WO2021221634A1

WO2021221634A1 - Ergonomic usage recommendations

Info

Publication number: WO2021221634A1
Application number: PCT/US2020/030507
Authority: WO
Inventors: Gaurav ROY; Michael W Cumbie; Louis R. JACKSON, Jr.; Padma Jangala
Original assignee: Hewlett-Packard Development Company, L.P.
Priority date: 2020-04-29
Filing date: 2020-04-29
Publication date: 2021-11-04

Abstract

In an example implementation according to aspects of the present disclosure, a system, method, and storage medium for ergonomic usage recommendations. A processor receives a computing device information profile. The processor receives a set of environment attributes and a set of usage attributes from a plurality of sensors. The processor determines an ergonomic score based on the computing device information profile, the environment attributes and the usage attributes. The processor determines an ergonomic recommendation based on the score and displays the ergonomic recommendation.

Description

ERGONOMIC USAGE RECOMMENDATIONS

BACKGROUND

[0001] Humans interface with computers in different environments. Different environments impact humans and their working efficiency. The study of ergonomics may lead to better interfacing between humans and computers.

BRIEF DESCRIPTION OF THE DRAWINGS

[0002] FIG. 1 illustrates a system for creating ergonomic usage recommendations, according to an example;

[0003] FIG. 2 is a block diagram corresponding to a method for creating ergonomic usage recommendations, according to an example; and

[0004] FIG. 3 is a computing device for supporting instructions for creating ergonomic usage recommendations, according to an example.

DETAILED DESCRIPTION

[0005] Humans interface with various types of computer systems in various types of environments. Some instances of the interfacing are not optimal nor efficient. The user may find themselves straining to use the computer system efficiently. In other instances, the user may find themselves in discomfort during sustained usage of the computer system. Described herein is a system, method and computer readable medium for ergonomic usage recommendations.

[0006] In an example, a system may include a plurality of sensors and a processor couple to the plurality of sensors. The processor may receive a computing device information profile, a set of environmental attributes from the sensors, and a set of usage attributes from the sensors. The processor may determine an ergonomic score based on the received profile and attributes. The processor may determine an ergonomic recommendation and display the ergonomic recommendation.

[0007] In another example, a method including receiving a computing device information profile, a set of environment attributes from sensors, receiving a set of usage attributes from the sensors, determining an ergonomic score based on the computing device information profile, the environment attributes, and usage attributes, and transmitting an adjustment instruction to a peripheral device based on the ergonomic score.

[0008] FIG. 1 illustrates a system 100 for creating ergonomic usage recommendations, according to an example. The system 100 may include sensors 106, a processor 102, and instructions 104.

[0009] The sensors 106 may include but are not limited to fitness trackers, web cameras, and input devices, such as keyboards and mice. The sensors 106 may include other connected devices including integrated camera arrays, time of flight sensors, touch screen, and stylus. The sensors 106 may be communicatively coupled with the processor 102. The communicative coupling may be implemented with standardized bus technologies as part of an integrated device. In another implementation, the sensors 106 may be wirelessly connected utilizing standardized or proprietary connection protocols and interfaces, such as Wi-Fi®, Bluetooth® or Near Field Communication (NFC) techniques. In another implementation the sensors 106 may be networked through a wireline network. The wireline network may be electrically connected through switching and routing systems, utilizing a networking interface to communicate with the processor 102.

[0010] The processor 102 may be implemented as a general-purpose processor such as a central processing unit (CPU). The processor 102 may also be implemented as a virtual processor. A virtual processor may be abstracted from a specific piece of hardware and may be defined by the workload it processes. A virtual processor may be a cloud processing instance, or a virtual machine instance. The processor 102 may be connected to supporting electronics including a host system and a network (not shown) to facilitate the operation of the processor.

[0011] The processor 102 may be able to process instructions 104. The instructions 104 may be firmware or software to change or control the behavior of the processor 102. For example, instructions 104 for the processor may include receiving computing device information profile, receiving environmental attributes, determining usage attributes, determining an ergonomic score, and displaying an ergonomic recommendation. The instructions may be stored in a non-transitory storage medium.

[0012] FIG. 2 is a block diagram corresponding to a method for creating ergonomic usage recommendations, according to an example.

