Methods and systems of provisioning an intervention based on an ergonomic risk assessment associated with a user

Abstract

A method of provisioning an intervention based on an ergonomic risk assessment associated with a user. Further, the method may include receiving an ergonomic risk assessment data from a user device associated with the user. Further, the ergonomic risk assessment data corresponds to an evaluation of an ergonomic risk associated with the user. Further, the method may include determining an ergonomic risk score data based on the ergonomic risk assessment data. Further, the ergonomic risk score data corresponds to a score associated with an ergonomic risk. Further, the method may include analyzing the ergonomic risk score data. Further, the method may include generating an intervention data based on the analyzing. Further, the intervention data corresponds to the intervention based on the ergonomic risk. Further, the method may include transmitting the intervention data to the user device.

Description

METHODS AND SYSTEMS OF PROVISIONING AN INTERVENTION BASED ON AN ERGONOMIC RISK ASSESSMENT ASSOCIATED WITH A USERREFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 638,426, titled “Systems and Methods for Determining an Ergonomic Risk Assessment Score”, filed on 04 / 25 / 2024, which is incorporated by reference herein in its entiretyFIELD OF DISCLOSURE

[0002] The present disclosure relates to the field of data processing. More specifically, the present disclosure relates to methods and systems of provisioning an intervention based on an ergonomic risk assessment associated with a user.BACKGROUND

[0003] The field of data processing is technologically important to several industries, business organizations, and / or individuals. Existing techniques for determining an ergonomic risk assessment score are deficient with regard to several aspects. The modern workplace is characterized by a dynamic nature and diverse occupational demands, presenting many challenges to employee health and productivity, particularly in the field of ergonomics.Ergonomics is important in designing workplace systems, processes, and products that optimize human well-being and overall work performance. However, traditional ergonomic assessment methods often fail to analyze the complex and specific nature of human-environment interactions within the workplace.

[0004] Historically, ergonomic assessments have been conducted through a combination of subjective self-reports, direct observation, and the use of tools to evaluate the physical aspects of workspaces, such as desk height, chair support, and monitor position. While these approaches provide valuable insights, they are limited by their static nature, lack of adequate evaluation protocols, and inability to effectively capture data relating to the worker's posture and interaction with elements of the working environment.

[0005] With the advent of advanced technologies, there is a growing need for more sophisticated and personalized ergonomic assessment tools that compute specific ergonomic data and includepredictive analytics for understanding user interaction in the workplace. Further, current technologies do not assess the physical setup of the workplace and consider the nature of the work, the individual's physical and psychological characteristics, and the dynamic interactions between the worker and their environment. Further, the current technologies do not move beyond reactive solutions to preventive strategies that can adapt to the workforce's and the workplace's evolving needs. Furthermore, the current technologies do not provide an effective system for monitoring and assessing the ergonomic risk of a worker, combined with learning modules and exams carried out by ergonomists for high-risk cases.

[0006] Therefore, there is a need for improved methods and systems of provisioning an intervention based on an ergonomic risk assessment associated with a user that may overcome one or more of the above-mentioned problems and / or limitations.SUMMARY OF DISCLOSURE

[0007] This summary is provided to introduce a selection of concepts in a simplified form, that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this summary intended to be used to limit the claimed subject matter’s scope.

[0008] The present disclosure provides a method of provisioning an intervention based on an ergonomic risk assessment associated with a user. Further, the method may include receiving, using a communication device, an ergonomic risk assessment data from a user device associated with the user. Further, the ergonomic risk assessment data corresponds to an evaluation of an ergonomic risk associated with the user. Further, the method may include determining, using a processing device, an ergonomic risk score data based on the ergonomic risk assessment data. Further, the ergonomic risk score data corresponds to a score associated with an ergonomic risk. Further, the method may include analyzing, using the processing device, the ergonomic risk score data. Further, the method may include generating, using the processing device, an intervention data based on the analyzing. Further, the intervention data corresponds to the intervention based on the ergonomic risk. Further, the method may include transmitting, using the communication device, the intervention data to the user device.

[0009] The present disclosure provides a system for provisioning an intervention based on an ergonomic risk assessment associated with a user. Further, the system may include acommunication device. Further, the communication device may be configured for receiving an ergonomic risk assessment data from a user device associated with the user. Further, the ergonomic risk assessment data corresponds to an evaluation of an ergonomic risk associated with the user. Further, the communication device may be configured for transmitting an intervention data to the user device. Further, the system may include a processing device communicatively coupled with the communication device. Further, the processing device may be configured for determining an ergonomic risk score data based on the ergonomic risk assessment data. Further, the ergonomic risk score data corresponds to a score associated with an ergonomic risk. Further, the processing device may be configured for analyzing the ergonomic risk score data. Further, the processing device may be configured for generating the intervention data based on the analyzing. Further, the intervention data corresponds to the intervention based on the ergonomic risk.

[0010] Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.BRIEF DESCRIPTIONS OF DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the applicants. The applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

[0012] Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure.

[0013] Fig. 1 is a flow chart of an ergonomic risk evaluation process, in accordance with some embodiments.

[0014] Fig. 2 is an illustration of a user 200 occupying a workstation, in accordance with some embodiments.

[0015] Fig. 3 illustrates a schematic representation of an ergonomic scoring system, in accordance with some embodiments.

[0016] Fig. 4 is an illustration of an online platform 400 consistent with various embodiments of the present disclosure.

[0017] Fig. 5 is a block diagram of a computing device 500 for implementing the methods disclosed herein, in accordance with some embodiments.

[0018] Fig. 6 illustrates a flowchart of a method 600 of provisioning an intervention based on an ergonomic risk assessment associated with a user, in accordance with some embodiments.

[0019] Fig. 7 illustrates a flowchart of a method 700 of provisioning an intervention based on an ergonomic risk assessment associated with a user including detecting, using the processing device 804, an updated risk level associated with the modified ergonomic risk score data, in accordance with some embodiments.

[0020] Fig. 8 illustrates a block diagram of a system 800 of provisioning an intervention based on an ergonomic risk assessment associated with a user, in accordance with some embodiments.DETAILED DESCRIPTION OF DISCLOSURE

[0021] As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

[0022] Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim limitation found herein and / or issuing here from that does not explicitly appear in the claim itself.

[0023] Thus, for example, any sequence(s) and / or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present disclosure. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

[0024] Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein — as understood by the ordinary artisan based on the contextual use of such term — differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

[0025] Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”

[0026] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description torefer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the claims found herein and / or issuing here from. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

[0027] The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of the disclosed use cases, embodiments of the present disclosure are not limited to use only in this context.

