ERGONOMIC CLASSIFICATIONS

An example electronic device includes a display device, and a sensor to detect position data of a user of the electronic device. The position data indicates a position and orientation of the user relative to the display device. In addition, the electronic device includes a controller coupled to the sensor and the display device. The controller is to: receive the position data from the sensor; use a machine learning model and the position data to classify an interaction of the user with the electronic device in a first ergonomic category or a second ergonomic category; and adjust an angular position of a display device or an output from the display device responsive to a classification of the interaction in the first ergonomic category.

BACKGROUND

Prolonged interaction with electronic devices can lead to a number of physical injuries and conditions (collectively referred to herein as “injuries”). In some cases, medical treatment, including surgery, may be administered to address such injuries. Proper body positioning, as well as proper electronic device settings may be used to prevent such injuries from occurring or to lessen their severity.

DETAILED DESCRIPTION

To prevent injury, a user of an electronic device may employ proper body positioning (e.g., posture) and electronic device settings (e.g., display settings, sound settings). However, it can be difficult for a user to assess their current positioning and/or settings. Moreover, what may be considered proper body posturing and electronic device settings may change depending on the user's environment and other transient factors.

Accordingly, examples disclosed herein include electronic devices that may receive data from a sensor (or a plurality of sensors), and based on the received data, may classify a user's interaction with the electronic device as either ergonomic or unergonomic. In some examples, the electronic devices may use machine learning models to classify the user's interaction with the electronic device as unergonomic, and responsive to the classification, determine (and potentially automatically implement) corrections to various parameters, positions, settings, etc. of the electronic device to change the classification to ergonomic. Thus, through use of the examples disclosed herein, a user may more consistently achieve and maintain ergonomic interaction with the electronic device so as to avoid injury.

Reference is now made toFIG.1, which shows a user5interacting with an electronic device10. The term “electronic device” may comprise any device that may execute machine-readable instructions. For instance, an electronic device may comprise a computer (e.g., a desktop computer, laptop computer, tablet computer, all-in-one computer), a server, or a smartphone. The electronic device10ofFIG.1is a laptop computer that includes a housing12including a first housing member14pivotably coupled to a second housing member16with a hinge13.

The first housing member14may support a display device18for presenting images (e.g., text, graphics, pictures, videos, symbols) to user5. User5may change the rotative position of the first housing member14, relative to the second housing member16about the hinge13, to adjust an angle α between the first housing member14and second housing member16about the axis of rotation of hinge13during operations.

Several angles, measurements, positions, and settings may contribute to an overall classification of the interaction of the user5with the electronic device10as ergonomic or unergonomic. For example, the position and orientation of the user5relative to electronic device10(and particularly to display device18) may result in posture that may lead to injuries to the back, neck, and/or shoulders of the user5.

In some examples, the position and orientation of the user5relative to the electronic device10may be characterized by various parameters including a viewing distance D, a head tilt angle β, a viewing angle θ, a head height H, etc.

The viewing distance D may be measured from the face of the user5(or a particular facial feature, such as the eyes) to the display device18(to an edge of the display device18, the center of the display device18, etc.). The head tilt angle β may comprise an angle formed between a centerline4extending from the head of the user5relative to vertical (or to another reference line that may be considered neutral for the head and neck of the user5). The head tilt angle β may be characteristic of the bending angle of the neck of the user5, with a tilt angle R of 0° corresponding to a neutral or straight neck. The viewing angle θ may comprise the angle formed between the line of sight7extending from the eyes of the user5to the display device18and to the horizontal direction. The head height H may comprise the vertical height of the head of the user5(or some other body part, such as the shoulders or eyes) from the support surface of the electronic device10such as the table-top9shown inFIG.1. In some examples, the head height H may be measured from some other reference point or component on the electronic device10(e.g., a point, surface, or component on the first or second housing members14,16, the hinge13, a sensor, etc.).

