Patent Publication Number: US-2022215496-A1

Title: Dynamic message management for personal protective equipment

Description:
TECHNICAL FIELD 
     The present disclosure relates to industrial personal protective and safety equipment, such as respirators, self-contained breathing apparatuses, welding helmets, earmuffs, eyewear. 
     BACKGROUND 
     Many work environments include hazards that may expose people working within a given environment to a safety event, such as hearing damage, eye damage, a fall, breathing contaminated air, or temperature related injuries (e.g., heat stroke, frostbite, etc.). In many work environments, workers may utilize personal protective equipment (PPE) to help mitigate the risk of a safety event. Communication between workers may increase the risk of a safety event, for example, by preventing the worker from focusing on a task. 
     SUMMARY 
     In general, the present disclosure describes techniques for managing messages presented to workers in a work environment while the workers are utilizing personal protective equipment (PPE). According to examples of this disclosure, a computing device automatically computes and performs a safety risk assessment and dynamically determines whether to output messages to a worker that is currently utilizing PPE within a given work environment. In examples, the computing device determines whether to output a message audibly, visually, audibly and visually, or neither audibly nor visually. In some examples, the computing device computes a current risk level for the worker based on a number of factors to determine whether to output the message to the worker. The risk level for the worker may, for example, be indicative of a likelihood of the worker experiencing a safety event if presented with the message. 
     In one example, when the risk level for the worker is low, the computing device may visually output the message by outputting a graphical user interface (GUI) that includes at least a portion of the message via a display device, such that the worker may visually consume the content of the message. As another example, when the risk level for the worker is high, the computing device may refrain from visually outputting the message, such that the user may not visually consume the content of the message at that time. In such examples, the computing device may output the message audibly or may refrain from outputting the message altogether at that time. In some instances, the computing device determines whether to visually output the message based on the urgency of the message. As such, the computing device may determine an output modality (e.g., visual, audible, etc.) based on aspects such as the risk level, worker activity, type of PPE, work environment or hazards, or any other suitable context information. For instance, the computing device may output urgent messages (e.g., an alert of an imminent hazard) even when the worker is performing a task with a relatively high risk level. In another instance, the computing device may visually output non-urgent messages when the risk level is relatively low. 
     In this way, the computing device may determine a risk level for a worker and/or an urgency level of a message. The computing device may selectively output messages via a display device of the PPE device based on the risk level for the worker and/or urgency level of the message. By selectively outputting messages when the risk level is low and/or the urgency level is high, the computing device may reduce distractions to the worker. Reducing distractions to the worker may increase worker safety, for example, by enabling the worker to focus while performing dangerous tasks. 
     In one example, the disclosure describes a system that includes an article PPE associated with a first worker and at least one computing device. The article of PPE includes a display device. The at least one computing device is configured to receive an indication of audio data from a second worker, the audio data including a message; determine a risk level for the first worker; determine, based at least in part on the risk level, whether to display a visual representation of the message; and responsive to determining to display the visual representation of the message, output, for display by the display device, the visual representation of the message. 
     In another example, the disclosure describes an article of PPE that includes a display device and at least one computing device. The at least one computing device is configured to: receive an indication of audio data from a second worker, the audio data including a message; determine a risk level for the first worker; determine, based at least in part on the risk level, whether to display a visual representation of the message; and responsive to determining to display the visual representation of the message, output, for display by the display device, the visual representation of the message. 
     The details of one or more examples of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an example system for managing worker communication in a work environment while workers are utilizing personal protective equipment, in accordance with various techniques of this disclosure. 
         FIG. 2  is a conceptual diagram illustrating example operations of an article of personal protective equipment, in accordance with various techniques of this disclosure. 
         FIG. 3  is a conceptual diagram illustrating an example article of personal protective equipment, in accordance with various techniques of this disclosure. 
         FIG. 4  is a conceptual diagram illustrating an example personal protective equipment management system, in accordance with various techniques of this disclosure. 
         FIG. 5  is a flowchart illustrating example operations of an example computing system, in accordance with various techniques of this disclosure. 
     
    
    
     It is to be understood that the embodiments may be utilized and structural changes may be made without departing from the scope of the invention. The figures are not necessarily to scale. Like numbers used in the figures refer to like components. However, it will be understood that the use of a number to refer to a component in a given figure is not intended to limit the component in another figure labeled with the same number. 