[0028] In general, the method disclosed herein may be performed by one or more computing devices. For example, in some embodiments, the method may be performed by a server computer in communication with one or more client devices over a communication network such as, for example, the Internet. In some other embodiments, the method may be performed by one or more of at least one server computer, at least one client device, at least one network device, at least one sensor and at least one actuator. Examples of the one or more client devices and / or the server computer may include, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a portable electronic device, a wearable computer, a smart phone, an Internet of Things (loT) device, a smart electrical appliance, a video game console, a rack server, a super-computer, a mainframe computer, mini-computer, micro-computer, a storage server, an application server (e.g. a mail server, a web server, a real-time communication server, an FTP server, a virtual server, a proxy server, a DNS server etc.), a quantum computer, and so on.Further, one or more client devices and / or the server computer may be configured for executing a software application such as, for example, but not limited to, an operating system (e.g. Windows, Mac OS, Unix, Linux, Android, etc.) in order to provide a user interface (e.g. GUI, touch-screen based interface, voice based interface, gesture based interface etc.) for use by the one or more users and / or a network interface for communicating with other devices over a communication network. Accordingly, the server computer may include a processing device configured for performing data processing tasks such as, for example, but not limited to, analyzing, identifying,determining, generating, transforming, calculating, computing, compressing, decompressing, encrypting, decrypting, scrambling, splitting, merging, interpolating, extrapolating, redacting, anonymizing, encoding and decoding. Further, the server computer may include a communication device configured for communicating with one or more external devices. The one or more external devices may include, for example, but are not limited to, a client device, a third party database, public database, a private database and so on. Further, the communication device may be configured for communicating with the one or more external devices over one or more communication channels. Further, the one or more communication channels may include a wireless communication channel and / or a wired communication channel. Accordingly, the communication device may be configured for performing one or more of transmitting and receiving of information in electronic form. Further, the server computer may include a storage device configured for performing data storage and / or data retrieval operations. In general, the storage device may be configured for providing reliable storage of digital information. Accordingly, in some embodiments, the storage device may be based on technologies such as, but not limited to, data compression, data backup, data redundancy, deduplication, error correction, data finger-printing, role based access control, and so on.

[0029] Further, one or more steps of the method disclosed herein may be initiated, maintained, controlled and / or terminated based on a control input received from one or more devices operated by one or more users such as, for example, but not limited to, an end user, an admin, a service provider, a service consumer, an agent, a broker and a representative thereof. Further, the user as defined herein may refer to a human, an animal or an artificially intelligent being in any state of existence, unless stated otherwise, elsewhere in the present disclosure. Further, in some embodiments, the one or more users may be required to successfully perform authentication in order for the control input to be effective. In general, a user of the one or more users may perform authentication based on the possession of a secret human readable secret data (e.g. username, password, passphrase, PIN, secret question, secret answer etc.) and / or possession of a machine readable secret data (e.g. encryption key, decryption key, bar codes, etc.) and / or or possession of one or more embodied characteristics unique to the user (e.g. biometric variables such as, but not limited to, fingerprint, palm-print, voice characteristics, behavioral characteristics, facial features, iris pattern, heart rate variability, evoked potentials, brain waves, and so on) and / or possession of a unique device (e.g. a device with a unique physical and / orchemical and / or biological characteristic, a hardware device with a unique serial number, a network device with a unique IP / MAC address, a telephone with a unique phone number, a smartcard with an authentication token stored thereupon, etc.). Accordingly, the one or more steps of the method may include communicating (e.g. transmitting and / or receiving) with one or more sensor devices and / or one or more actuators in order to perform authentication. For example, the one or more steps may include receiving, using the communication device, the secret human readable data from an input device such as, for example, a keyboard, a keypad, a touch-screen, a microphone, a camera and so on. Likewise, the one or more steps may include receiving, using the communication device, the one or more embodied characteristics from one or more biometric sensors.

[0030] Further, one or more steps of the method may be automatically initiated, maintained and / or terminated based on one or more predefined conditions. In an instance, the one or more predefined conditions may be based on one or more contextual variables. In general, the one or more contextual variables may represent a condition relevant to the performance of the one or more steps of the method. The one or more contextual variables may include, for example, but are not limited to, location, time, identity of a user associated with a device (e.g. the server computer, a client device etc.) corresponding to the performance of the one or more steps, environmental variables (e.g. temperature, humidity, pressure, wind speed, lighting, sound, etc.) associated with a device corresponding to the performance of the one or more steps, physical state and / or physiological state and / or psychological state of the user, physical state (e.g. motion, direction of motion, orientation, speed, velocity, acceleration, trajectory, etc.) of the device corresponding to the performance of the one or more steps and / or semantic content of data associated with the one or more users. Accordingly, the one or more steps may include communicating with one or more sensors and / or one or more actuators associated with the one or more contextual variables. For example, the one or more sensors may include, but are not limited to, a timing device (e.g. a real-time clock), a location sensor (e.g. a GPS receiver, a GLONASS receiver, an indoor location sensor etc.), a biometric sensor (e.g. a fingerprint sensor), an environmental variable sensor (e.g. temperature sensor, humidity sensor, pressure sensor, etc.) and a device state sensor (e.g. a power sensor, a voltage / current sensor, a switch-state sensor, a usage sensor, etc. associated with the device corresponding to performance of the or more steps).

[0031] Further, the one or more steps of the method may be performed one or more number of times. Additionally, the one or more steps may be performed in any order other than as exemplarily disclosed herein, unless explicitly stated otherwise, elsewhere in the present disclosure. Further, two or more steps of the one or more steps may, in some embodiments, be simultaneously performed, at least in part. Further, in some embodiments, there may be one or more time gaps between performance of any two steps of the one or more steps.

[0032] Further, in some embodiments, the one or more predefined conditions may be specified by the one or more users. Accordingly, the one or more steps may include receiving, using the communication device, the one or more predefined conditions from one or more and devices operated by the one or more users. Further, the one or more predefined conditions may be stored in the storage device. Alternatively, and / or additionally, in some embodiments, the one or more predefined conditions may be automatically determined, using the processing device, based on historical data corresponding to performance of the one or more steps. For example, the historical data may be collected, using the storage device, from a plurality of instances of performance of the method. Such historical data may include performance actions (e.g. initiating, maintaining, interrupting, terminating, etc.) of the one or more steps and / or the one or more contextual variables associated therewith. Further, machine learning may be performed on the historical data in order to determine the one or more predefined conditions. For instance, machine learning on the historical data may determine a correlation between one or more contextual variables and performance of the one or more steps of the method. Accordingly, the one or more predefined conditions may be generated, using the processing device, based on the correlation.