In addition, settings of the electronic device10may also be relevant to classifying the interaction of the user5with the electronic device10as ergonomic or unergonomic. For instance, improper output parameters of the display device18, such as font size, brightness, etc. may contribute to eye strain or other vision related injuries for user5. In some instances, the appropriateness of the output parameters of the display device18may be influenced by the position and orientation of the user (e.g., via the parameters D, H, α, θ, β), and/or by other factors (e.g., such as whether the user's iris contracted, the ambient light, relative humidity of the surrounding environment, whether the user5is wearing corrective lenses and whether those lenses are convex or concave).

Further, improper volume settings for speakers of the electronic device10(e.g., speaker60inFIG.2) may contribute to hearing loss or other hearing related injuries for user5. As with the output parameters of the display device18, the appropriateness of the volume output by the speaker(s) of the electronic device10may be influenced by the position and orientation of the user (e.g., via the parameters D, H, α, θ, β), and/or by other factors (e.g., ambient noise levels, speaker type).

As described in more detail below, electronic device10may receive data relating to the position and orientation of the user5relative to the electronic device10(or the display device18), the output from the display device18, and/or the speaker output to determine whether the interaction of the user5with the electronic device10is ergonomic or unergonomic. Further details of the structure and components of electronic device10are now described below to explain the functionality of electronic device10during operations.

Referring now toFIGS.1and2, in addition to display device18, electronic device10may comprise a user input assembly20in the second housing member16. The user input assembly20may comprise a device (or a collection of devices) for receiving user inputs during operations. For instance, in some examples, user input assembly20may comprise a keyboard, trackpad, etc. Also, the electronic device10may include a speaker60. Speaker60comprises a device (or an array of devices) that may output audible sound. Speaker60may be actuated to output sound from electronic device10during operations. For instance, speaker60may output sound effects, music and/or other audio feeds, etc. from electronic device10.

In addition, electronic device10may include a controller50which further comprises a processor52and a memory54. The processor52may comprise any suitable processing device, such as a microcontroller, central processing unit (CPU), graphics processing unit (GPU), timing controller (TCON), a scaler unit. The processor52executes machine-readable instructions (e.g. machine-readable instructions56) stored on memory54, thereby causing the processor52(and, more generally, electronic device10) to perform some or all of the actions attributed herein to the processor52(and, more generally, to electronic device10). More specifically, processor52fetches, decodes, and executes instructions (e.g., machine-readable instructions56). In addition, processor52may also perform other actions, such as, making determinations, detecting conditions or values, etc., and communicating signals. If processor52assists another component in performing a function, then processor52may be said to cause the component to perform the function.

The memory54may comprise volatile storage (e.g., random access memory (RAM)), non-volatile storage (e.g., flash storage, etc.), or combinations of both volatile and non-volatile storage. Data read or written by the processor52when executing machine-readable instructions can also be stored on memory54. Memory54may comprise “non-transitory machine readable medium.”

The processor52may comprise one processing device or a plurality of processing devices that are distributed within electronic device10. Likewise, the memory54may comprise one memory device or a plurality of memory devices that are distributed within the electronic device10. For instance, in some examples, controller50(or a component thereof) may be distributed within the first housing member14and the second housing member16. In addition, in some examples, controller50(or a component thereof) may be positioned within another electronic device (not shown) that is communicatively coupled to electronic device10via a network or other suitable connection.

Further, electronic device10may include a plurality of sensors communicatively coupled to controller50that are to measure, detect, infer, etc. the various parameters discussed above for determining whether a user (e.g., user5) is interacting with the electronic device10in an ergonomic or unergonomic manner. For instance, in some examples, the electronic device10includes an image sensor40, a light sensor62, a microphone64, a humidity sensor66, angle detecting sensor68, etc.