[0013] At 202, the processor 102 receives computing device information profile. The computing device information profile may include information relating to a computing device and user under examination. In an example the computing device information profile may include a user application list. User applications in the list may include software applications that a user may utilize for job responsibilities like word processors, graphical design packages and video production software. The computing device information profile may also include a user age group which may indicate a range of ages for which the user belongs. An ergonomics importance prioritization may be included with the computing device information profile. The ergonomics importance prioritization may include details relating to goals of the user in the working combination with the computing device (e.g. comfort, exercise, productivity, ailments). In another example the computing device information profile may include personal space and desk information to better describe the overall working environment outside of the computing device. In one implementation, the computing device information profile may be a survey provided to a user iteratively during the course of usage of the computing device.

[0014] At 204, the processor 102 receives a set of environmental attributes. The environmental attributes may include lighting levels, speaker volumes, desk settings, and environmental color combinations within proximity of the computing device. The set of environmental attributes may be detected and received from the sensors 106. For example, an integrated webcam may detect lighting levels present through light sampling of captured images. In another example, speaker volumes may be detected through integrated microphones within the computing device when sound is being played through integrated speakers.

[0015] At 206, the processor 102 receives a set of usage attributes. The usage attributes may include details such as working locations, application usage, collaboration, environment, posture, work speed and intensity, accessory usage, and microbreaks. Working location attributes may include the office, home, coffee shop, conference rooms and shared workspaces. The location attribute may be captures as networks change. For example, a user moves from one network at home to another network at work. Internet Protocol (IP) geolocation may be utilized to determine the location as well known or used locations can be landmarked. Additionally, IP address lookups may reveal generalized location attributes. Application usage attributes may include business productivity software, games, and creative content creation software application. In one implementation, the application usage may be collected on a periodic basis. The collection may include utilizing a telemetry agent to capture open executables. Additionally, in Software as a Service (SaaS) implementations, usage may be logged as part of the service. Collaboration attributes may include applications when more than one user is utilizing the computing device and may be determined based the object of a user’s gaze on a computer screen. The collaboration attribute may be deciphered using time of flight sensor. Environmental attributes may include lighting and noise levels surrounding the computing device. Environmental attributes may be collected on location changes. Posture attributes may include sitting, standing, and head location in reference to a collaboration attribute. Posture attributes may be capture head position with regard to screen position via the time of flight sensor. Work speed and intensity attributes may include typing speed, mouse click, mouse drag, touch input, and stylus input. Work speed and intensity attributes may be captured by typing speed and mouse statistics. Accessory usage attributes may include which accessories are active using peripheral connectivity as well as the time the accessories are active. Breaks and micro-pause attributes may include information regarding the frequency and counts, detected by periods of inactivity.

[0016] At 208, the processor 102 determines an ergonomic score. The ergonomic score processing raw input data, imputing missing data, binning data, and calculating a weight of evidence and information value. The input data may be retrieved from a database comprising, the computing device information profile, and the usage attributes. An example of input data as a database schema may be visualized in table 1.

Table 1

[0017] The processor 102 may impute a set of missing usage attributes. If a value is not present in the database for the last 30 days, the processor may adapt the existing data to account for it. In one example the processor 102 may use a mean value for previous ranges. The mean value may be fitted as a linear regression curve. This approach may allow the processor 102 to extrapolate using a simple curve by examining previous values. In another example, pertaining to a new user who may not have a complete set of usage attributes, utilizing all of the values in Table 1 across all users in the database to create an average as a seed value for the new user.

[0018] The processor 102 may bin the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes. In some implementations, real world data corresponding to the usage attributes may include noise. Noise is data with a large amount of additional meaningless information. The binning of the usage attributes may include routines attempt to smooth out noise while identifying outliers in the usage attributes. Binning may smooth the sorted usage attribute values by evaluating a given value against values around it in the set and discarding values that deviate outside a threshold. Another implementation may include regression analysis where the usage attribute data may be conformed to a function. A linear regression involves determining a best line to fit two attributes (or values) so that one attribute can be used to predict the other. In another implementation, outlier analysis may be applied. Outliers may be detected by clustering. Similar values may be organized into groups. Values that fall outside of the set of clusters may be considered outliers.

[0019] For example, the computing device information profile may be cross- referenced with the usage attributes, the users may be binned as per computing device information profile classification and usage attributes. For instance, a designer may play computer games more often than designing at home. In this instance outliers may be filtered using fixed bins (e.g. recreational active usage for more than 4 hours a day is aberrant). Bins may be defined as per age and industry for each usage attribute as show below on Table 2.

