or posture of one or more body segments of the user. Such a determination may correspond to categorizing a user's movements and/or postures into one or more ergonomic zones. An ergonomic zone may be determined for a body segment based at least in part on the movement and/or posture (eg, bend angle) of the body segment during one or more measurement periods. The measurement period can be any suitable period during which data is collected to facilitate assessment of the user's movement and/or posture. In some implementations, the measurement period may be initiated by a user, for example, via interaction with ergonomic evaluation garment 102, computing device 104, or other suitable computing device.

In some implementations, the ergonomic zone in which the user and/or the movement of the user may be classified is the number of cases where the bend angle of the body segment of interest exceeds a threshold during one or more measurement periods. May be defined based at least in part. For example, the ergonomic zone is defined based at least in part on the average number of cases where the bend angle of the body segment of interest is above a threshold during each of a plurality of subset periods of one or more measurement periods. Can be done. In some implementations, the ergonomic zone is at least partially based on the amount of time (eg, the average amount of time) that the bend angle of the body segment of interest is above the threshold during one or more measurement periods. Can be defined based on The amount of such time can be quantified as the ratio of the time above the threshold to the total time of one or more measurement periods. Ergonomic zones may be defined to indicate different layers of posture and/or motion acceptability. For example, the first ergonomic zone may exhibit some acceptable movement and/or posture and the second ergonomic zone may exhibit less acceptable movement and/or posture. , The third ergonomic zone may exhibit less acceptable movement and/or posture.

The ergonomic zone is based at least in part on the number and/or amount of cases in which the movement and/or posture of the user and/or target body segment exceeds a threshold bending angle of the target body segment. It may be defined to be classified into a particular ergonomic zone. In this way, the number of cases and/or the amount of time in which the bending angle of the body segment exceeds the threshold value during one or more measurement periods can be determined via the ergonomic zone classification as It may be associated with posture quality, safety, and/or acceptable levels.

As shown, each target body segment for which data is being collected may be categorized into a respective ergonomic zone corresponding to that target body segment. In some implementations, the ergonomic evaluator 110 may further determine an overall ergonomic zone for the user. The overall ergonomic zone may be determined based at least in part on the ergonomic zone determined for each target body segment. In this way, the overall ergonomic zone can indicate the overall quality, safety, and/or acceptability of the user's movement and/or posture. In some implementations, the overall ergonomic zone corresponds to the strictest ergonomic zone (eg, least likely pose and/or movement) into which a user's target body segment is classified. Ergonomic zone). In some implementations, the overall ergonomic zone may correspond to a set of ergonomic zones associated with each target body segment.

The ergonomic rating may further include one or more power consumption metrics associated with the user. One power consumption metric may be determined for one target body segment and may be an estimate of the amount of power consumed by that target body segment during one or more measurement periods. The power consumption metric may be determined based at least in part on the velocity and/or acceleration of movement of the target body segment. More specifically, the power consumption metric may be determined based at least in part on the angular velocity and/or angular acceleration of movement of the body segment. In some implementations, the power consumption metric may be for the maximum acceptable power value for the user. The ergonomic evaluator 110 can further collect power consumption metrics for each target body segment to determine the overall power consumption by the user.

The ergonomic rating may further include a productivity rating associated with the user. The productivity rating is the sum of the duration of the user's activity, the duration of the user's breaks, the number of breaks taken by the user, the duration of the breaks, the average duration of the breaks, the duration of the breaks, and the measurement duration. Information may be included that indicates time, the type of activity being performed, an estimated amount of output per unit time for a work mission, and/or other suitable productivity indicators. The type of activity being performed may include a description of the activity being performed. For example, the ergonomic evaluator 110 can determine such type of activity based at least in part on the sensor data and/or body data. For example, such instructions identify tasks such as "fold items,""stackitems,""transfer items from a first surface to a second surface,""load and unload." can do. In some implementations, the productivity assessment may be based at least in part on behavioral data included in the body data.