Image sensor40may comprise any suitable sensor or sensor array that is to detect images in or outside the visible light spectrum (e.g., infrared, ultraviolet). In some examples, image sensor40comprises a camera (e.g., a video camera). During operations, image sensor40is to capture images of a user (e.g., user5inFIG.1) of electronic device10. In some examples, the image sensor40is positioned along a topmost side of the display device18. However, the precise location of image sensor40may be varied in different examples, including various locations in either the first housing member14or the second housing member16.

Controller50may receive images captured by the image sensor40(or data that is representative of the captured images). In addition, controller50may analyze images captured by the image sensor40to determine a position of a user's5face (or particular features thereof, such as the eyes) relative to the display device18and/or other components of electronic device10. Any suitable analysis techniques may be used by controller50to determine a position of the user's5face. For instance, in some examples, the controller50may analyze the relative location of various facial features (e.g., eyes, nose, mouth) to determine an orientation of the user's5face relative to the display device18. In addition, the controller50may analyze images captured by the image sensor40to determine a distance between the display device18and the user's5face, such as by analyzing the relative sizing and spacing of the user's5face (or facial features thereof) and objects in the images that are positioned at known distances from the display device18(e.g., components of electronic device10). In some examples, the controller50may interrogate a time-of-flight or other suitable proximity sensor (not shown) either alone or in combination with the image(s) captured by the image sensor40to determine the distance between the display device18and the user of the electronic device10. Based on the analysis, the controller50may determine the various parameters described above for characterizing the position and orientation of the user5relative to the electronic device10(or more particularly display device18) (e.g., D, H, β, θ).

In addition to the position and orientation of the user5(FIG.1), controller50may also analyze the images captured by image sensor40for other information. For instance, in some examples, controller50may determine (using the image sensor40) whether the user5is wearing corrective lenses, and whether the lenses are concave or convex. Also, in some examples, controller50may determine (using the image sensor40) whether and to what extend to the user's5irises are contracted or expanded. As described in more detail below, these additional parameters may also be used when classifying the interaction of the user5and electronic device10as ergonomic or unergonomic.

Light sensor62may comprise a device (or an array of devices) that may measure or detect the amount of light within an environment. In general, the light sensor62may detect ambient light levels within the environment surrounding electronic device10, and communicate the detected light to the controller50. The controller50may then analyze the output signals from light sensor62to make determinations, such as, the ambient light levels of the environment surrounding electronic device10.

Microphone64comprises a device (or an array of devices) that may receive or collect sound waves traveling within the environment surrounding electronic device10. The collected sound waves may be communicated to controller50, which may then analyze the collected sound waves to determine a number of parameters, such as, the ambient noise levels, pitch and/or frequency of a person's voice, etc.

Humidity sensor66may measure or detect the relative humidity within the environment surrounding electronic device10. The humidity sensor66may measure or detect both water vapor and the temperature within the environment surrounding the electronic device10to allow a determination of the relative humidity within the environment surrounding electronic device10(e.g., via controller50) during operations. Without being limited to this or any other theory, the relative humidity of the environment surrounding the electronic device10may affect a user's ability to clearly see the images emitted from display device18. Accordingly, as the relative humidity increases, other parameters of the display device18may also be adjusted (e.g., font size, brightness) to avoid unergonomic conditions.

In addition, electronic device10may also include an angle detecting sensor68that may detect or measure the rotative position of first housing member14about hinge13. In some examples, the angle detecting sensor68may measure or detect the angle α between the first housing member14and second housing member16shown inFIG.1. Angle detecting sensor68may comprise any suitable device (or devices) for detecting the rotative of position of the first housing member14relative to the second housing member16about hinge13(e.g., gyroscopes, magnetic sensor such as a hall effect sensor). During operations, output signals from angle detecting sensor68may be used by controller50to determine the angle α shown inFIG.1.

Referring still toFIGS.1and2, during operations, controller50may receive data from the image sensor40, light sensor62, microphone64, humidity sensor66, angle detecting sensor68, and potentially other sources. Using the received data, controller50may classify the interaction of the user5with the electronic device10as ergonomic or unergonomic.