Table 2

[0020] The approach described above allows for the identification of outlier data collection errors. In some instances, bins make some sense to the classification. For example: For the user whose usage attribute data is represented in Table 1 , may be due to a snowed-in for a week, a vacation cancellation, or a pandemic quarantine. The user may have had more time to play.

[0021] The processor 102 may determine a weight for each of the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes. The weight may be described as similar to a weight of evidence utilized in credit scoring. In one implementation the weight of evidence may correspond to how good or bad is the bad ergonomics affecting this month (or thirty-day period). The weight of evidence may be represented as Equation 1.

Distribution Good

In— - ; - - — - * 100

Distribution Bad

Equation 1.

[0022] Additionally, an information value provides additional insight across the dataset. The information value corresponds to how predictable is the good or bad ergonomics. The information value answers the question “is this a chronic situation?” The information value may be represented as Equation 2.

Equation 2.

[0023]Table 3 visualizes a two-week example for a posture usage attribute.

Table 3

[0024] Similarly utilizing Equation 2, an information value of 0.24 may be determined using the data in Table 3. In some implementations, certain usage attributes may utilize a weight of evidence value. For example, in some implementations, Age, Industry may not have a weight of evidence value calculated. However, in the same implementation %TimelnBadPosture, %TimelnBadl_ocationg, %TimeSitting, and %TimeBadEnv may have a weight of evidence value calculated. In some implementations, the weight of evidence may be calculated on a per week basis. A weighted summation may be utilized for a final weight of evidence. The weighted summation may apply a weight to a weekly weight of evidence.

[0025] The processor 102 may finalize a score per usage attribute. The score may be a relative score, where the score lies with respect to the general population for which the system collects a computing device information profile, the set of environmental attributes and the set of usage attributes. The score may be a percentage score and dependent on the rest of the population within the dataset. In one implementation the scoring may be done on an age and industry combination from the computing device information profile. In this implementation, the scoring percentages would correspond relatively to all uses within a given age and a given industry.

[0026] Additionally, in calculating the score, factors may be utilized for adjusting the usage attributes. For example, Table 4 illustrates the calculation of a score utilizing usage attribute-based factoring.

Table 4.

[0027] In other implementations, a factor may not be included and therefore may be omitted or set to one for the multiplicative effect.

[0028] At 210, the processor 102 transmits an adjustment instruction. Upon reaching a score, the processor 102 may compare the score to a threshold. The threshold may correspond a critical point. The score traversing the threshold indicates an action point. In one example, assuming the environment component of the score is low, the processor 102 may send instructions to take corrective action. Corrective action may be a user executed action. The corrective action may take the form of an ergonomic recommendation displayed on the display of the computing device associated with the user. For example, if the micro breaks component of the score is low, the processor 102 may promote notifications to the user through agent software to take more breaks. The processor 102 may also promote notifications for the user to relocate to an environment that is more ergonomically favorable. In another example, the processor 102 may adjust components of the system that may be programmatically adjusted. A sound system, including speaker and/or microphone may be programmatically adjusted by the computing device.

[0029] The processor 102 may update the computing device information profile based on the ergonomic score. As the processor 102, identifies strengths and weaknesses in ergonomics through scoring and usage, the processor 102 may update the computing device information profile based on the insights obtained. Additionally, the processor 102 may iteratively re-create the computing device information profile based on feedback from a user. One implementation may be a follow up survey. A follow up survey to update the computing device information profile may allow the processor 102 to update and refine computational aspects including the factoring.

[0030] FIG. 3 is a computing device for supporting instructions for creating ergonomic usage recommendations, according to an example. The computing device 300 depicts a processor 102 and a storage medium 304 and, as an example of the computing device 300 performing its operations, the storage medium 404 may include instructions 306-316 that are executable by the processor 102. The processor 102 may be synonymous with the processor 102 referenced in FIG. 1. Additionally, the processor 102 may include but is not limited to central processing units (CPUs). The storage medium 304 can be said to store program instructions that, when executed by processor 102, implement the components of the computing device 300. The executable program instructions stored in the storage medium 304 include, as an example, instructions to receive a computing device information profile 306, instruction to receive a set of environmental attributes 308, instruction to receive a set of usage attributes 310, instructions to determine an ergonomic score 312, instructions to determine a weight of evidence value and an information value 314, and instructions to create an ergonomic recommendation 316.