Ergonomic evaluator 110 can provide an ergonomic evaluation for display on, for example, computing device 104 or other suitable computing device. The ergonomic rating may be displayed within the user interface so that the user can view relevant information provided by the ergonomic rating.

Ergonomic evaluation can be used to provide tactile feedback to the user. For example, the ergonomic evaluation garment 102 may include one or more feedback devices, such as vibration motors, actuators, etc., mounted on or otherwise attached to the ergonomic evaluation garment 102. Such feedback device may be configured to provide tactile feedback to the user based at least in part on the ergonomic assessment. For example, computing device 104 may determine one or more tactile feedback signals based at least in part on ergonomic assessments, body data, and/or sensor data, and send such tactile feedback signals via the feedback device. Can be configured to be provided to the user. In some implementations, the feedback signal may include a vibration pattern associated with one or more ergonomic zones determined for the user, power consumption metrics, and the like. For example, vibrations having a first vibration pattern may be provided to a user to indicate that the user's movements and/or postures are classified as being in the first ergonomic zone. Vibrations having the second pattern may be provided to the user to indicate that the user's movements and/or postures are classified as being in the second ergonomic zone.

In some implementations, the ergonomic evaluation garment 102 may be calibrated based on the user wearing the ergonomic evaluation garment 102. For example, calibration may be performed by computing device 104 based at least in part on sensor data from sensor device 106. Such a calibration may indicate one or more reference points by which the ergonomic assessment is judged. The reference point may indicate, for example, the natural posture of the user. In this manner, the calibration may be performed prior to the ergonomic evaluator 110 determining the ergonomic evaluation.

FIG. 3 illustrates a flow diagram of an exemplary method (200) of determining an ergonomic rating in accordance with exemplary aspects of this disclosure. Method (200) may be implemented by one or more computing devices, such as one or more of the computing devices shown in FIG. In a particular implementation, method (200) may be implemented by body data determiner 108 and/or ergonomic evaluator 110 of FIG. In addition, FIG. 3 illustrates the steps performed in a particular order for purposes of illustration and description. Using the disclosure provided herein, one of ordinary skill in the art can adapt, rearrange, expand and omit various method steps described herein in various ways without departing from the scope of the disclosure. , Or may be modified.

At (202), method (200) may include calibrating one or more sensor devices associated with the ergonomic evaluation garment. As shown, the ergonomic evaluation garment can be any suitable garment configured to obtain data indicative of the user's movement and/or posture. In some implementations, the ergonomic evaluation garment can be a smart garment constructed using one or more conductive threads. The conductive thread can be configured to form one or more circuits to facilitate exemplary aspects of the present disclosure. The ergonomic evaluation garment may include one or more sensor devices (eg, accelerometers, gyroscopes, inertial measurement units, etc.) configured to obtain data indicative of a user's movement and/or posture. In the implementation where the ergonomic evaluation garment is a smart garment, the sensor device may be bonded to one or more electrically conductive threads.

The sensor device of the ergonomic evaluation garment may be calibrated for a user wearing the ergonomic evaluation garment. The calibration can provide a baseline or reference for measuring the movement and/or posture of the user. The calibration can indicate the user's natural pose, which is a measure of movement and/or pose changes.

At (204), method (200) may include receiving sensor data from one or more sensor devices during the measurement period. As shown, the measurement period can be any suitable period during which data is acquired by the sensor device to facilitate the determination of an ergonomic rating for the user. The sensor data may include raw sensor data indicating movement and/or posture of the user during the measurement period.

At (206), method (200) may include determining physical data associated with the user's movement and/or posture based at least in part on the sensor data. For example, determining body data may include identifying and/or extracting various features from raw sensor data indicative of various movements, bending angles, velocities, accelerations, etc. of a user during a measurement period. .. Features can correspond to various attributes, characteristics, or patterns of sensor data. In this way, features can be extracted at different times from different portions of the sensor data to identify different user movements, bend angles, etc. made by the user.