     DETAILED DESCRIPTION 
       FIG. 1  is a block diagram illustrating an example system  2  for managing worker communication in a work environment while workers are utilizing personal protective equipment (PPE), according to techniques described in this disclosure. In the example of  FIG. 1 , environment  8  includes a plurality of workers  10 A- 10 B (collectively, workers  10 ) utilizing PPE  13 A- 13 B (collectively, PPE  13 ). 
     As shown in the example of  FIG. 1 , system  2  represents a computing environment in which computing device(s) within an environment  8  electronically communicate with one another and/or with personal protection equipment management system (PPEMS)  6  via one or more computer networks  4 . PPEMS  6  may include distributed computing platform (e.g., a cloud computing platform executing on various servers, virtual machines and/or containers within an execution environment provided by one or more data centers), physical servers, desktop computing devices or any other type of computing system. 
     Environment  8  represents a physical environment, such as a work environment, in which one or more individuals, such as workers  10 , utilize personal protective equipment  13  while engaging in tasks or activities within the respective environment. Examples of environment  8  include an industrial warehouse, a construction site, a mining site, a manufacturing site, among others. 
     As shown in this example, environment  8  may include one or more articles of equipment  30 A- 30 C (collectively, equipment  30 ). Examples of equipment  30  may include machinery, industrial tools, robots, individual manufacturing lines or stages, among others. For example, equipment  30  may include HVAC equipment, computing equipment, manufacturing equipment, or any other type of equipment utilized within a physical work environment. Equipment  30  may be moveable or stationary. 
     In the example of  FIG. 1 , PPE  13  may include head protection. As used throughout this disclosure, head protection may refer to any type of PPE worn on the worker&#39;s head to protect the worker&#39;s hearing, sight, breathing, or otherwise protect the worker. Examples of head protection include respirators, welding helmets, visors, shields, earmuffs, eyewear, or any other type of PPE that is worn on a worker&#39;s head. As illustrated in  FIG. 1 , PPE  13 A includes speakers  32 A, display device  34 A, and microphone  36 A. Similarly, PPE  13 B may include speakers  32 B, display device  34 B, and microphone  36 B. 
     Each article of PPE  13  may include one or more output devices for outputting data that is indicative of operation of PPE  13  and/or generating and outputting communications to the respective worker  10 . For example, PPE  13  may include one or more devices to generate audible feedback (e.g., speaker  32 A or  32 B, collectively “speakers  32 ”). As another example, PPE  13  may include one or more devices to generate visual feedback, such as display device  34 A or  34 B (collectively, “display devices  34 ”), light emitting diodes (LEDs) or the like. As yet another example, PPE  13  may include one or more device to generate tactile feedback (e.g., a device that vibrates or provides other haptic feedback). 
     Each article of PPE  13  is configured to communicate data, such as sensed motions, events and conditions, over network  12  via wireless communications, such as via a time division multiple access (TDMA) network or a code-division multiple access (CDMA) network, or via 802.11 WiFi® protocols, Bluetooth® protocol, Digital Enhanced Cordless Telecommunications (DECT), or the like. In some such examples, one or more of the PPEs  13  communicates directly with a wireless access point  19 , and through wireless access point  19  to PPEMS  6 . 
     In general, environment  8  may include computing facilities (e.g., a local area network) by which sensing stations  21 , beacons  17 , and/or PPE  13  are able to communicate with PPEMS  6 . For examples, environments  8  may include network  12 . In some examples, network  12  enables PPE  13 , equipment  30 , and/or computing devices  16  to communicate with one another and/or other computing devices (e.g., computing devices  18  or PPEMS  6 ). Network  12  may include one or more wireless networks, such as 802.11 wireless networks, 802.15 ZigBee networks, CDMA networks, TDMA networks, and the like. Environment  8  may include one or more wireless access points  19  to provide support for wireless communications. In some examples, environment  8  may include a plurality of wireless access points  19  that may be geographically distributed throughout the environment to provide support for wireless communications throughout the work environment. 
     As shown in the example of  FIG. 1 , environment  8  may include one or more wireless-enabled beacons  17  that provide location data within the work environment. For example, beacon  17  may be GPS-enabled such that a controller within the respective beacon may be able to precisely determine the position of the respective beacon. In some examples, beacons  17  may not be GPS-enabled. In such examples, beacon  17  and/or an article of PPE  13  may determine a location of the article of PPE  13  based on determining that beacon  17  and the article of PPE  13  are within proximity of one another. In some instances, beacon  17  and/or an article of PPE  13  may determine whether beacon  17  and article of PPE  13  are within proximity of one another using a short-range communication protocol such as BLUETOOTH®, RFID, Near-field communication (NFC), among others. Based on wireless communications with one or more of beacons  17 , an article of PPE  13  is configured to determine the location of the worker within environment  8 . In this way, event data reported to PPEMS  6  may be stamped with positional data to aid analysis, reporting and analytics performed by PPEMS  6 . 