[0033] Further, one or more steps of the method may be performed at one or more spatial locations. For instance, the method may be performed by a plurality of devices interconnected through a communication network. Accordingly, in an example, one or more steps of the method may be performed by a server computer. Similarly, one or more steps of the method may be performed by a client computer. Likewise, one or more steps of the method may be performed by an intermediate entity such as, for example, a proxy server. For instance, one or more steps of the method may be performed in a distributed fashion across the plurality of devices in order to meet one or more objectives. For example, one objective may be to provide load balancing between two or more devices. Another objective may be to restrict a location of one or more of an inputdata, an output data and any intermediate data therebetween corresponding to one or more steps of the method. For example, in a client-server environment, sensitive data corresponding to a user may not be allowed to be transmitted to the server computer. Accordingly, one or more steps of the method operating on the sensitive data and / or a derivative thereof may be performed at the client device.Overview

[0034] The present disclosure discloses a method aimed at improving ergonomic practices within various work environments. This method includes an approach to adjust workplace items with modifiable parameters, such as seats and steering mechanisms, to align with an individual's physical needs. Said method comprises a process of evaluating comfort levels, applying visual cues for optimal adjustment settings, fine-tuning the items to achieve an ergonomically favorable configuration, and documenting these settings for sustained ergonomic integrity. The method extends to regular audits for adherence to ergonomic standards and proactive monitoring to identify high-risk ergonomic scenarios. The method is applicable across diverse settings, including offices, vehicles, and educational institutions, and aims to educate the users with the necessary guidance and records to maintain an ergonomically healthy workspace, thereby promoting health, safety, and productivity.

[0035] The present disclosure may disclose a method and system focused on minimizing ergonomic risks in workplaces by assessing and planning operational tasks. Said method involves collecting workplace data, segmenting the workplace into areas, evaluating ergonomic risks, and calculating a cumulative ergonomic score. The approach includes assessing each operator for specific risk factors and strategically reallocating workload resources to achieve ergonomic balance. This method is designed for enhance workplace safety and efficiency through targeted interventions and resource optimization.

[0036] The present disclosure may disclose an ergonomic risk mapping process to assess and prioritize risk factors across various job roles and activities within a company using the IDEP system. Said ergonomic risk mapping process incorporates a matrix, the SERTA, workplace details, tasks, associated risks, ergonomic risk, and control measures, compiled with inputs from occupational medicine and absenteeism data. The ergonomic assessments described are further enriched by the Ergonomics Census or Corlett surveys. Risk levels are determined through aformula that multiplies severity, probability, and control indices, categorizing risks from Normal Technical Action to High Risk, and facilitating targeted ergonomic interventions.

[0037] The present disclosure relates to a system and method for ergonomic risk assessment for workplace occupants. The system / method includes the following steps: calculating a user's ergonomic risk score based on the questionnaire data; performing one or more actions based on the results of the said risk score assessment; and generating a set of interventions to mitigate the ergonomic risk of the user in the workplace.

[0038] In a particular embodiment, the ergonomic risk score assessment method includes: presenting a questionnaire through a web interface; the user responds to the questions of said questionnaire; the system tricalculates the user's risk score by summing up the scores obtained by each selected response in the questionnaire and performs an action based on the assessed ergonomic risk score.

[0039] In any embodiment, the ergonomic hazard refers to workplace conditions in which the comfort and / or safety of a user is at risk. In the context of this invention, the term “user” is interchangeable with the terms “workplace occupant”, “worker”, “person”, “employee”.

[0040] In one embodiment, the ergonomic risk of the user in a workplace is correlated to the ergonomic risk score assessed through a questionnaire. In a specific embodiment, a greater value of the assessed ergonomic risk may correspond to a higher hazard for the user in the workplace.

[0041] In one embodiment, the system computes the ergonomic risk of the user and performs one or more actions for reducing and / or monitoring said ergonomic risks. Example actions include, but are not limited to, providing a professional ergonomic assessment conducted by an ergonomist; implementing continuous monitoring of the user's ergonomic risk by sending follow-up emails containing a questionnaire to collect further information from the user; proposing to the user the consultation of one or more learning modules to improve knowledge related to a healthy ergonomics in the workplace. In another embodiment, said action for reducing and / or monitoring the ergonomic risks of the user being performed by the system based at least on the content of the user input, including, for example, a request for information, the user ergonomic risk score assessed via the questionnaire, the user ergonomic risk score calculated in the ergonomist evaluation session, the ergonomic risk score assesses through thefollow-up questionnaires via e-mail, a command, any other suitable such input, or any combination thereof.

[0042] In one embodiment, the ergonomic system described in this invention comprises one or more e-learning modules presented to the user via a web interface. Said e-learning module contents to be learned to reduce the ergonomic hazards of the user in the work environment.

[0043] In one embodiment, the process according to the present invention comprises several steps, including but not limited to, providing one or more web pages to the user, wherein the one or more web pages are configured to present the questionnaire to the user and receive the responses to the questionnaire from the user; after the user completes the questionnaire the ergonomic risk score is calculated based on the response for each question. In a specific embodiment, a numeric value is assigned to the answers to each question; and the ergonomic risk score of the user is based on the sum of said numerical values. In this embodiment, the numerical value assigned at each response reflect the ergonomic risk level related to said response. Based upon the results of said risk score assessment the system formulates and recommends a set of interventions aimed at reducing or monitor over time the ergonomic risk of the user, wherein the said set of interventions comprised, but not limited to: providing a professional ergonomic assessment conducted by a qualified ergonomist, implementing continuous monitoring of the user's exposure to ergonomic hazards, sending follow-up emails containing questionnaires to monitor and update the ergonomic risk score of the user over time.

[0044] In one embodiment, the questionnaire comprises multiple-choice selections and the system computes the user's ergonomic risk score by summing up the score of each answer to the questionnaire. In one embodiment, the questionnaire comprises multiple-choice questions, wherein each response is assigned a numerical value that quantitatively reflects the ergonomic risk associated with the user's selection. In one specific embodiment, the risk levels are categorized as low, medium, or high, the higher the risk of concern, the larger the natural number is given as the risk value. In an alternative embodiment, different scoring systems may be employed to quantify the ergonomic risk of the user.

[0045] In a particular embodiment, the scoring system is numerical. In a particular embodiment, for each answer to each question of the questionnaire is assigned a score and then, the systemsum the scores of each answer and based on this calculation the ergonomic risk score is assigned to the user.

[0046] In one embodiment, the questionnaire comprises one or more questions related to the work environment and / or the job-related tasks of the user and / or the personal attributes of the user. The personal attributes can include, but are not limited to age, gender, fitness, height, body weight, and pre-existing health conditions of the user. The work environment can include, but is not limited to, furniture types and positioning, computing devices, hardware, hand tools, controls, and barriers. The job-related tasks can include, but are not limited to, the user's posture during work, the level of physical exertion perceived, and the frequency of repetitive motions involved in the execution of job tasks.

[0047] In a particular embodiment, the risk score of the user ranges from 1 to 100. In this embodiment, a numerical value ranging from 1 to 19 indicates low ergonomic risk; from 20 to 39, indicates a low to medium ergonomic risk; from 40 to 59, corresponds to medium ergonomic risk; from 60 to 79, indicates medium to high-risk ergonomic risk score; and from 80 to 100, corresponds to high ergonomic risk.

[0048] In another embodiment, the risk score of the user ranges from 1 to 5. In this embodiment, a numerical value ranging from 1 to 2 indicates a low ergonomic risk; a numerical value of 3 indicates a medium ergonomic risk; and a numerical value from 4 to 5 corresponds to high ergonomic risk.