At (208), method (200) may include determining an ergonomic rating for the user based at least in part on the physical data. The ergonomic assessment may include suitable information regarding the user's posture and/or movement during the measurement period. For example, an ergonomic assessment may include ergonomic zones of a user's one or more target body segments into ergonomic zones that indicate the acceptability, quality, and/or safety of the user's movement and/or posture during the measurement period. Can indicate a classification. An ergonomic zone for a target body segment may be defined based at least in part on the number of cases where the bend angle of the target body segment exceeds a threshold during the measurement period. In some implementations, the ergonomic zone for a given body segment may be defined based at least in part on the ratio of the time the bending angle is above the threshold to the total time of the measurement period. In some implementations, an overall (eg, corresponding to the least acceptable posture and/or movement) corresponding to the severest ergonomic zone determined for the subject body segment during the measurement period. Ergonomic zones may be determined for the user.

The ergonomic evaluation may further include power consumption metrics for one or more target body segments. The power consumption metric may be determined based at least in part on the velocity and/or acceleration of movement of the target body segment. In some implementations, an overall power consumption metric may be determined for each set of power consumption metrics for each target body segment. In some implementations, the ergonomic evaluation may include a productivity evaluation. Productivity assessments include the duration of monitored work, duration of breaks, number of breaks (eg, average number of breaks), duration of breaks (eg, average duration of breaks), type of work being performed, It may indicate an estimated amount of output per unit time for a work mission, and/or any other suitable productivity indicator. Such productivity assessments can be determined from sensor data and/or physical data.

As an example, the ergonomic rating may be associated with the user's rating associated with one or more activities. The activity may include, for example, one or more of medical rehabilitation, sports performance, and injury diagnosis (eg, tracking and injury prevention). For example, information such as bend angles, ergonomic zones achieved, power consumption metrics, etc., indicate how the user is progressing during medical rehabilitation (eg, user range of motion, increasing power). , Whether the user is maximizing his range of motion for maximum sports performance (eg for javelin throwing) and/or is the body segment moving to a zone that may indicate an increased risk of injury (eg hyperextension)? It can be analyzed to show if.

At (210), method (200) may include providing data indicative of an ergonomic rating to a user interface of a computing device associated with the user. For example, data indicative of an ergonomic rating may be provided for display in a graphical user interface of a computing device. In this way, the user may be notified of the ergonomic assessment so that he may have the opportunity to adjust his posture and/or movement. For example, a user may be informed about how he or she is doing medical rehabilitation, sports performance, injury diagnosis, etc.

At (212), method (200) may include providing a haptic feedback signal to a user based at least in part on the ergonomic assessment. The ergonomic evaluation garment may include one or more tactile feedback devices. Such a tactile feedback device may be coupled to, for example, one or more conductive threads of an ergonomic evaluation garment. The haptic feedback signal may be determined based at least in part on the ergonomic assessment, body data, and/or sensor data and provided to the user via the feedback device.

FIG. 4 illustrates an exemplary computing system 300 that can be used to implement methods and systems according to exemplary aspects of this disclosure. System 300 can be implemented using a client-server architecture that includes a computing device 310 that communicates with one or more ergonomic evaluation garments 330 through a network 340. System 300 can be implemented using other suitable architectures, such as a single computing device.

System 300 includes a computing device 310. Computing device 310 may be a general purpose computer, a dedicated computer, a laptop, desktop, mobile device, navigation system, smartphone, tablet, wearable computing device, display with one or more processors, or other suitable computing device. , May be any suitable type of computing device. In some implementations, computing device 310 may be integrated or implemented within ergonomic evaluation garment 330. In some implementations, computing device 310 may be a separate device separate from ergonomic evaluation garment 330 or may be located remotely from ergonomic evaluation garment 330. Good. In some implementations, a computing device may include one or more sensor devices 320. For example, the sensor device may include one or more accelerometers, gyroscopes, inertial measurement units, force gauges, tachometers, electromyographic sensors, heart rate monitors, and/or other suitable sensors. The sensor device 320 may be included within the computing device 310 or otherwise physically connected to the computing device 310. In this way, the computing device 310 and the sensor 320 are coupled to the ergonomic evaluation garment 330 to obtain sensor data indicating movement and/or posture of a user wearing the ergonomic evaluation garment 330. It may be attached, affixed, or otherwise connected.