     In addition, environment  8  may include one or more wireless-enabled sensing stations  21 . Each sensing station  21  includes one or more sensors and a controller configured to output environmental data indicative of sensed environmental conditions. Moreover, sensing stations  21  may be positioned within respective geographic regions of environment  8  or otherwise interact with beacons  17  to determine respective positions and include such positional data when reporting environmental data to PPEMS  6 . As such, PPEMS  6  may be configured to correlate the sensed environmental conditions with the particular regions and, therefore, may utilize the captured environmental data when processing event data received from PPE  13  and/or sensing stations  21 . For example, PPEMS  6  may utilize the environmental data to aid generating alerts or other instructions for PPE  13  and for performing predictive analytics, such as determining any correlations between certain environmental conditions (e.g., heat, humidity, visibility) with abnormal worker behavior or increased safety events. As such, PPEMS  6  may utilize current environmental conditions to aid prediction and avoidance of imminent safety events. Example environmental conditions that may be sensed by sensing stations  21  include but are not limited to temperature, humidity, presence of harmful gas, pressure, visibility, wind and the like. Safety events may refer to heat related illness or injury, cardiac related illness or injury, or eye or hearing related injury or illness, or any other events that may affect the health or safety of a worker. 
     In addition, environment  8  may include computing facilities that provide an operating environment for end-user computing devices  16  for interacting with PPEMS  6  via network  4 . In one example, environment  8  may include one or more safety managers that may utilize computing devices  16 , for example, to oversee safety compliance within the environment. 
     Remote users  24  may be located outside of environment  8 . Users  24  may use computing devices  18  to interact with PPEMS  6  (e.g., via network  4 ) or communicate with workers  10 . For purposes of example, computing devices  16 ,  18  may be laptops, desktop computers, mobile devices such as tablets or so-called smart phones, or any other type of device that may be used to interact or communicate with workers  10  and/or PPEMS  6 . 
     Users  24  may interact with PPEMS  6  to control and actively manage many aspects of PPE  13  and/or equipment  30  utilized by workers  10 , such as accessing and viewing usage records, analytics and reporting. For example, users  24  may review data acquired and stored by PPEMS  6 . The data acquired and stored by PPEMS  6  may include data specifying task starting and ending times, changes to operating parameters of an article of PPE  13 , status changes to components of an article of PPE  13  (e.g., a low battery event), motion of workers  10 , environment data, and the like. In addition, users  24  may interact with PPEMS  6  to perform asset tracking and to schedule maintenance events for individual article of PPE  13  or equipment  30  to ensure compliance with any procedures or regulations. PPEMS  6  may allow users  24  to create and complete digital checklists with respect to the maintenance procedures and to synchronize any results of the procedures from computing devices  18  to PPEMS  6 . 
     PPEMS  6  provides an integrated suite of personal safety protection equipment management tools and implements various techniques of this disclosure. That is, PPEMS  6  provides an integrated, end-to-end system for managing personal protection equipment, e.g., PPE, used by workers  10  within one or more physical environments  8 . The techniques of this disclosure may be realized within various parts of system  2 . 
     PPEMS  6  may integrate an event processing platform configured to process thousands or even millions of concurrent streams of events from digitally enabled devices, such as equipment  30 , sensing stations  21 , beacons  17 , and/or PPE  13 . An underlying analytics engine of PPEMS  6  may apply models to the inbound streams to compute assertions, such as identified anomalies or predicted occurrences of safety events based on conditions or behavior patterns of workers  10 . 
     Further, PPEMS  6  may provide real-time alerting and reporting to notify workers  10  and/or users  24  of any predicted events, anomalies, trends, and the like. The analytics engine of PPEMS  6  may, in some examples, apply analytics to identify relationships or correlations between worker data, sensor data, environmental conditions, geographic regions and other factors and analyze the impact on safety events. PPEMS  6  may determine, based on the data acquired across populations of workers  10 , which particular activities, possibly within certain geographic region, lead to, or are predicted to lead to, unusually high occurrences of safety events. 
