Computer implemented method for improving safety at a jobsite

Abstract

The present invention relates to a computer implemented method for improving safety at a jobsite. The method comprising: upon, a fieldworker has arrived at the jobsite, enabling the fieldworker to capture an audio-visual presentation of the jobsite using a video module of a mobile computing device associated with the fieldworker; obtaining site-specific context data; determining a site-specific hazard prediction comprising one or more site-specific hazards by inputting the captured audio-visual presentation of the jobsite and the obtained site-specific context data into a machine learning model being trained to output one or more site specific hazards based on video and / or audio data and on context data; displaying an indication of the one or more site-specific hazards of the site-specific hazard prediction at a display of the mobile computing device; monitoring whether there has been a change in jobsite conditions by obtaining new site-specific context data and inputting the new site-specific context data together with a latest captured audio-visual presentation of the jobsite into the machine learning model and determining whether one or more site-specific hazards outputted from the machine learning model are different as compared to a latest site-specific hazard prediction; upon there has been a change in jobsite conditions, prompting the fieldworker to capture a new audio-visual presentation of the jobsite using the video module of the mobile computing device; inputting the new site-specific context data and the new audio-visual presentation into the machine learning model, thereby generating a new site-specific hazard prediction comprising one or more site-specific hazards; and displaying an indication of the one or more site-specific hazards of the new site-specific hazard prediction at the display of the mobile computing device.

Description

[0001] COMPUTER IMPLEMENTED METHOD FOR IMPROVING SAFETY AT A JOBSITE

[0002] Technical field

[0003] The present invention relates to improving safety at a jobsite. Especially a computer implemented method for improving safety at a jobsite is presented. The computer implemented method being suitable to be executed by a risk assessment system comprising one or more mobile computing devices and a remote server.

[0004] Background

[0005] In general, industries such as, but not limited to, construction industries, energy and utilities industries, or the like, involve day-to-day work (also be referred to as field work, field operations, or the like) to be performed at a location / work site. The work at the location has to be performed in compliance with field work management requirements defined based on state, local, and federal laws, a type of work, a work site, and so on. The field work management requirements include multiple regulations, codes and standards with regard to health and safety of workers, environmental protection, and quality management.

[0006] Performing risk assessment before, during and after a job may be encouraged or required in these types of industries. Video risk assessment can be used to identify risks in an area such as a jobsite, where work needs to be done efficiently without accidents or inconveniences slowing the work down or even stopping the work. Any field worker on the site can use a video risk assessment, VRA, application on a mobile phone or other mobile device that includes a camera, capturing video of the area in which they intend to work for video analysis.

[0007] As the work progress at the jobsite conditions at the jobsite as well as sitespecific hazards may change. Hence, there is a need for the fieldworker, and potentially also a manager supervising the jobsite, to be updated about hazards at the jobsite.

[0008] Summary of the invention

[0009] In view of the above, it is an object of the present invention to provide improved safety at a jobsite. According to a first aspect computer implemented method for improving safety at a jobsite is provided. The method comprising: upon, a fieldworker has arrived at the jobsite, enabling the fieldworker to capture an audio-visual presentation of the jobsite using a video module of a mobile computing device associated with the fieldworker; obtaining site-specific context data; determining a site-specific hazard prediction comprising one or more site-specific hazards by inputting the captured audio-visual presentation of the jobsite and the obtained site-specific context data into a machine learning model being trained to output one or more site specific hazards based on video and / or audio data and on context data; displaying an indication of the one or more site-specific hazards of the site-specific hazard prediction at a display of the mobile computing device; monitoring whether there has been a change in jobsite conditions by obtaining new site-specific context data and inputting the new sitespecific context data together with a latest captured audio-visual presentation of the jobsite into the machine learning model determining whether one or more site-specific hazards outputted from the machine learning model are different as compared to a latest site-specific hazard prediction; upon there has been a change in jobsite conditions, prompting the fieldworker to capture a new audio-visual presentation of the jobsite using the video module of the mobile computing device; inputting the new sitespecific context data and the new audio-visual presentation into the machine learning model, thereby generating a new site-specific hazard prediction comprising one or more updated site-specific hazards; and displaying an indication of the one or more site-specific hazards of the new site-specific hazard prediction at the display of the mobile computing device.