Computing device 310 may include one or more processors 312 and one or more memory devices 314. Computing device 310 may also include an ergonomic evaluation garment 330 and/or other suitable computing device, eg, a server computing device, and a network interface used to communicate through network 340. The network interface may include any suitable components for interfacing with one or more networks, including, for example, transmitters, receivers, ports, controllers, antennas, or other suitable components.

The one or more processors 312 may include any suitable processing device, such as a microprocessor, microcontroller, integrated circuit, logic device, or other suitable processing device. One or more memory devices 314 include one or more computer readable media including, but not limited to, non-transitory computer readable media, RAM, ROM, hard drives, flash drives, or other memory devices. May be included. The one or more memory devices 314 may store information accessible by the one or more processors 312, including computer readable instructions 316 that may be executed by the one or more processors 312. Instructions 316 may be any set of instructions that, when executed by one or more processors 312, cause the one or more processors 312 to perform an operation. For example, instructions 316 may be executed by one or more processors 312 to implement body data determiner 108 and ergonomic evaluator 110 described with reference to FIG.

As shown in FIG. 4, one or more memory devices 314 may also store data 318 that may be retrieved, manipulated, created, or stored by one or more processors 312. Data 318 may include, for example, sensor data and other data. The data 318 may be stored locally on the computing device 310 and/or in one or more databases. One or more databases may be connected to computing device 310 by a high bandwidth LAN or WAN, or may be connected to computing device 310 through network 340. One or more databases may be partitioned such that they are located at multiple locations.

Computing device 310 may exchange data with one or more ergonomic evaluation garments 330 or other suitable computing devices through network 340. Although one ergonomic evaluation garment 330 is shown in FIG. 4, any number of ergonomic evaluation garments 330 may be connected to computing device 310 through network 340. Ergonomic evaluation garment 330 may be any suitable garment. In some implementations, the ergonomic evaluation garment comprises one or more electrically conductive threads configured to form one or more circuits to implement the exemplary aspects of this disclosure. It may be a smart garment constructed using. In some implementations, the ergonomic evaluation garment may include one or more sensor devices 350. For example, such implementations may include implementations in which computing device 310 is located remotely from the ergonomic evaluation garment.

In some implementations, computing device 310 may be communicatively coupled to one or more additional computing devices. For example, computing device 310 may provide one piece of data indicative of the ergonomic rating, eg, for displaying the ergonomic rating to a user via a display device associated with one or more such additional computing devices. It may be configured to provide additional computing devices as described above. In some implementations, the one or more additional computing devices, with the consent of the user, perform multiple ergonomic assessments from multiple ergonomic assessment garments and/or associated computing devices. May include a server computing device configured to obtain. As shown, such server computing device is then configured to collect and analyze ergonomic ratings to determine trends, patterns, etc. associated with such ergonomic ratings. obtain. In some implementations, such a server computing device may include one or more ergonomics (eg, via a collaborative application, direct communication) to facilitate the functionality of the ergonomic evaluation garment 330. Can communicate with the physical evaluation garment 330. For example, a server computing device (e.g., a federated application to a clothing application) may have one or more functions (e.g., gathering sensor data, processing sensor data, etc.) of each ergonomic evaluation garment 330. One or more ergonomic evaluation garments 330 may be communicated to provide and/or facilitate determining physical data, determining ergonomic evaluation, etc.). For example, the server computing device may provide/update data analysis tools for the ergonomic evaluation garment 330 over time.