For instance, based on the received data, controller50may determine the position and orientation of the user5relative to a display device18. As previously described, the position and orientation of user5may be characterized by various measurements, angles, and other parameters (e.g., D, H, a,6, P). The data received and used by controller50to determine these various position-characterizing parameters may be referred to herein as “position data.” The position data may be received by controller50from one or a plurality of sources (e.g., image sensor40, angle detecting sensor68).

In addition, based on the received data, controller50may determine various output parameters of the display device18. For instance, the output parameters of the display device18may comprise, display brightness, display contrast, font size, etc. The data received and used by controller50to determine the output parameters of the display device18may be referred to has “image output data.” The image output data may be received by controller50from one or a plurality of sources (e.g., display device18, a GPU of electronic device10, CPU of electronic device10).

Further, based on the received data, the controller50may determine an environmental condition of the environment surrounding the electronic device10. The environmental condition may comprise the ambient noise levels, volume output by the speaker60, ambient light brightness or intensity, and/or relative humidity of the environment surrounding electronic device10. The data received and used by the controller50to determine the environmental condition may be referred to herein as “environmental data,” The environmental data may be received by controller50from one or a plurality of sources (e.g., microphone64, light sensor62, humidity sensor66, speaker60).

The controller50may use a first machine learning model to classify the interaction of the user5and the electronic device10as unergonomic or ergonomic. In particular, the controller50may provide the position data, the image output data, and/or the environmental data (and/or the parameters determined by the controller50using the position data, image output data, and/or environmental data as described above) to the first machine learning model, and in turn, the first machine learning model may provide an output indicative of the classification of the interaction of the user5and electronic device10. The first machine learning model may classify the interaction of the user5and electronic device10into one of two categories: a first ergonomic category to indicate the interaction is unergonomic; and a second ergonomic category to indicate that the interaction is ergonomic.

In some examples, the first machine learning model may comprise a logistic regression model, a random forest model, an extreme gradient boosting model, etc. In some examples, the first machine learning model may classify the current interaction of the user5with the electronic device10as either ergonomic or un-ergonomic based on relationships between the position data, the environmental data, and/or the image output data (or parameters determined by the controller50using the position data, image output data, and/or the environmental data).

In some examples, the data provided to the first machine learning model may first be processed by the controller50. For instance, in some examples, the data provided to the first machine learning model (e.g., the position data, image output data, environmental data, and/or other parameters determined therewith), may be subjected to filtering, normalization, transformation, conversion, and/or other processing techniques.

In some examples, the first machine learning model may be trained and selected using labeled data. The labeled data may comprise position data, environmental data, and/or image output data (and/or parameters that may be determined using the position data, environmental data, and/or image output data as described above) that are known to correspond with an ergonomic or unergonomic interaction between a user and an electronic device. The labeled data may be derived from experimentation in a controlled environment (e.g., laboratory, factory, research facility, office), and may provide data and parameters to represent a wide variety of situations and scenarios (e.g., such as users having different builds, sizes, genders, ages as well as different environmental conditions).

To select and train the first machine learning model, the labeled data may be provided to a plurality of classification models (which may comprise logistic regression models, random forest models, extreme gradient boosting models, and/or other types of machine learning models) to derive and validate the coefficients for the plurality of classification models to properly classify interactions represented by the labeled data as ergonomic or unergonomic. More specifically, in some examples the plurality of classification models may be provided with a first portion of the labeled data to derive the coefficients for the models, and a second portion (different from the first portion) of the labeled data to validate the derived coefficients. In some examples, a third portion (different from the first and second portions) of the labeled data may be used to test the plurality of classification models following derivation and validation of the coefficients to determine which of the models provides the most accurate prediction of ergonomic or unergonomic interaction between a user and the electronic device10. In some examples, the first portion of the labeled data may comprise approximately 60% of the labeled data, and the second and third portions of the labeled data may each comprise approximately 20% of the labeled data.