[0010] As the work progress at a jobsite conditions at the jobsite as well as site-specific hazards may change. When it happens, a fieldworker needs to complete a new risk assessment to stay safe on site, which they often omit to do for one reason or another. And for remote manager it’s hardly possible without travelling to site to know if site conditions changed significantly enough for the new risk assessment to be performed and to prompt their team complete it. The here provided method allow for remote real time visibility into site conditions change to ensure timely risk-re-assessment. This will improve safety at the jobsite. An indication of a site-specific hazard may comprise details of the site-specific hazard. An indication of a site-specific hazard may comprise an associated set of control measures for the site-specific hazard.

[0011] Upon a plurality of site-specific hazards are outputted from the machine learning model, the method may comprise displaying an individual indication for each of the plurality of site-specific hazards at the display of the mobile computing device.

[0012] The method may comprise requesting for the fieldworker to acknowledge contents of the indication of the one or more site-specific hazards.

[0013] Obtaining the site-specific context data may comprise receiving context data from one or more internal and / or external application programming interfaces. Obtaining the new site-specific context data may comprise receiving context data from one or more internal and / or external application programming interfaces.

[0014] The site-specific context data may comprise one or more of: data on nearby points of interest, weather data, traffic data, roadworks data, geographic information system, GIS, data, and telematics data. The new site-specific context data may comprise one or more of: data on nearby points of interest, weather data, traffic data, roadworks data, geographic information system, GIS, data, and telematics data.

[0015] The site-specific context data may comprise historical personal behaviour data for the fieldworker. The new site-specific context data may comprise historical personal behaviour data for the fieldworker.

[0016] The method may comprise displaying an indication of the one or more sitespecific hazards at a display of a computing device associated with a manager supervising the jobsite.

[0017] According to a second aspect a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium having stored thereon instructions for implementing the method according to the first aspect, when executed on a device having processing capabilities

[0018] The above mentioned features of the method, when applicable, apply to this second aspect as well. In order to avoid undue repetition, reference is made to the above.

[0019] A further scope of applicability will become apparent from the detailed description given below. However, it should be understood that the detailed description and specific examples are given by way of illustration only. It is to be understood that the terminology used herein is for purpose of describing particular embodiments only, and is not intended to be limiting. It must be noted that, as used in the specification and the appended claim, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the elements unless the context clearly dictates otherwise. Thus, for example, reference to "a unit" or "the unit" may include several devices, and the like. Furthermore, the words "comprising", “including”, “containing” and similar wordings does not exclude other elements or steps.

[0020] Brief description of the drawings

[0021] The above and other aspects will now be described in more detail, with reference to appended figures. The figures should not be considered limiting; instead they are used for explaining and understanding.

[0022] As illustrated in the figures, the sizes of layers and regions may be exaggerated for illustrative purposes and, thus, are provided to illustrate the general structures. Like reference numerals refer to like elements throughout.

[0023] Fig. 1 illustrate a risk assessment system and a fieldworker interacting with a mobile computing device of the risk assessment system at a jobsite comprising a plurality of hazards.

[0024] Fig. 2 is a block diagram of a method for onsite video risk assessment.

[0025] Figs 3a and 3b illustrates an example of displaying the one or more site-specific hazards determined with the method of Fig. 2 to a fieldworker.

[0026] Fig. 4 is a block diagram of a method for post video risk assessment updating of hazards.

[0027] Figs 5a-5f illustrate screenshots of different implementations of different steps in the method of Fig. 4.

[0028] Detailed description

[0029] The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which currently preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms. In connection with Fig. 1 general concepts of risk assessment using a mobile computing device 102 associated with a fieldworker 100 will be discussed. The fieldworker 100 may capture an audio-visual presentation of a jobsite 120 using the mobile computing device 102. The audio-visual presentation comprising both video and audio data. The video data comprises visual information in a series of frames. The audio data comprise audio information. Risks may be assessed based on the video data, the audio data or both. A video to be used for video risk assessment, VRA, is typically the order of minutes in duration. Especially, the duration of the video for VRA shall be sufficiently long for the fieldworker 100 to indicate hazards that they can see and any other notable things that they can see or know about the jobsite.