In some implementations, the computing device 310 may host (or otherwise be associated with) a cloud-based service that provides ergonomic assessments. For example, computing device 310 may be associated with a service that obtains data associated with one or more ergonomic assessment garments 330, such as data indicative of an individual ergonomic assessment. A computing device 310 (eg, remote from the ergonomic evaluation garment) spends time on data from one or more ergonomic evaluation garments 330 (which may be associated with the same or different individuals, for example). Can be collected. The computing device 310 can determine an overall ergonomic rating and/or an ergonomic rating trend based on such data. Overall ergonomic ratings and/or trends may be provided to enterprise customers, individuals, etc. of cloud-based services for medical-related tracking, sports-related tracking, injury prevention, and/or other purposes.

In some implementations, ergonomic evaluation garment 330 and/or computing device 310 may store or include one or more models 360. For example, the model 360 may be or may include various machine learning models such as neural networks (eg, deep neural networks), or other multi-layer nonlinear models. The neural network may include a recursive neural network (eg, long and short term memory recursive neural network), a feedforward neural network, or other form of neural network.

In some implementations, the ergonomic evaluation garment 330 and/or computing device 310 may receive one or more models 360 over a network 340 (eg, from another computing device) and may receive one or more models 360. The model 360 may be stored in the memory 314/334 and used or otherwise implemented by the one or more processors 312/332. In some implementations, the ergonomic evaluation garment 330 and/or computing device 310 may include multiple parallels of a single model 360 (eg, to perform parallel garment calibration and/or trend analysis). Instance can be realized.

The model 360 may be trained to calibrate the ergonomic evaluation garment 330 and/or determine ergonomic evaluation trends. For example, the model 360 can receive inputs that include at least physical data, sensor data, and/or other data associated with a user. The model 360 may be trained to provide a model output showing the ergonomic rating associated with the user. The model output may be based at least in part on the model input (eg, body data). Additionally or alternatively, the model 360 can be trained to determine an overall ergonomic rating and/or one or more ergonomic rating trends. For example, the model 360 can receive input including multiple ergonomic ratings (eg, from individual users, multiple users). The model 360 may be trained to provide model outputs that show trends in ergonomic evaluation (eg, evaluation patterns over time, repetitive characteristics, other trends). The model output may be based at least in part on the model input (eg, multiple ergonomic evaluations).

Additionally or alternatively, one or more models 360 communicate with computing device 310 and/or ergonomic evaluation garment 330 according to a client-server relationship (eg, computing device 310 and/or It may be included in, or otherwise stored and implemented by, a server computing system (remote from ergonomic evaluation garment 330). For example, model 360 may be implemented by a server computing system as part of a web service. Thus, one or more models 360 may be stored and implemented in computing device 310 and/or ergonomic evaluation garment 330, and/or one or more models 360 may be a server computing system. May be stored and implemented in.

The model 360 may be trained via interaction with a training computing system communicatively coupled through the network 340. The training computing system may be separate from the server computing system, computing device 310, and/or ergonomic evaluation garment 330, or the server computing system, computing device 310, and/or ergonomics. It may be a part of the physical evaluation clothing 330.

The training computing system may include a model trainer that trains the machine learning model 360 using various training or learning techniques, such as error back-propagation. In some implementations, backpropagating the error may include performing a truncated backpropagation over time. The model trainer can perform many generalization techniques (eg, weight decay, dropout, etc.) to improve the generalization ability of the model under training. In some implementations, supervisory training techniques may be used on the set of labeled training data.

In particular, the model trainer can train the model 360 based on the set of training data. Training data may include, for example, many previous ergonomic assessments and/or body data (and/or sensor data) associated therewith. In some implementations, the training data may include labeled ergonomic evaluation data. The training data can be labeled manually, automatically, or using a combination of automatic and manual labeling.

In some implementations, training examples may be provided by the ergonomic evaluation garment 330 if the user agrees. Thus, in such implementations, model 360 may be trained by the training computing system on user-specific communication data received from ergonomic evaluation garment 330. In some cases, this process may be referred to as model personalization.