In some examples, the training and selection of the first machine learning model may be performed at a controlled environment (e.g., laboratory, factory, research facility, office), so that when a user interacts with the electronic device10(e.g., following purchase or assignment of the electronic device10), the controller50may receive the position data, the environmental data, and/or the image output data and use the received data (and/or parameters determined using the received data) with the first machine learning model (which was previously trained and selected with labeled data as described above) to classify the interaction of the user and the electronic device10as ergonomic or unergonomic. In some examples, the controller50may refine the coefficients of the first machine learning model using collected data of the user5.

In some examples, if the interaction of the user5and the electronic device10is classified as unergonomic via the the first machine learning model, the position data, image output data, the environmental data, and/or other parameters determined therewith may be used with a second machine learning model to determine corrections to a parameter or parameters of the electronic device10to change the classification of the interaction from unergonomic to ergonomic. For instance, in some examples, the second machine learning model may comprise a clustering model (e.g., a centroid-based clustering model, a distribution-based clustering model, a density-based clustering model, a grid-based clustering model, a hierarchical clustering model) that compares the received data (e.g., position data, environmental data, and/or image output data) to benchmark data sets, and determines a correction to a parameter of the received data that would change the classification obtained from the first machine learning model from unergonomic to ergonomic.

For instance, reference is now made toFIG.3, which shows a plurality of benchmark data sets82a-82f, each including a number of data points corresponding to the position data, image output data, environmental data, and/or parameters determined therewith. In addition, each benchmark data set82a-82fmay include an “ergonomic indicator” to indicate whether the benchmark data sets82a-82fcorrespond with either an ergonomic or unergonomic interaction between a user and the electronic device10. Because the ergonomic indicator may represent a binary classification, it may be represented in the benchmark data sets82a-82fas either a “1” for an ergonomic interaction or a “0” for an unergonomic interaction. In some examples, the benchmark data sets82a-82fmay comprise the labeled data (or a subset thereof) used to train the first machine learning model. In some examples, the benchmark data sets82a-82f(or some of the benchmark data sets82a-82f) may be derived from past collected data from the user5(FIG.1) that was confirmed to correspond with either an ergonomic or unergonomic interaction with the electronic device10(FIG.1).

Referring now toFIGS.1-3, during operations the second machine learning model may compare the received data (e.g., position data, image output data, environmental data, and/or parameters determined therewith) with the benchmark data sets82a-82fand based on this determination may determine that the received data is clustered with one (or a plurality) of the benchmark data sets82a-82f. A comparison of the received data and the selected benchmark data sets82a-82f(e.g., via error comparison) may yield a suggested correction to a parameter or parameters of the electronic device10that may change the classification of the interaction from unergonomic (having an ergonomic indicator of “0”) to ergonomic (having an ergonomic indicator of “1”).

In some examples, the second machine learning model may determine a correction to: (1) the angular position of the display device18about the hinge13(e.g., as represented by the angle α); (2) an output parameter of the display device18(e.g., font size, brightness); and/or (3) a volume output from the speaker60. In some examples, the second machine learning model may comprise a plurality of models as described above—each model to determine a correction for a given parameter (e.g., the angle α, an output parameter of display device18, the volume of speaker60) of the electronic device10to change the classification obtained from the first machine learning model from unergonomic to ergonomic. The adjusted parameter and the magnitude of the correction(s) may depend on the comparison of the received data with the benchmark data performed by the second machine learning model.

In an example scenario, user5may be sitting too close to the display device18, which may cause the distance D, angles α, β, and other parameters to be associated with values that may result in an unergonomic classification for the ergonomic indicator via the first machine learning model. Accordingly, a comparison of the received data with the benchmarking data sets82a-82fvia the second machine learning model may provide a correction to the angular position of the display device18about hinge13(e.g., as represented by the angle α) that may better correspond the received data with one of the benchmark data sets82a-82cthat includes an ergonomic indicator of “1” (to indicate an ergonomic interaction).