[0030] Fig. 1 shows an example field worker 100, who will capture a video at a jobsite 120 in order for a video risk assessment to be performed. Figure 1 depicts an example mobile computing device 102 comprising a video module 104. The video module 104 comprises a video recorder configured to record video. In this example, a lens of the video recorder is shown on the back of the mobile computing device 102. Fig. 1 also shows the front of the mobile computing device 102 which in this example comprises a display 106. The mobile computing device 102 may additionally or alternatively include a lens of the video recorder on the front side - for example, to allow a field worker 100 to capture video including their face using a front-facing camera. Further, the mobile computing device 102 typically also comprise a microphone 110 configured to record audio. In order for the video module 104 to capture both video data and audio data it typically comprises both the video recorder (having either a back-facing camera, a front-facing camera or both) and the microphone 110. The mobile computing device 102 may further comprise a speaker 110a configured to play audio.

[0031] The mobile computing device 102 comprises a processor 108 and memory 112. The memory 112 comprises an operating system configured to operate the mobile computing device 102. The memory 112 may store one or more mobile applications. For example a video risk assessment, VRA, application.

[0032] The mobile computing device 102 may be a mobile phone, a tablet, a laptop or another suitable mobile computing device. A computing device that the field worker 100 cannot carry around at a jobsite 120 would not be suitable for the present purpose. A computing device moveable on wheels or the like may be used but may not be the most elegant solution to capturing video of a jobsite versus a handheld mobile computing device. The mobile computing device 102 may be a digital camera equipped with a processor 108 and memory 112 such that a mobile application can be run on the digital camera.

[0033] The video module 104, typically also including the microphone 110, and the speaker 110a may be operatively connected to the memory 112 and the processor 108. The mobile computing device 102 further comprise controlling circuitry 114 configured to control operations of the mobile computing device 102. The controlling circuitry 114 is further configured to execute functions to be performed by the mobile computing device 102. One or more such functions may be in the form of steps of a method presented in this disclosure. The mobile device 102 comprise a transmitter and receiver or transceiver 116 configured to send and receive digital signals. The display 106 may comprise or be provided additionally to an I / O module 106a, which may comprise a keyboard or touchscreen to allow a user to input information into the mobile computing device 102. The I / O module 106a may comprise the display 106 and the speaker 110a, for example, such that information can be presented to the user as an output of analysis. The information may take the form of text, graphical representations such as charts or diagrams, or audio - for example a spoken message in a relevant language to the user or sounds such as bells or alarms or the like. The microphone 110 may be part of the I / O and may allow to field worker 100 to control operations of the mobile computing device 102 using voice commands, for example.

[0034] In Fig. 1, the jobsite 120 is shown comprising three example hazards 122: cloudy weather (which may indicate that rain will fall at the jobsite or may reduce visibility), an area where entry is prohibited, a road and an electrical hazard, for example power lines. These are merely examples of hazards that could occur at a jobsite 120 that would affect how a job is carried out. The field worker 100 may capture an audio-visual presentation of the jobsite 120 including one or more hazards. For video risk assessment purposes, the field worker 100 may spend time showing a hazard in detail (from a safe distance or in a safe manner) or may narrate their audiovisual presentation to indicate that they have perceived the hazard - for example “I am showing a road that passes close to the jobsite. I have observed that the road is currently busy”.

[0035] The mobile computing device 102 is typically part of a risk assessment system 300 also comprising a remote server 302. The remote server 302 may be a single server device. Alternatively, the remote server 302 may be a cloud-based server. The remote server 302 comprises circuitry 306, a processor 308 and a memory 312. The circuitry 306 is configured to execute different functions to be executed by the remote server 302 . One or more of such functions may be in the form of steps of a method presented in this disclosure. Further, functions forming steps of a method presented in this disclosure may be executed partly by the controlling circuitry 114 of the mobile computing device 102 and partly by the circuitry 306 of the server 302. Results from a function executed by the controlling circuitry 114 of the mobile computing device 102 may be used by a function executed by the circuitry 306 of the server 302 and vice versa. For example, functions for providing an output to the fieldworker 100 is typically executed by the controlling circuitry 114 of the mobile computing device 102. The mobile computing device 102 is connectable to the remote server 302 using a communication network. The communication network is typically a wireless network such as a cellular network, or a Wi-Fi network.