[0049] In one embodiment, after the risk score assessment, the system initiates one or more actions to monitor and / or mitigate the ergonomic risk of the user, said actions are based on the assessed risk score. Actions may include, but are not limited to, a professional ergonomic assessment by an ergonomist; continuous risk monitoring through follow-up emails with questionnaires; and offering educational modules on workplace ergonomics.

[0050] In one embodiment, the await review step occurs in response to the assessed low risk level and includes sending a follow-up email to the user that contains a questionnaire designed to monitor and reassess the ergonomic risk score of the user. The frequency of these emails can be adjusted to any preferred interval, from daily, weekly, to monthly. Upon completion of the questionnaire by the user, the system recalculates the risk score based on the user's answer and takes an action according to the following criteria: for a low-risk level, no further action is taken;for a medium-risk level, the system informs the user to repeat the self-assessment process; and for high-risk level, the system takes an urgent action, offering an ergonomist-led evaluation to the user. Following the evaluation, the risk score is updated, and the system continues to monitor the ergonomic risk of the user.

[0051] In one embodiment, If the user does not respond to the questionnaire, the system will send one or more reminder notifications, in this context, if said reminders do not culminate in the completion of the questionnaire, the system is programmed to stop any further actions.

[0052] For a risk score assessed as low to medium, the 'closely monitor' step involves reinitiating the assessment process, ensuring ongoing monitoring and evaluation of the user's ergonomic risk.

[0053] In one embodiment, the offer of an ergonomist evaluation is an action performed in response to an assessed risk score from medium to high. In this embodiment, after the ergonomist evaluation, the user risk score is updated, and after this step the system acts to send a series of follow-up emails containing questionnaires to the user, updating over time the ergonomic risk score based on the responses of the user in said questionnaire. Based on the updated ergonomic risk, the system will continue to evaluate over time the actions to be performed to reduce and control the ergonomic risk of the user.

[0054] In one embodiment the term “system” is used generically herein to describe any number of components, elements, sub-systems, devices, packet switch elements, packet switches, routers, networks, computer, database and / or communication devices or mechanisms, or combinations of components thereof.

[0055] In one embodiment the questionnaire is presented to the user by e-mail in the form of direct mail or a mail magazine. In another embodiment, the questionnaire is presented to the user within an interface of the system. In another embodiment, the questionnaire is presented to the user via a web interface. In another embodiment one or more web pages are configured to present the questionnaire to the user and receive the responses to the questionnaire from the user.

[0056] FIG. 1 is a flow chart illustrating the ergonomic risk evaluation process, in accordance with some embodiments.

[0057] At step 104, based on the user's responses to a series of questions in a questionnaire the ergonomic risk score of the said user is calculated. In this context, said questionnaire can be presented to the user through a web interface and is formed by a plurality of successive questions related to the ergonomic habitat of the user, including multiple-choice questions. The at least one question may have a single select or multi-select answer option(s) wherein each selectable option is associated with a specific numerical value and said numerical value is associated with the risk level of that particular response.

[0058] After the user has completed the questionnaire by answering the multiple-choice questions, the system computes the risk score of the said user based on the cumulative sum of the numerical value assigned to each answer in said questionnaire. In one specific embodiment, the higher the value of a specific selection, the greater the ergonomic risk associated with that selection.

[0059] Subsequently, the system performs an action based on the calculated ergonomic risk score, said action includes, but is not limited to: for a low ergonomic risk level the system performs an "await review" action 106, for a medium-low ergonomic risk level the system performs a "closely monitor" action 108, for a medium ergonomic risk level the system performs an "offer an ergonomist evaluation to the user” action 110, for a high ergonomic risk level the system perform an “urgent ergonomic evaluation” action 112.

[0060] In the step “closely monitor” 108, the system offers the user the option to repeat the selfevaluation process 114. After this step, the system starts the evaluation process from step 100.

[0061] In the “offer an ergonomic evaluation” step 110 and in the “urgent ergonomic evaluation” step 112, the system offers the user the option of performing an ergonomic evaluation with an ergonomist and after this ergonomist assessment, the risk score is updated 118. In one embodiment, the risk score is updated based on the data derived from the assessment of the ergonomist.

[0062] The await review step 106 involves sending a follow-up email with a questionnaire 120 to the user, when it is determined that the user has completed said questionnaire 122 the system updates the user's ergonomic risk score 125 based on the data collected in said questionnaire, and based on the updated risk score the system performs an action that includes, but is not limited to a: "no further action” 130 for a low ergonomic risk level; “repeat the self-evaluation” 132 for amedium level of risk and “urgent action required” 134 for a high level of risk. In one embodiment, the system updates the overall risk score of the user based on the cumulative sum of the numerical value assigned to each answer in the questionnaire. In this context, after the “urgent action required” step 134, the system offers the user the ability to perform an ergonomic evaluation with an ergonomist 126 and after this step, the risk score is updated 118; after the “repeat self-evaluation step” 132, the system starts the evaluation process again from step 100.

[0063] In one embodiment, if after sending the follow-up e-mail with a questionnaire 120 the user does not respond, the system will send another reminder email two weeks later to the user 128, if there is no response the system will not take further action 136.

[0064] In one embodiment, if the updated risk score of the user 125 corresponds to a low level of ergonomic risk 130, the system will not take further action.

[0065] FIG. 2 is an illustration of a user 200 occupying a workstation, in accordance with some embodiments.

[0066] The user 200 after completing a questionnaire presented via a web interface, receives a series of recommendations from a system to improve ergonomics, these recommendations may include, but not limited to: advice on a height and an inclination of a work chair; advice on a height and a position of a monitor 202; suggestions on how to the input devices on the user's desk, such as keyboard and mouse. In an embodiment, the system presents via a web interface a series of recommendations to the user based on the information acquired in the questionnaire, with the aim of improving the ergonomics of said user in the workplace. After, the user inputs the data in the system, through the web interface, said data are relating to the suggestions that said user have been put into practice, and the risk score will be updated based on the number and quality of suggestions the user has applied. In a specific embodiment, the greater the number and quality of ergonomics advice that the user has put into practice, the lower the risk score assigned to the user by the system.

[0067] FIG. 3 is an illustration of a schematic representation of an ergonomic scoring system, in accordance with some embodiments.

[0068] The risk scoring system 310 processes internal data stored in a memory and external data from the user input, said risk scoring system 310 can communicate with a variety of devices anddisplay to the user, preferably via a web interface, one or more web elements including, but not limited to: an interactive questionnaire, suggestions for improving ergonomics of the user in the workplace, e-learning modules, a dashboard. Said devices may include, but are not limited to: tablet 300, computer 302, and smartphone 304. Communication between the risk scoring system and the devices can take place in any way known in the prior art, but preferably via the internet, including the use of cloud technologies 306.