In another example scenario, user5may be viewing images with a font size that is too small for the viewing distance D and/or may be using a brightness setting on the display device18that is incompatible with the ambient light intensity, such that the received data is classified as unergonomic via the first machine learning model. Accordingly, a comparison of the received data with the benchmarking data sets82a-82fvia the second machine learning model may provide corrections to the font size and/or brightness of the display device18that may better correspond the received data with one of the benchmark data sets82a-82cthat includes an ergonomic indicator of “1” (to indicate an ergonomic interaction).

In still another example scenario, user5may be using a volume setting for the speaker60that is inappropriately high based on the viewing distance D and angle of the head of the user5relative to speaker60(which may be determined or inferred using the angle β and/or the angle θ) such that the received data is classified as unergonomic via the first machine learning model. Accordingly, a comparison of the received data with the benchmarking data sets82a-82fvia the second machine learning model may provide corrections to the volume output from the speaker60that may better correspond the received data with one of the benchmark data set82a-82cthat includes an ergonomic indicator of “1” (to indicate an ergonomic interaction).

In some examples, the corrections to the parameter(s) of the received data determined via the second machine learning model may be communicated to the user, such that the user5may manually, via physical interaction with the electronic device10and/or suitable menu selections provided by the electronic device10, implement the suggested changes. However, in some examples, the electronic device10may automatically implement the suggested corrections determined by the second machine learning model.

For instance, as shown inFIG.2, in some examples, a driver70may be coupled to the hinge13and may be actuated by controller50to rotate the first housing member14about the hinge13relative to the second housing member16to thereby adjust the angle α shown inFIG.1. Driver70may comprise any suitable driving device or assembly (e.g., electric motor, magnetic motor, linear actuator). Thus, during operations, controller50may actuate driver70to adjust the angular position of the display device18based on a correction determined by the second machine learning model. In addition, in some examples, the controller50may automatically adjust various output parameters of the display device18, such as, for instance font size and/or brightness, and/or the volume output of speaker60based on the correction(s) determined by the second machine learning model.

Additional examples of the machine-readable instructions100,200,300that may be executed by processor52of controller50to perform the functions generally described above will now be discussed herein. The machine-readable machine-readable instructions100,200,300may comprise examples of machine-readable instructions56shown inFIG.2. Thus, in describing the features of machine-readable instructions100,200,300, continuing reference will be made to the features shown inFIGS.1-3and described above.

Referring now toFIG.4, in some examples, the controller50may execute machine-readable instructions100to adjust angular position of the display device18(e.g., the angle α shown inFIG.1) or an output from the display device18to change a classification of a user's interaction with the electronic device10. Specifically, machine-readable instructions100include receiving position data from a sensor40at block102. As previously described, the position data may comprise data that indicates the position and orientation of a user (e.g., or a particular body-part of the user such as the head) relative to the display device18. In addition, as is also previously described, the position data may be received by the image sensor40, and/or other sensors (e.g., angle detecting sensor68).

In addition, the machine-readable instructions100include using a machine learning model and the position data to classify an interaction of the user with the electronic device10in a first ergonomic category or a second ergonomic category at block104. The machine learning model may comprise the first machine learning model described above. The first ergonomic category may correspond with an interaction that is considered unergonomic such that the interaction may cause injury. Conversely, the second ergonomic category may correspond with an interaction that is considered ergonomic such that the interaction may avoid injury.

Further, the machine-readable instructions100include adjusting an angular position of the display device18or an output from the display device18response to a classification of the interaction in the first ergonomic category at block106. For instance, in some examples, the adjustment of the angular position or the output parameter may be determined via the second machine learning model as described above. In addition, the adjustment may be implemented by the controller50by actuating a driver (e.g., driver70shown inFIG.2) of the electronic device10, and/or by adjusting signals that are communicated to the display device18(e.g., to adjust font size, brightness).