[0036] Once arriving at the jobsite 120, onsite risk assessment is often required to be performed. One type of risk assessment is video risk assessment, VRA. In connection with Fig. 2 a method 200 for onsite VRA will be discussed. Fig. 2 is a flow chart illustrating the steps of the method 200. Below, the different steps is described in more detail. Even though illustrated in a specific order, the steps of the method 200 may be performed in any suitable order, in parallel, as well as multiple times. Some of the steps, or even all steps, of the method 200 may be executed by a mobile computing device, typically the mobile computing device 102 associated with the fieldworker 100. Some of the steps of the method 200 may be executed by the remote server 302 and / or a device 320 associated with the manager. Such a device 320 associated with the manager is then also communicably connected to the risk assessment system 300.

[0037] Upon an indication that the fieldworker 100 has arrived at the jobsite 120, the fieldworker 100 will be enabled to capture S202 an audio-visual presentation of the jobsite 120. This by using the video module 104 of the mobile computing device 102. As discussed above, the audio-visual presentation comprising both video data and audio data. The indication that the fieldworker 100 has arrived at the jobsite 120 may be given by comparing a location of the jobsite 120 with a location of the mobile computing device 102 associated with the fieldworker 100. If the location of the jobsite 120 corresponds with the location of the mobile computing device 102 it is considered that the fieldworker 100 is at the jobsite. In this context a location may be seen as a GPS location. Enabling S202 the fieldworker 100 to capture an audio-visual presentation of the jobsite 120 may comprise prompting the fieldworker 100 to actually perform the capturing of the audio-visual presentation. Such prompting may be made by alerting the fieldworker 100 to actually perform the capturing of the audio-visual presentation. During the capturing of the audio-visual presentation the fieldworker 100 may be instructed about situations and / or parts of the jobsite 120 to be contained in the audio-visual presentation. What situations and / or parts of the jobsite 120 to be contained in the audio-visual presentation may be selected based on historical hazards previously been determined to be present at the jobsite 120 or an a similar jobsite.

[0038] Next, site-specific context data is obtained S204. The site-specific context data may be obtained in response to an audio-visual presentation has been or is in the process of being captured. Hence, the context data may be obtained S204 upon the fieldworker 100 is capturing the audio-visual presentation of the jobsite 120. The context data may be received from one or more internal and / or external application programming interfaces. For example, historical data on jobs and user behaviour are considered internal as it is proprietary data of the client. Some examples of external application programming interfaces are a weather service, a traffic service, geographic information system, GIS, service, ongoing job service and telematics data service. Context data can be any type of data that might indicate a presence of a potential hazard at a specific site, e.g. the jobsite 120. The context data may comprises one or more of: data on nearby points of interest, for example bus stops, schools, hospitals, shopping centers, etc.; traffic data, for example intensity, speed, incidents, etc.; roadworks data, for example traffic restrictions, road closures, etc.; weather data, for example rain, snow, wind, ice, heat, storms, etc.;

[0039] GIS data, for example data related to underground assets, gas main location, ground condition, etc.; telematics data, for example historical data on how vehicles has been driven on the day or before.

[0040] The context data may alternatively or in addition comprise historical personal behaviour data for the fieldworker 100. Examples of such historical personal behaviour data for the fieldworker 100 are: experience level, recent near misses or incidents, number of hours worked during the day, number of hours worked in the last 7 days, quality of risk assessments submitted before, and genuine interactions with hazards and controls before. In more detail, some aspects of the historical personal behaviour data may update slowly, such as “experience”, but other aspects will update as the day goes, such as “number of hours worked” and might start making impact on risk assessment as the fieldworker is becoming more and more fatigued. Hence, the historical personal behaviour data may comprise both relatively static data, such as experience level, and dynamic data, such as number of hours worked during the day. Accordingly, in case of the historical personal behaviour data comprising dynamic data such dynamic data may also be used in the risk assessment.