[0069] In one embodiment, after the ergonomic risk assessment, the system provides the user with a set of recommendations, for example, via a web interface or e-mail. Said recommendations are based on the data collected in the questionnaire presented to the user and can include, but are not limited to, advice on how to use the keyboard and mouse, recommended desk height, advice on the ergonomic correct use of primary and secondary monitors. Subsequently, the system will monitor the user's progress through the presentation of new questionnaires and / or an ergonomist evaluation and / or the number and type of e-learning modules completed, and after this step, the system compute and update the user's risk score. In one embodiment, as the user implements one or more of the provided recommendations, the system correspondingly reduces their assessed ergonomic risk level.

[0070] In various implementations, the risk score assessment steps disclosed in this invention may be presented through a website, app on a mobile device or other computing device, or any other interface or application known in the prior art.

[0071] In one embodiment, the system presents to the user via a web interface a dashboard designed to offer an overview of key metrics and historical data from ergonomic assessments. In one embodiment, said dashboard comprises an array of informative elements, such as the duration of each evaluation, average completion time of the questionnaire and / or ergonomic risk assessment, the number of ergonomic assessment and / or the questionnaire completed, the number of evaluations not completed, and the number of evaluations not started. In one embodiment, said dashboard comprised a plurality of interactive buttons for performing actions such as exporting evaluation data into a PDF format directly from the dashboard, downloading data in an Excel format, generating detailed reports, filtering data by specific geographical regions, and tailor the display settings to align with personal preferences or specific periods ofinterest. In a specific embodiment, the dashboard interface is accessible by the administrator to monitor the progress of one or more workers.

[0072] FIG. 4 is an illustration of an online platform 400 consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform 400 may be hosted on a centralized server 402, such as, for example, a cloud computing service. The centralized server 402 may communicate with other network entities, such as, for example, a mobile device 406 (such as a smartphone, a laptop, a tablet computer etc.), other electronic devices 410 (such as desktop computers, server computers etc.), databases 414, and sensors 416 over a communication network 404, such as, but not limited to, the Internet. Further, users of the online platform 400 may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

[0073] A user 412, such as the one or more relevant parties, may access online platform 400 through a web based software application or browser. The web based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 500.

[0074] With reference to FIG. 5, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device 500. In a basic configuration, computing device 500 may include at least one processing unit 502 and a system memory 504. Depending on the configuration and type of computing device, system memory 504 may comprise, but is not limited to, volatile (e.g. random-access memory (RAM)), nonvolatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 504 may include operating system 505, one or more programming modules 506, and may include a program data 507. Operating system 505, for example, may be suitable for controlling computing device 500’ s operation. In one embodiment, programming modules 506 may include image -processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 5 by those components within a dashed line 508.

[0075] Computing device 500 may have additional features or functionality. For example, computing device 500 may also include additional data storage devices (removable and / or nonremovable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 5 by a removable storage 509 and a non-removable storage 510. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory 504, removable storage 509, and non-removable storage 510 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 500. Any such computer storage media may be part of device 500. Computing device 500 may also have input device(s) 512 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s) 514 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

[0076] Computing device 500 may also contain a communication connection 516 that may allow device 500 to communicate with other computing devices 518, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 516 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

[0077] As stated above, a number of program modules and data files may be stored in system memory 504, including operating system 505. While executing on processing unit 502, programming modules 506 (e.g., application 520 such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit 202 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.

[0078] Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, general purpose graphics processor-based systems, multiprocessor systems, microprocessor-based or programmable consumer electronics, application specific integrated circuit-based electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0079] Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems.

[0080] Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storagemedia readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and / or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0081] The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0082] Embodiments of the present disclosure, for example, are described above with reference to block diagrams and / or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions / acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality / acts involved.

[0083] While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, solid state storage (e.g., USB drive), or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods’ stages may be modified in any manner, including by reordering stages and / or inserting or deleting stages, without departing from the disclosure.

[0084] Fig. 6 illustrates a flowchart of a method 600 of provisioning an intervention based on an ergonomic risk assessment associated with a user, in accordance with some embodiments.

[0085] Accordingly, the method 600 may include a step 602 of receiving, using a communication device 802, an ergonomic risk assessment data from a user device associated with the user. Further, the ergonomic risk assessment data corresponds to an evaluation of an ergonomic risk associated with the user. Further, the method 600 may include a step 604 of determining, using a processing device 804, an ergonomic risk score data based on the ergonomic risk assessment data. Further, the ergonomic risk score data corresponds to a score associated with an ergonomic risk. Further, the method 600 may include a step 606 of analyzing, using the processing device 804, the ergonomic risk score data. Further, the method 600 may include a step 608 of generating, using the processing device 804, an intervention data based on the analyzing. Further, the intervention data corresponds to the intervention based on the ergonomic risk. Further, the method 600 may include a step 610 of transmitting, using the communication device 802, the intervention data to the user device.

[0086] In some embodiments, the ergonomic risk assessment data includes a user questionnaire response data corresponding to a response based on a questionnaire provisioned to the user.

[0087] In some embodiments, the user questionnaire response data includes two or more question response data corresponding to the response associated with two or more questions. Further, each of the two or more questions includes an answer option corresponding to a possible answer based on each of the two or more questions. Further, the answer option includes two or more answer options. Further, each of the two or more answer options may be associated with a score corresponding to a numerical value based on an extend of the ergonomic risk. Further, thegenerating of the ergonomic risk assessment data may be further based on an addition of the score associated with each of the two or more answer options selected by the user based on the two or more questions.

[0088] In some embodiments, analyzing of the ergonomic score data further includes detecting a risk level corresponding to a level of risk associated with user. Further, the generating of the intervention data may be further based on the risk level associated with the ergonomic score data. Further, the risk level includes two or more risk levels. Further, the two or more risk levels includes one or more of a low risk level, a medium risk level and a high risk level. Further, the low risk level corresponds to a lower risk associated with the user. Further, the medium risk level corresponds to a medium risk associated with the user. Further, the high risk level corresponds to a higher risk associated with the user.

[0089] In some embodiments, the intervention data includes an await review data corresponding to an instruction to wait for a review based on the ergonomic risk associated with the user. Further, the generating of the await review data may be based on the lower risk level. Further, the await review data includes a follow up email data corresponding to an updated questionnaire based on the ergonomic risk. Further, the follow up email data may be configured to be presented on a user presentation device associated with the user device.

[0090] In some embodiments, the user device includes a user input device which may be configured for receiving a modified ergonomic risk assessment data corresponding to a modification of the ergonomic risk assessment data. Further, the user device further includes a user-communication device which may be configured for transmitting the modified ergonomic risk assessment data.

[0091] In some embodiments, the method 600 may further include receiving, using the communication device 802, the modified ergonomic risk assessment data from the user device.

[0092] Fig. 7 illustrates a flowchart of a method 700 of provisioning an intervention based on an ergonomic risk assessment associated with a user including detecting, using the processing device 804, an updated risk level associated with the modified ergonomic risk score data, in accordance with some embodiments.