[0031]Storage medium 304 represents generally any number of memory components capable of storing instructions that can be executed by processor 102. Storage medium 304 is non-transitory in the sense that it does not encompass a transitory signal but instead is made up of at least one memory component configured to store the relevant instructions. As a result, the storage medium 304 may be a non-transitory computer-readable storage medium. Storage medium 304 may be implemented in a single device or distributed across devices. Likewise, processor 102 represents any number of processors capable of executing instructions stored by storage medium 304. Processor 102 may be integrated in a single device or distributed across devices. Further, storage medium 304 may be fully or partially integrated in the same device as processor 102, or it may be separate but accessible to that computing device 300 and the processor 102.

[0032] In one example, the program instructions 306-316 may be part of an installation package that, when installed, can be executed by processor 102 to implement the components of the computing device 300. In this case, storage medium 304 may be a portable medium such as a CD, DVD, or flash drive, or a memory maintained by a server from which the installation package can be downloaded and installed. In another example, the program instructions may be part of an application or applications already installed. Here, storage medium 304 can include integrated memory such as a hard drive, solid state drive, or the like.

[0033] It is appreciated that examples described may include various components and features. It is also appreciated that numerous specific details are set forth to provide a thorough understanding of the examples. However, it is appreciated that the examples may be practiced without limitations to these specific details. In other instances, well known methods and structures may not be described in detail to avoid unnecessarily obscuring the description of the examples. Also, the examples may be used in combination with each other. [0034] Reference in the specification to “an example” or similar language means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example, but not necessarily in other examples. The various instances of the phrase “in one example” or similar phrases in various places in the specification are not necessarily all referring to the same example.

[0035] It is appreciated that the previous description of the disclosed examples is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these examples will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other examples without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the examples shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A system comprising: a plurality of sensors; a processor, communicatively coupled to the plurality of sensors, to: receive a computing device information profile; receive a set of environment attributes from the plurality of sensors; receive a set of usage attributes from the plurality of sensors; determine an ergonomic score based on the information profile, the set of environment attributes, and the set of usage attributes; determine an ergonomic recommendation based on the ergonomic score; and display, on a display, the ergonomic recommendation.

2. The system of claim 1 further comprising the processor to send an adjustment instruction to a peripheral device.

3. The system of claim 1 , further comprising the processor to determine an ergonomic score: imputing a set of missing usage attributes; binning the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes; and determining a weight for each of the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes.

4. The system of claim 1 wherein the set of usage attributes comprises location data, application usage data, work speed data, and accessory usage data.

5. The system of claim 1 wherein the set of environmental attributes comprises lighting levels, speaker volumes, desk settings, and environmental color combinations.

6. A method comprising: receiving a computing device information profile; receiving a set of environment attributes from a plurality of sensors; receiving a set of usage attributes from the plurality of sensors; determining an ergonomic score based on the information profile, the set of environment attributes, and the set of usage attributes; and transmitting an adjustment instruction to a peripheral device based on the ergonomic score.

7. The method of claim 6, the determining an ergonomic score further comprising: imputing a set of missing usage attributes; binning the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes; and determining a weight for each of the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes.

8. The method of claim 6, wherein the set of usage attributes comprises location data, application usage data, work speed data, and accessory usage data.

9. The method of claim 6 wherein the set of environmental attributes comprises lighting levels, speaker volumes, desk settings, and environmental color combinations.

10. The method of claim 6 further comprising updating the computing device information profile based on the ergonomic score.

11. A non-transitory computer readable medium comprising instructions executable by a processor to: receive a computing device information profile; receive a set of environment attributes from the plurality of sensors; receive a set of usage attributes from the plurality of sensors; determine an ergonomic score based on the information profile, the set of environment attributes, and the set of usage attributes; determine a weight of evidence value and an information value based on the set of usage attributes, set of environment attributes, and the computing device information profile; create an ergonomic recommendation based at least in part on the ergonomic score, the weight of evidence value and the information value.

12. The non-transitory computer readable medium of claim 11 further comprising the instructions to send an adjustment instruction to a peripheral device based on the ergonomic recommendation.

13. The non-transitory computer readable medium of claim 11 , the instructions to determine an ergonomic score further comprising: impute a set of missing usage attributes; bin the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes; and determine a weight for each of the set of environmental attributes, the set of usage attributes, and the set of missing usage attributes.

14. The non-transitory computer readable medium of claim 11 wherein the set of usage attributes comprises location data, application usage data, work speed data, and accessory usage data.

15. The non-transitory computer readable medium of claim 11 wherein the set of environmental attributes comprises lighting levels, speaker volumes, desk settings, and environmental color combinations.