[0041] Next, a site-specific hazard prediction comprising one or more site-specific hazards is determined S206. This by inputting the captured audio-visual presentation of the jobsite 120 and the obtained site-specific context data into a machine learning model. The machine learning model is trained to output hazards based on video data and / or audio data and on context data. The machine learning model is trained based on historical data of user interactions (confirming or removing) with hazards. The machine learning model is working with input in form of risk assessment video and audio in the form of an audio-visual presentation and / or in the form of context data. The output from the machine learning model is a list of hazards. The machine learning model may be executed on a mobile computing device, such as the mobile computing device 102 associated with the fieldworker 100. Alternatively, or in combination, the machine learning model may be executed on the remote server 302.

[0042] The fieldworker 100 can then review and confirm the hazards outputted from the machine learning model. This since an indication of the one or more hazards is displayed S208 at the display 106 of the mobile computing device 102. Additionally, an indication of the one or more hazards may as well be displayed at the device 320 associated with the manager supervising the jobsite 120. The indication of the one or more hazards comprises details of the respective hazard. Examples of such details are illustrated in Fig. 3. The indication of the one or more hazards may further comprise an associated set of control measures for each of the hazards. Such a set of control measures may comprise one or more control measures. Some examples of details of a hazard and an associated set of control measures are: • Trees and hedgerows. Ensure works has minimal effect on surrounding trees and hedges (refer to environmental handbook). No cutting roots larger than 25mm.

[0043] • Moderate Volume of Cars or Light Commercial Vehicles. Safeguard adequate safety zones around works. Consider Traffic Management as per New Roads and Street Works Act, NRSWA.

[0044] • Pedestrian and vehicles. Safeguard appropriate barriers / signage in place as per NRSWA. Consider additional walkway for pedestrians. Consider curb ramps. Maintain access to properties. Secure all tools and ladders / clear signage in place / secure barriers in place. Segregation and safety zones in place, clearly signed as per NRSWA.

[0045] • Restricted visibility by work vehicles or local environment. Consider traffic management arrangements as per NRSWA.

[0046] • High Winds. Appropriate weights for barriers / signs must be used. Beware of falling debris. Remove branded netting from Heras fencing.

[0047] • High Crime Area. Be aware of potential vandalism. Ensure vehicles kept locked when working. Keep all tools and equipment secure.

[0048] • Pin Bar in use. Accurate up to date utility drawings available. The horizontal searcher bar must only be used within the confines of an excavation when the gas pipe has been located. Use CAT and Genny in all modes and mark up P,G,A & R.

[0049] • Adverse Weather. Drink warm fluids. Keep Hydrated. Regular breaks. Sunscreen on exposed skin. Warm up prior to manual work. Wear layered clothing.

[0050] • Gas Escape or Gas in Atmosphere. Monitor as per procedures. No smoking. Remove sources of ignition. Wear working PAM.

[0051] The control measures may e.g. be accessed by the fieldworker 100 by interacting with the respective detailed hazard in the screen illustrated in Fig. 3.

[0052] In case there is more than one hazard outputted from the machine learning model, each such hazard may be displayed S208 as an individual indication. As discussed above, such displaying S208 is made at the display 106 of the mobile computing device 102 associated with the fieldworker 100. One or more of the individual indications of the hazards may as well be displayed to the manager of the jobsite 120 on the device 320 associated with the manager.

[0053] In connection with displaying S208 the indications of the one or more hazards a request for the fieldworker 100 to acknowledge the contents of the respective indication may be made. Such acknowledgement may be prompted for by using a virtual close / next button on the display 106.

[0054] The method 200 for onsite VRA is typically implemented as a computer program to be executed in the risk assessment system 300. Some or all of the steps of the method 200 may be executed at the mobile computing device 102 associated with the fieldworker 100. Some of the steps of the method 200 may be executed at the remote server 302. Some of the steps of the method 200 may be executed at the device 320 associated with the manager. The computer program for implementing the method 200 may be stored on a non-transitory computer-readable storage medium.

[0055] As the work progress at the jobsite 120 conditions at the jobsite 120 as well as hazards may change. Hence, there is a need for the fieldworker 100, and potentially also the manager supervising the jobsite 120, to be updated about new hazards and also about which hazards that may still remain. Possibly also which hazards that no longer are present may be presented to the fieldworker 100 and / or the manager supervising the jobsite 120.