[0093] Further, in some embodiments, the method 700 further may include a step 702 of determining, using the processing device 804, a modified ergonomic risk score data based on the modified ergonomic risk assessment data. Further, in some embodiments, the method 700 further may include a step 704 of generating, using the processing device 804, a modified intervention data may be based on analyzing of the modified ergonomic risk score data. Further, the intervention data includes the modified intervention data. Further, in some embodiments, the method 700 further may include a step 706 of detecting, using the processing device 804, an updated risk level associated with the modified ergonomic risk score data. Further, the updated risk level includes one or more of the low risk level, the medium risk level and the high risk level.

[0094] In some embodiments, the modified intervention data includes one or more of a no further action data, a newly modified ergonomic risk assessment data and an urgent action requirement data. Further, the generating of the no further action data may be based on the low risk level associated with the modified ergonomic score data. Further, the no further action data corresponds to the requirement of no further action on the ergonomic risk. Further, the generating of the newly modified ergonomic risk assessment data may be based on the medium risk level associated with the modified ergonomic score data. Further, the newly modified ergonomic risk assessment data corresponds to a newly modified questionnaire for the user associated with the ergonomic risk assessment. Further, the generating of the urgent action requirement data may be based on the higher risk level associated with the modified ergonomic score data. Further, the urgent action requirement data corresponds to an urgent intervention based on the ergonomic risk associated with the user.

[0095] In some embodiments, the intervention data includes one or more of a closely monitor data, an ergonomist evaluation offer data and an urgent ergonomist evaluation data. Further, the closely monitor data corresponding to an instruction for provisioning an updated ergonomic assessment to the user. Further, the generating of the closely monitor data may be based on the medium risk level associated with the ergonomic score data. Further, the ergonomist evaluation offer data corresponding to an optional instruction for an ergonomist evaluation based on the ergonomic risk. Further, generating of the ergonomic risk data may be further based on the ergonomist evaluation offer data. Further, the generating of the ergonomist evaluation offer data may be based on the risk level in the range of the medium risk level and the high risk level.Further, the urgent ergonomist evaluation data corresponding to a mandatory instruction for an ergonomist evaluation based on the ergonomic risk. Further, generating of the ergonomic risk data may be further based on the urgent ergonomist evaluation data. Further, the generating of the urgent ergonomist evaluation data may be based on the high risk level.

[0096] Fig. 8 illustrates a block diagram of a system 800 of provisioning an intervention based on an ergonomic risk assessment associated with a user, in accordance with some embodiments.

[0097] Accordingly, the system 800 may include a communication device 802. Further, the communication device 802 may be configured for receiving an ergonomic risk assessment data from a user device associated with the user. Further, the ergonomic risk assessment data corresponds to an evaluation of an ergonomic risk associated with the user. Further, the communication device 802 may be configured for transmitting an intervention data to the user device. Further, the system 800 may include a processing device 804. Further, the processing device 804 may be configured for determining an ergonomic risk score data based on the ergonomic risk assessment data. Further, the ergonomic risk score data corresponds to a score associated with an ergonomic risk. Further, the processing device 804 may be configured for analyzing the ergonomic risk score data. Further, the processing device 804 may be configured for generating the intervention data based on the analyzing. Further, the intervention data corresponds to the intervention based on the ergonomic risk.

[0098] In some embodiments, the ergonomic risk assessment data includes a user questionnaire response data corresponding to a response based on a questionnaire provisioned to the user.

[0099] In some embodiments, the user questionnaire response data includes two or more question response data corresponding to the response associated with two or more questions. Further, each of the two or more questions includes an answer option corresponding to a possible answer based on each of the two or more questions. Further, the answer option includes two or more answer options. Further, each of the two or more answer options may be associated with a score corresponding to a numerical value based on an extend of the ergonomic risk. Further, the generating of the ergonomic risk assessment data may be further based on an addition of the score associated with each of the two or more answer options selected by the user based on the two or more questions.

[0100] In some embodiments, analyzing of the ergonomic score data further includes detecting a risk level corresponding to a level of risk associated with user. Further, the generating of the intervention data may be further based on the risk level associated with the ergonomic score data. Further, the risk level includes two or more risk levels. Further, the two or more risk levels includes one or more of a low risk level, a medium risk level and a high risk level. Further, the low risk level corresponds to a lower risk associated with the user. Further, the medium risk level corresponds to a medium risk associated with the user. Further, the high risk level corresponds to a higher risk associated with the user.

[0101] In some embodiments, the intervention data includes an await review data corresponding to an instruction to wait for a review based on the ergonomic risk associated with the user. Further, the generating of the await review data may be based on the lower risk level. Further, the await review data includes a follow up email data corresponding to an updated questionnaire based on the ergonomic risk. Further, the follow up email data may be configured to be presented on a user presentation device associated with the user device.

[0102] In some embodiments, the user device includes a user input device which may be configured for receiving a modified ergonomic risk assessment data corresponding to a modification of the ergonomic risk assessment data. Further, the user device further includes a user-communication device which may be configured for transmitting the modified ergonomic risk assessment data.

[0103] In some embodiments, the communication device 802 may be further configured for receiving the modified ergonomic risk assessment data from the user device.

[0104] Further, in some embodiments, the processing device 804 may be further configured for determining a modified ergonomic risk score data based on the modified ergonomic risk assessment data. Further, the processing device 804 may be further configured for generating a modified intervention data may be based on analyzing of the modified ergonomic risk score data. Further, the intervention data includes the modified intervention data. Further, the processing device 804 may be further configured for detecting an updated risk level associated with the modified ergonomic risk score data. Further, the updated risk level includes one or more of the low risk level, the medium risk level and the high risk level.

[0105] In some embodiments, the modified intervention data includes one or more of a no further action data, a newly modified ergonomic risk assessment data and an urgent action requirement data. Further, the generating of the no further action data may be based on the low risk level associated with the modified ergonomic score data. Further, the no further action data corresponds to the requirement of no further action on the ergonomic risk. Further, the generating of the newly modified ergonomic risk assessment data may be based on the medium risk level associated with the modified ergonomic score data. Further, the newly modified ergonomic risk assessment data corresponds to a newly modified questionnaire for the user associated with the ergonomic risk assessment. Further, the generating of the urgent action requirement data may be based on the higher risk level associated with the modified ergonomic score data. Further, the urgent action requirement data corresponds to an urgent intervention based on the ergonomic risk associated with the user.

[0106] In some embodiments, the intervention data includes one or more of a closely monitor data, an ergonomist evaluation offer data and an urgent ergonomist evaluation data. Further, the closely monitor data corresponding to an instruction for provisioning an updated ergonomic assessment to the user. Further, the generating of the closely monitor data may be based on the medium risk level associated with the ergonomic score data. Further, the ergonomist evaluation offer data corresponding to an optional instruction for an ergonomist evaluation based on the ergonomic risk. Further, generating of the ergonomic risk data may be further based on the ergonomist evaluation offer data. Further, the generating of the ergonomist evaluation offer data may be based on the risk level in the range of the medium risk level and the high risk level. Further, the urgent ergonomist evaluation data corresponding to a mandatory instruction for an ergonomist evaluation based on the ergonomic risk. Further, generating of the ergonomic risk data may be further based on the urgent ergonomist evaluation data. Further, the generating of the urgent ergonomist evaluation data may be based on the high risk level.