Referring now toFIG.5, in some examples, controller50may execute machine-readable instructions200to adjust an output of the display device18to change a classification of a user's interaction with the electronic device10. Specifically, machine-readable instructions include obtaining position data from an image sensor at block202. The image sensor used to obtain the position data may comprise the image sensor40previously described above.

In addition, machine-readable instructions200include using a first machine learning model and the position data to classify an interaction of the user with the electronic device10in a first ergonomic category at block204. The first ergonomic category may correspond with an interaction that is considered unergonomic, such that the interaction may cause injury. In addition, the first machine learning model in block204may comprise the first machine learning model described above.

Further, machine-readable instructions200include using a second machine learning model to determine a correction to an output of the display device18to classify the interaction in a second ergonomic category at block206. In some, examples, the second ergonomic category may correspond with an interaction that is considered ergonomic such that the interaction may avoid injury. In addition, the second machine learning model of block206may comprise the second machine learning model described above.

Still further, machine-readable instructions300include adjusting the output of the display device based on the correction at block208. The adjustment may comprise a change to an output parameter of the display device18, such as, for example, the font size and/or the brightness of the display device18.

Referring now toFIG.6, some examples may comprise machine-readable instructions for adjusting an angular position of a display device or an output from the display device to change a classification of a user's interaction with the electronic device10. Specifically, machine-readable instructions300may comprise obtaining position data of a user using an image sensor at block302. As described above, the position data may comprise data that indicates a position and orientation of the user relative to the display device18. In some examples, the image sensor may comprise the image sensor40described above.

In addition, the machine-readable instructions300may comprise obtaining image output data for the display device at block304. As described above, the image output data may comprise information related to images output by the display device. In some examples, the image output data may comprise output parameters such as font size, brightness, contrast, etc. of the display device18.

Further, the machine-readable instructions300may comprise obtaining environmental data at block306. As described above, the environmental data may comprise an environmental condition within the environment surrounding the electronic device. The environmental data may be obtained by a plurality of sensors, such as an ambient light sensor (e.g., ambient light sensor62inFIG.2) and/or a humidity sensor (e.g., humidity sensor66inFIG.2), a microphone (e.g., microphone64inFIG.2).

Still further, machine-readable instructions300include using the position data, the image output data, the environmental data, and a machine learning model to classify an interaction of the user with the electronic device in a first ergonomic category. The first ergonomic category may correspond with an interaction that is considered unergonomic, such that the interaction may cause injury. In addition, the first machine learning model may comprise the first machine learning model described above.

Also, the machine-readable instructions300include adjusting an angular position of the display device or an output from the display device to change the classification of the interaction to a second ergonomic category. In some, examples, the second ergonomic category may correspond with an interaction that is considered ergonomic such that the interaction may avoid injury. In addition, as described above, the angular position of the display device may be adjusted by actuating a driver coupled to the hinge13(e.g., driver70shown inFIG.2). Also, as described above, the output from the display may be adjusted by adjusting one of a font size, brightness, etc. for the images output by the display device.

Accordingly, examples disclosed herein include electronic devices that may receive data from a sensor (or a plurality of sensors), and based on the received data, may classify a user's interaction with the electronic device as either ergonomic or unergonomic. Thus, through use of the examples disclosed herein, a user may more consistently achieve and maintain ergonomic interaction with the electronic device so as to avoid injury.

In the figures, certain features and components disclosed herein may be shown exaggerated in scale or in somewhat schematic form, and some details of certain elements may be omitted in the interest of clarity and conciseness. In some of the figures, in order to improve clarity and conciseness, a component or an aspect of a component may be omitted.

In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . .” Also, the term “couple” or “couples” is intended to be broad enough to encompass both indirect and direct connections. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices, components, and connections.

As used herein, including in the claims, the word “or” is used in an inclusive manner. For example, “A or B” means any of the following: “A” alone, “B” alone, or both “A” and “B.”