[0056] In connection with Fig. 4 a method 400 for post VRA updating of hazards will be discussed. The method 400 for post VRA updating of hazards can be executed after the method 200 for onsite VRA discussed above in connection with Fig. 2. Fig. 4 is a flow chart illustrating the steps of the method 400. Below, the different steps is described in more detail. Even though illustrated in a specific order, the steps of the method 400 may be performed in any suitable order, in parallel, as well as multiple times. Some of the steps, or even all steps, of the method 400 may be executed by a mobile computing device, typically the mobile computing device 102 associated with the fieldworker 100. Some of the steps of the method 400 may be executed by the remote server 302 and / or the device 320 associated with the manager.

[0057] Monitoring S402 whether there has been a change in jobsite conditions. This is typically made by checking if there has been a change in hazards present at the jobsite. The monitoring S402 may be made periodically, e.g. once a hour. The monitoring S402 may be made by making a new call to the one or more internal and / or external application programming interfaces for new site-specific context data. The internal and / or external application programming interfaces are discussed in detail above, in order to avoid undue repletion reference is made to the above discussion.

[0058] Hence, in connection with the monitoring S402 new site-specific context data is requested from an external or internal data source, i.e. the one or more internal and / or external application programming interfaces. In connection with this a re-run of the sitespecific hazard prediction using the machine learning model is made. The input to the machine learning model being audio and video data from the latest audio-visual presentation and the new site-specific context data. Upon the resulting one or more site-specific hazards outputted from the machine learning model are different as compared to what was confirmed as hazards in the last risk assessment, an alert will be triggered for the fieldworker 100 and / or the manager supervising the jobsite 120 that site conditions changed and a new risk assessment is needed. Hence, the fieldworker 100 will be prompted S404 to capture a new audio-visual presentation of the jobsite 120 to be used for generating S406 a new site-specific hazard prediction. Such prompting S404 may be made by in-app notifications and / or mobile push notifications. Also the manager for the jobsite may be notified using in-app notification, a mobile push-notification and / or email. In connection with Figs 5a-5d an example of prompting S404 the fieldworker 100 to capture a new audio-visual presentation of the jobsite 120 and the actual capturing of the new audio-visual presentation of the jobsite 120 is illustrated. According to this example, high weather disruption has been detected triggering the prompting S404 of the fieldworker 100 to capture a new audio-visual presentation of the jobsite 120.

[0059] The new audio-visual presentation of the jobsite 120 captured by the fieldworker 100 will be used as input to the machine learning model for generating S406 the new site-specific hazard prediction. Also the new site-specific context data will be used as input to the machine learning model for generating S406 the new site-specific hazard prediction. Hence, generating S406 the new site-specific hazard prediction may be performed by jointly inputting both the new audio-visual presentation and the new sitespecific context data to the machine learning model.

[0060] The manager supervising the jobsite may also be notified about a change in jobsite conditions. Hence, the manager may, via the device 320 associated with the manager, control the prompting S404 of the fieldworker 100 to capture a new audio- visual presentation of the jobsite 120 to be used for generating S606 the new sitespecific hazard prediction.

[0061] Upon the new site-specific hazard prediction has been generated S406, an indication of one or more site-specific hazards of the new site-specific hazard prediction is displayed S408 at the display 106 of the mobile computing device 102.

[0062] Hence, the fieldworker 100 can review and confirm newly predicted site-specific hazards. This since an indication of the one or more site-specific hazards of the new site-specific hazard prediction is displayed S408 at the display 106 of the mobile computing device 102. An example of displaying S408 the one or more hazards of the new site-specific hazard prediction to the fieldworker 100 is illustrated in Figs 5e and 5f.

[0063] Additionally, an indication of the one or more newly predicted site-specific hazards of the new site-specific hazard prediction may as well be displayed at the device 320 associated with the manager supervising the jobsite 120.

[0064] As discussed above, the indication of the one or more newly predicted sitespecific hazards comprises details of the respective site-specific hazard. An example of an screen shot comprising details of the respective site-specific hazard is illustrated in connection with Fig. 5e. As also discussed above, the indication of the one or more site-specific hazards may further comprise an associated set of control measures for each of the site-specific hazards. Such a set of control measures may comprise one or more control measures. Reference is made to the above discussions for examples of details and control measures for potential site-specific hazards. An example of an screen shot comprising both details and control measures of the respective sitespecific hazard is illustrated in connection with Fig. 5f.