[0107] In some embodiments, the ergonomic risk associated with the user may be based on a user environment.

[0108] In some embodiments, the user environment includes an office environment corresponding to an office associated with the user.

[0109] In some embodiments, the user environment includes a vehicular environment corresponding to a vehicle associated with the user.

[0110] In some embodiments, the user environment includes an institutional environment corresponding to an educational institute associated with the user.

[0111] In some embodiments, the user includes one or more of a workplace occupant, a worker, a person, and an employee associated with the user environment.

[0112] In some embodiments, the ergonomic risk assessment data includes a user data corresponding to an additional information associated with the user.

[0113] In some embodiments, the user data includes one or more of an age data, a gender data, a fitness data, a body weight data, and a pre-existing health condition data. Further, the age data corresponds to an age of the user. Further, the gender data corresponds to a gender of the user. Further, the fitness data represents a fitness of the user. Further, the body weight data represents a body weight of the user. Further, the pre-existing health condition data corresponds to a preexisting health condition of the user.

[0114] In some embodiments, the score associated with the ergonomic risk may be constrained within a score range corresponding to a numerical range associated with the ergonomic risk.

[0115] In some embodiments, the score range includes one or more of a one to hundred score range and a one to five score range.

[0116] In some embodiments, the one to hundred score range includes a one to nineteen score range corresponding to a low ergonomic risk.

[0117] In some embodiments, the one to hundred score range includes a twenty to thirty-nine score range corresponding to the range of the low ergonomic risk to a medium ergonomic risk.

[0118] In some embodiments, the one to hundred score range includes a forty to fifty-nine score range corresponding to the medium ergonomic risk.

[0119] In some embodiments, the one to hundred score range includes a sixty to seventy-nine score range corresponding to the range of the medium ergonomic risk to a high ergonomic risk.

[0120] In some embodiments, the one to hundred score range includes an eighty to hundred score range corresponding to the high ergonomic risk.

[0121] In some embodiments, the one to five score range includes a one to two score range corresponding to a low ergonomic risk.

[0122] In some embodiments, the one to five score range includes a three score corresponding to a medium ergonomic risk.

[0123] In some embodiments, the one to five score range includes a four to five score range corresponding to a high ergonomic risk.

[0124] In some embodiments, the ergonomic risk assessment data includes a user questionnaire response data corresponding to a response based on a questionnaire provisioned to the user. Further, the ergonomic risks associated with the user may be based on a user environment. Further, the questionnaire may be associated with a characteristic associated with the user environment.

[0125] In some embodiments, the characteristic includes a user posture corresponding to a posture of the user in the user environment.

[0126] In some embodiments, the characteristic includes a physical exertion level corresponding to a level of physical exertion perceived by the user.

[0127] In some embodiments, the characteristic includes a repetitive motion frequency corresponding to a frequency of a repetitive motion performed by the user in the user environment.

[0128] In some embodiments, the intervention data includes an ergonomics educational module data corresponding to an educational module which may be configured for mitigating the ergonomic risks associated with the user.

[0129] In some embodiments, the intervention data may be configured to be presented on a user presentation device associated with the user device.

[0130] In some embodiments, provisioning of the questionnaire to the user may be based on a web interface.

[0131] In some embodiments, the web interface includes two or more web pages. Further, each of the two or more web pages may be configured to present the questionnaire to the user.

[0132] In some embodiments, the intervention data includes a recommendation data corresponding to a recommendation associated with mitigating the ergonomic risk.

[0133] In some embodiments, the ergonomic risk score data includes an ergonomic health rating data corresponding to an ergonomic health rating associated with a user health in relation to the user.

[0134] In some embodiments, the ergonomic health rating associated with the user may be constrained within an ergonomic health rating scale corresponding to a quantification of the ergonomic risk associated with the user. Further, the ergonomic health rating scale is constrained within one or more of a poor health rating and an excellent health rating. Further, the poor health rating corresponds to a poor user health. Further, the excellent health rating corresponds to an excellent user health.

[0135] Although the invention has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the invention as hereinafter claimed.

Claims

CLAIMS1. A method of provisioning an intervention based on an ergonomic risk assessment associated with a user, wherein the method comprising: receiving, using a communication device, an ergonomic risk assessment data from a user device associated with the user, wherein the ergonomic risk assessment data corresponds to an evaluation of an ergonomic risk associated with the user; determining, using a processing device, an ergonomic risk score data based on the ergonomic risk assessment data, wherein the ergonomic risk score data corresponds to a score associated with an ergonomic risk; analyzing, using the processing device, the ergonomic risk score data; generating, using the processing device, an intervention data based on the analyzing, wherein the intervention data corresponds to the intervention based on the ergonomic risk; and transmitting, using the communication device, the intervention data to the user device.

2. The method of claim 1, wherein the ergonomic risk assessment data comprises a user questionnaire response data corresponding to a response based on a questionnaire provisioned to the user.

3. The method of claim 2, wherein the user questionnaire response data comprises a plurality of question response data corresponding to the response associated with a plurality of questions, wherein each of the plurality of questions comprises an answer option corresponding to a possible answer based on each of the plurality of questions, wherein the answer option comprises a plurality of answer options, wherein each of the plurality of answer options is associated with a score corresponding to a numerical value based on an extend of the ergonomic risk, wherein the generating of the ergonomic risk assessment data is further based on an addition of the score associated with each of the plurality of answer options selected by the user based on the plurality of questions.

4. The method of claim 1, wherein the analyzing of the ergonomic score data comprises detecting a risk level corresponding to a level of risk associated with the user, wherein the generating of the intervention data is further based on the risk level associated with the ergonomic score data, wherein the risk level comprises a plurality of risk levels, wherein theplurality of risk levels comprises at least one of a low risk level, a medium risk level, and a high risk level, wherein the low risk level corresponds to a lower risk associated with the user, wherein the medium risk level corresponds to a medium risk associated with the user, wherein the high risk level corresponds to a higher risk associated with the user.

5. The method of claim 4, wherein the intervention data comprises an await review data corresponding to an instruction to wait for a review based on the ergonomic risk associated with the user, wherein the generating of the await review data is based on the lower risk level, wherein the await review data comprises a follow up email data corresponding to an updated questionnaire based on the ergonomic risk, wherein the follow up email data is configured to be presented on a user presentation device associated with the user device.

6. The method of claim 5, wherein the user device comprises a user input device configured for receiving a modified ergonomic risk assessment data corresponding to a modification of the ergonomic risk assessment data, wherein the user device further comprises a usercommunication device configured for transmitting the modified ergonomic risk assessment data.

7. The method of claim 6 further comprising receiving, using the communication device, the modified ergonomic risk assessment data from the user device.