In implementations where computing device 310 is a separate and remote device located remotely from ergonomic evaluation garment 330, ergonomic evaluation garment 330 may include one or more processors 332 and memory 334. Can be included. For example, such processor 332 and/or memory 334 may be used to obtain sensor data from sensor device 350 and to provide sensor data to computing device 310, eg, via network 340. One or more processors 332 may include one or more central processing units (CPUs), and/or other processing devices. The memory 334 may include one or more computer-readable media and may contain information accessible by the one or more processors 332, including instructions 336 and data 338 that may be executed by the one or more processors 332. May be stored.

The ergonomic evaluation garment 330 and/or computing device 310 of FIG. 4 includes various input/output devices for providing and receiving information from a user, such as a mechanism or other means for starting and stopping the measurement period. May be included. In some implementations, computing device 310 may include a display device for presenting a user interface for displaying ergonomic ratings in accordance with exemplary aspects of this disclosure.

Ergonomic evaluation garment 330 may also include a network interface used to communicate with one or more remote computing devices (eg, computing device 310) through network 340. The network interface may include any suitable components for interfacing with one or more networks, including, for example, transmitters, receivers, ports, controllers, antennas, or other suitable components.

Network 340 may be any type of communication network, such as a local area network (eg, intranet), wide area network (eg, internet), cellular network, or some combination thereof. The network 340 may also include a direct connection between the ergonomic evaluation garment 330 and the computing device 310. In general, communication between computing device 310 and ergonomic evaluation garment 330 uses any type of wired and/or wireless connection to communicate with a variety of communication protocols (eg, TCP/IP, HTTP, SMTP, FTP). ), encoding or format (eg, HTML, XML), and/or protection schemes (eg, VPN, secure HTTP, SSL).

The techniques described herein refer to servers, databases, software applications, and other computer-based systems, as well as the actions taken and the information sent to and from such systems. Those skilled in the art will recognize that the inherent flexibility of computer-based systems allows for a wide variety of possible configurations, combinations, and divisions of tasks and functionality between components. .. For example, the server processes described herein may be implemented using a single server or multiple servers working in combination. Databases and applications may be implemented on a single system or distributed across multiple systems. The distributed components may operate sequentially or in parallel.

While the present subject matter has been described in detail with respect to particular exemplary embodiments thereof, those skilled in the art with the understanding of the foregoing will make changes to such embodiments, variations of such embodiments, and It will be appreciated that equivalents to the embodiments can easily be made. Accordingly, the scope of the present disclosure is illustrative rather than limiting, and the present disclosure includes such modifications, variations and/or additions to the present subject matter, as will be readily apparent to those skilled in the art. Do not exclude.

Claims (20)