[0065] In case there is more than one newly predicted site-specific hazard outputted from the second machine learning model, each such site-specific hazard may be displayed S408 as an individual indication. As discussed above, such displaying S408 is made at the display 106 of the mobile computing device 102 associated with the fieldworker 100. One or more of the individual indications of the site-specific hazards may as well be displayed to the manager of the jobsite 120 on the device 320 associated with the manager.

[0066] In connection with displaying S408 the indications of the one or more sitespecific hazards a request for the fieldworker 100 to acknowledge the contents of the respective indication may be made. Such acknowledgement may be prompted for by using a virtual close / next button on the display 106.

[0067] The method 400 is typically implemented as a computer program to be executed in the risk assessment system 300. Some or all of the steps of the method 400 may be executed at the mobile computing device 102 associated with the fieldworker 100. Some of the steps of the method 400 may be executed at the remote server 302. Some of the steps of the method 400 may be executed at the device 320 associated with the manager. The computer program for implementing the method 400 may be stored on a non-transitory computer-readable storage medium. The person skilled in the art realizes that the present invention by no means is limited to what is explicitly described above. On the contrary, many modifications and variations are possible within the scope of the appended claims.

[0068] Additionally, variations can be understood and effected by the skilled person in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

Claims

CLAIMS1. A computer implemented method for improving safety at a jobsite (120), the method comprising: upon, a fieldworker (100) has arrived at the jobsite (120), enabling the fieldworker (100) to capture (S202) an audio-visual presentation of the jobsite (120) using a video module (104) of a mobile computing device (102) associated with the fieldworker (100); obtaining (S204) site-specific context data; determining (S206) a site-specific hazard prediction comprising one or more site-specific hazards by inputting the captured audio-visual presentation of the jobsite (120) and the obtained site-specific context data into a machine learning model being trained to output one or more site specific hazards based on video and / or audio data and on context data; displaying (S208) an indication of the one or more site-specific hazards of the site-specific hazard prediction at a display (106) of the mobile computing device (102); monitoring (S402) whether there has been a change in jobsite conditions by obtaining new site-specific context data and inputting the new site-specific context data together with a latest captured audio-visual presentation of the jobsite (120) into the machine learning model and determining whether one or more site-specific hazards outputted from the machine learning model are different as compared to a latest sitespecific hazard prediction; upon there has been a change in jobsite conditions, prompting (S404) the fieldworker (100) to capture a new audio-visual presentation of the jobsite (120) using the video module (104) of the mobile computing device (102); inputting the new site-specific context data and the new audio-visual presentation into the machine learning model, thereby generating (S406) a new sitespecific hazard prediction comprising one or more site-specific hazards; and displaying (S408) an indication of the one or more site-specific hazards of the new site-specific hazard prediction at the display (106) of the mobile computing device (102).

2. The method according to claim 1, wherein an indication of a site-specific hazard comprises details of the site-specific hazard.

3. The method according to claim 3, wherein an indication of a site-specific hazard further comprises an associated set of control measures for the site-specific hazard.

4. The method according to any one of claims 1-3, upon a plurality of site-specific hazards are outputted from the machine learning model, the method further comprises displaying an individual indication for each of the plurality of site-specific hazards at the display (106) of the mobile computing device (102).

5. The method according to any one of claims 1-4, further comprising requesting for the fieldworker (100) to acknowledge contents of the indication of the one or more sitespecific hazards.

6. The method according to any one of claims 1-5, wherein obtaining the site-specific context data and the new site-specific context data comprises receiving context data from one or more internal and / or external application programming interfaces.

7. The method according to any one of claims 1-6, wherein the site-specific context data and the new site-specific context data comprises one or more of: data on nearby points of interest, weather data, traffic data, roadworks data, geographic information system, GIS, data, and telematics data.

8. The method according to any one of claims 1-7, wherein the site-specific context data and the new site-specific context data comprises historical personal behaviour data for the fieldworker (100).

9. The method according to any one of claims 1-8, further comprising displaying an indication of the one or more site-specific hazards at a display (322) of a computing device (320) associated with a manager supervising the jobsite (120).

10. A non-transitory computer-readable storage medium having stored thereon instructions for implementing the method according to any one of claims 1-9, when executed on a device having processing capabilities.

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