8. The method of claim 7 further comprising: determining, using the processing device, a modified ergonomic risk score data based on the modified ergonomic risk assessment data; generating, using the processing device, a modified intervention data based on analyzing of the modified ergonomic risk score data, wherein the intervention data comprises the modified intervention data; and detecting, using the processing device, an updated risk level associated with the modified ergonomic risk score data, wherein the updated risk level comprises at least one of the low risk level, the medium risk level, and the high risk level.

9. The method of claim 8, wherein the modified intervention data comprises at least one of a no further action data, a newly modified ergonomic risk assessment data and an urgent action requirement data, wherein the generating of the no further action data is based on the low risk level associated with the modified ergonomic score data, wherein the no further action datacorresponds to the requirement of no further action on the ergonomic risk, wherein the generating of the newly modified ergonomic risk assessment data is based on the medium risk level associated with the modified ergonomic score data, wherein the newly modified ergonomic risk assessment data corresponds to a newly modified questionnaire for the user associated with the ergonomic risk assessment, wherein the generating of the urgent action requirement data is based on the higher risk level associated with the modified ergonomic score data, wherein the urgent action requirement data corresponds to an urgent intervention based on the ergonomic risk associated with the user.

10. The method of claim 4, wherein the intervention data comprises at least one of a closely monitor data, an ergonomist evaluation offer data, and an urgent ergonomist evaluation data, wherein the closely monitor data corresponding to an instruction for provisioning an updated ergonomic assessment to the user, wherein the generating of the closely monitor data is based on the medium risk level associated with the ergonomic score data, wherein the ergonomist evaluation offer data corresponding to an optional instruction for an ergonomist evaluation based on the ergonomic risk, wherein generating of the ergonomic risk data is further based on the ergonomist evaluation offer data, wherein the generating of the ergonomist evaluation offer data is based on the risk level in the range of the medium risk level and the high risk level, wherein the urgent ergonomist evaluation data corresponding to a mandatory instruction for an ergonomist evaluation based on the ergonomic risk, wherein generating of the ergonomic risk data is further based on the urgent ergonomist evaluation data, wherein the generating of the urgent ergonomist evaluation data is based on the high risk level.

11. A system for provisioning an intervention based on an ergonomic risk assessment associated with a user, wherein the system comprising: a communication device configured for: receiving an ergonomic risk assessment data from a user device associated with the user, wherein the ergonomic risk assessment data corresponds to an evaluation of an ergonomic risk associated with the user; and transmitting an intervention data to the user device; anda processing device communicatively coupled with the communication device, wherein the processing device is configured for: determining an ergonomic risk score data based on the ergonomic risk assessment data, wherein the ergonomic risk score data corresponds to a score associated with an ergonomic risk; analyzing the ergonomic risk score data; and generating the intervention data based on the analyzing, wherein the intervention data corresponds to the intervention based on the ergonomic risk.

12. The system of claim 11, wherein the ergonomic risk assessment data comprises a user questionnaire response data corresponding to a response based on a questionnaire provisioned to the user.

13. The system of claim 12, wherein the user questionnaire response data comprises a plurality of question response data corresponding to the response associated with a plurality of questions, wherein each of the plurality of questions comprises an answer option corresponding to a possible answer based on each of the plurality of questions, wherein the answer option comprises a plurality of answer options, wherein each of the plurality of answer options is associated with a score corresponding to a numerical value based on an extend of the ergonomic risk, wherein the generating of the ergonomic risk assessment data is further based on an addition of the score associated with each of the plurality of answer options selected by the user based on the plurality of questions.

14. The system of claim 11, wherein the analyzing of the ergonomic score data comprises detecting a risk level corresponding to a level of risk associated with user, wherein the generating of the intervention data is further based on the risk level associated with the ergonomic score data, wherein the risk level comprises a plurality of risk levels, wherein the plurality of risk levels comprises at least one of a low risk level, a medium risk level and a high risk level, wherein the low risk level corresponds to a lower risk associated with the user, wherein the medium risk level corresponds to a medium risk associated with the user, wherein the high risk level corresponds to a higher risk associated with the user.

15. The system of claim 14, wherein the intervention data comprises an await review data corresponding to an instruction to wait for a review based on the ergonomic risk associated with the user, wherein the generating of the await review data is based on the lower risk level, wherein the await review data comprises a follow up email data corresponding to an updated questionnaire based on the ergonomic risk, wherein the follow up email data is configured to be presented on a user presentation device associated with the user device.

16. The system of claim 15, wherein the user device comprises a user input device configured for receiving a modified ergonomic risk assessment data corresponding to a modification of the ergonomic risk assessment data, wherein the user device further comprises a usercommunication device configured for transmitting the modified ergonomic risk assessment data.

17. The system of claim 16, wherein the communication device is further configured for receiving the modified ergonomic risk assessment data from the user device.

18. The system of claim 17, wherein the processing device is further configured for: determining a modified ergonomic risk score data based on the modified ergonomic risk assessment data; generating a modified intervention data based on analyzing of the modified ergonomic risk score data, wherein the intervention data comprises the modified intervention data; and detecting an updated risk level associated with the modified ergonomic risk score data, wherein the updated risk level comprises at least one of the low risk level, the medium risk level, and the high risk level.

19. The system of claim 18, wherein the modified intervention data comprises at least one of a no further action data, a newly modified ergonomic risk assessment data and an urgent action requirement data, wherein the generating of the no further action data is based on the low risk level associated with the modified ergonomic score data, wherein the no further action data corresponds to the requirement of no further action on the ergonomic risk, wherein the generating of the newly modified ergonomic risk assessment data is based on the medium risk level associated with the modified ergonomic score data, wherein the newly modified ergonomic risk assessment data corresponds to a newly modified questionnaire for the user associated with the ergonomic risk assessment, wherein the generating of the urgent action requirement data isbased on the higher risk level associated with the modified ergonomic score data, wherein the urgent action requirement data corresponds to an urgent intervention based on the ergonomic risk associated with the user.

20. The system of claim 14, wherein the intervention data comprises at least one of a closely monitor data, an ergonomist evaluation offer data and an urgent ergonomist evaluation data, wherein the closely monitor data corresponding to an instruction for provisioning an updated ergonomic assessment to the user, wherein the generating of the closely monitor data is based on the medium risk level associated with the ergonomic score data, wherein the ergonomist evaluation offer data corresponding to an optional instruction for an ergonomist evaluation based on the ergonomic risk, wherein generating of the ergonomic risk data is further based on the ergonomist evaluation offer data, wherein the generating of the ergonomist evaluation offer data is based on the risk level in the range of the medium risk level and the high risk level, wherein the urgent ergonomist evaluation data corresponding to a mandatory instruction for an ergonomist evaluation based on the ergonomic risk, wherein generating of the ergonomic risk data is further based on the urgent ergonomist evaluation data, wherein the generating of the urgent ergonomist evaluation data is based on the high risk level.

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