A computer-implemented method of determining an ergonomic rating associated with a user, the method comprising:
Receiving by one or more computing devices sensor data from one or more sensors implemented on an ergonomic evaluation garment worn by a user;
Determining physical data associated with at least one body segment of the user by the one or more computing devices based at least in part on the sensor data, the physical data comprising: Associated with a bend angle associated with one body segment, the method further comprising:
Determining at least partially based on the physical data an ergonomic rating associated with the user by the one or more computing devices, the ergonomic rating being associated with the user. A computer comprising an indication of one or more ergonomic zones, the one or more ergonomic zones being determined based at least in part on the bend angle associated with the at least one body segment. The method realized by. The computer according to claim 1, wherein the ergonomic evaluation garment is a smart garment constructed at least partially using conductive threads woven into a fabric structure of the ergonomic evaluation structure. The way it is realized. The computer-implemented computer system of claim 2, wherein at least one of the conductive threads is coupled to at least one of the one or more sensors to form one or more circuits. Method. The one or more sensors include one or more accelerometers implemented in the ergonomic evaluation garment to facilitate measurement of movement or posture associated with the at least one body segment, A computer-implemented method according to any one of the preceding claims. The body data includes data associated with at least one of a range of motion of the at least one body segment, a velocity of the at least one body segment, and an acceleration of the at least one body segment. A computer-implemented method according to any one of the preceding claims. 8. A computer-implemented computer according to any one of the preceding claims, wherein the body data comprises data associated with the bend angle of the at least one body segment for one or more bend angle thresholds. Method. 7. The body data includes data associated with timing of the bend angle of the at least one body segment relative to each of the one or more bend angle thresholds corresponding to the at least one body segment. A computer-implemented method as set forth in. The step of determining physical data for at least one body segment of the user by the one or more computing devices includes the bending angle of the at least one body segment being the respective one or more bending angle thresholds. 8. The computer-implemented method of claim 7, including the step of measuring an amount of time greater than a value. Determining an ergonomic rating associated with the user by the one or more computing devices comprises one or more ergonomics associated with the user based at least in part on the body data. Computer-implemented method according to any one of the preceding claims, comprising the step of determining a zone. The step of determining the one or more ergonomic zones associated with the user includes an amount of time that the bend angle of the at least one body segment is greater than each of the one or more bend angle thresholds. The computer-implemented method of claim 9, comprising determining the one or more ergonomic zones based at least in part on the. 10. The any one of the preceding claims, wherein determining an ergonomic rating associated with the user by the one or more computing devices comprises determining an assessment of power consumption by the user. A computer-implemented method as set forth in. Further comprising providing one or more tactile feedback signals to the user via the ergonomic evaluation garment by the one or more computing devices based at least in part on the ergonomic evaluation. , A computer-implemented method according to any one of the preceding claims. 8. Any of the preceding claims, further comprising calibrating the one or more sensors implemented on the ergonomic evaluation garment as worn by the user with the one or more computing devices. The computer-implemented method of paragraph 1. One or more processors,
A computing system including one or more memory devices, comprising:
The one or more memory devices store computer readable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations.
The operation is
Receiving sensor data from one or more sensors implemented on an ergonomic evaluation garment worn by a user;
Determining at least in part based on the sensor data, physical data associated with at least one body segment of the user, the physical data comprising a bend angle associated with the at least one body segment. And the action is further associated with
Determining at least in part the ergonomic rating associated with the user, the ergonomic rating of one or more ergonomic zones associated with the user. A computing system including a display, wherein the one or more ergonomic zones are determined based at least in part on the bend angle associated with the at least one body segment. The computing of claim 14, wherein the ergonomic evaluation garment is a smart garment constructed at least partially using conductive threads woven into a fabric structure of the ergonomic evaluation structure. system. The computing system of claim 15, wherein at least one of the conductive threads is coupled to at least one of the one or more sensors to form one or more circuits. The body data includes data associated with at least one of a range of motion of the at least one body segment, a velocity of the at least one body segment, and an acceleration of the at least one body segment. The computing system according to any one of 14 to 16. One or more tangible, non-transitory computer-readable media that store computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations.
The operation is
Receiving sensor data from one or more sensors implemented on an ergonomic evaluation garment worn by a user;
Determining at least in part based on the sensor data, physical data associated with at least one body segment of the user, the physical data comprising a bend angle associated with the at least one body segment. And the action is further associated with
Determining at least in part the ergonomic rating associated with the user, the ergonomic rating of one or more ergonomic zones associated with the user. One or more tangible, non-transitory computer including a display, the one or more ergonomic zones being determined based at least in part on the bend angle associated with the at least one body segment; Readable medium. The ergonomic evaluation garment is a smart garment constructed by at least partially using conductive threads woven in a fabric structure of an ergonomic evaluation structure, 19. One or more tangible non-transitory computer readable according to claim 18, wherein at least one is coupled to at least one of the one or more sensors to form one or more circuits. Medium. 20. One or more of any one of claims 18-19, wherein the ergonomic rating is associated with the user's rating associated with one or more of medical rehabilitation, sports performance, and injury diagnosis. Tangible, non-transitory computer-readable medium.

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