     In this way, PPEMS  6  tightly integrates comprehensive tools for managing personal protective equipment with an underlying analytics engine and communication system to provide data acquisition, monitoring, activity logging, reporting, behavior analytics and alert generation. Moreover, PPEMS  6  provides a communication system for operation and utilization by and between the various elements of system  2 . Users  24  may access PPEMS  6  to view results on any analytics performed by PPEMS  6  on data acquired from workers  10 . In some examples, PPEMS  6  may present a web-based interface via a web server (e.g., an HTTP server) or client-side applications may be deployed for devices of computing devices  16 ,  18  used by users  24 , such as desktop computers, laptop computers, mobile devices such as smartphones and tablets, or the like. 
     In accordance with techniques of this disclosure, articles of PPE  13 A- 13 B may each include a respective computing device  38 A- 38 B (collectively, computing devices  38 ) configured to manage worker communications while workers  10 A- 10 B are utilizing PPE  13 A- 13 B within work environment  8 . Computing devices  38  may determine whether to output messages to one or more of workers  10  within work environment  8 . Although shown as integrated within PPEs  13 , computing devices  38  may be external to the PPEs and located within environment  8  (e.g., computing device  16 ) or located external to the work environment and reachable through network  4 , such as PPEMS  6 . 
     In the example of  FIG. 1 , each PPE  13  may enable communication with other workers  10  and/or remote users  24 , for example, via speakers  32 , display devices  34 , and microphones  36 . In one example, worker  10 A may communicate with worker  10 B and/or remote user  24 . For example, microphone  36 A may detect audio input (e.g., speech) from worker  10 A. The audio input may include a message for worker  10 B. In some instances, workers  10  may be engaged in a casual conversation or may be discussing work related information, such as working together to complete a task within work environment  8 . 
     Computing device  38 A receives audio data from microphone  36 A, where the audio data includes a message. Computing device  38 A outputs an indication of the audio data to another computing device, such as computing device  38 B of PPE  13 B, computing devices  16 ,  18 , and/or PPEMS  6 . In some instances, the indication of the audio data includes the audio data. For instance, computing device  38 A may output an analog signal that includes the audio data. In another instance, computing device  38 A may encode the audio data into a digital signal and outputs the digital signal to computing device  38 B. In some examples, the indication of the audio data includes text indicative of the message. For example, computing device  38 A may perform natural language processing (e.g., speech recognition) to convert the audio data to text, such that computing device  38 A may output a data signal that includes a digital representation of the text. In some scenarios, computing device  38 A outputs a graphical user interface that includes the text prior to sending the indication of the audio data to computing device  38 B, which may allow worker  10 A to verify the accuracy of the text prior to sending. 
     Computing device  38 B receives the indication of the audio data from computing device  38 A. Computing device  38 B may determine whether to output a representation (e.g., visual, audible, or tactile representation) of the message included in the audio data. A visual representation of the message may include text or an image (a picture, icon, emoji, gif, or other image). In some examples, computing device  38 B determines whether to output a visual representation of the message based at least in part on a risk level for worker  10 B, an urgency level of the message, or both. 
     In some examples, computing device  38 B determines a risk level for worker  10 B based at least in part on worker data associated with worker  10 B, task data associated with a task performed by worker  10 B, sensor data, event data associated with PPE  13 B utilized by worker  10 B, or a combination thereof. The computed risk level for the worker may indicate a predicted likelihood, based on any and/or combinations of these factors, of the worker experiencing a safety event if presented with the visual representation at that time. Worker data may include data indicative of biographical characteristics of the worker (e.g., age, health information, etc.), a training level or experience level of the worker, an amount of time the worker has been working that day or shift, or any other data associated with the worker. Task data may include data indicating one or more tasks performed by the worker, such as a type of the task, a location of the task, a complexity of the task, a severity of harm to the worker, a likelihood of harm to the worker, and/or a duration of the task. Sensor data may include current physiological data indicative of physiological conditions of the worker, environmental data indicating environmental characteristics of environment  8 , or both. 
     As described herein, the complexity of a task may refer to a degree of difficulty of the task. For example, computing device  38 B may determine a welding task is relatively complex and may determine a painting task is relatively simple. The severity of harm may refer to an amount of harm the worker is likely to experience if the worker experiences a particular safety event associated with the task. In other words, the severity of harm may to the worker may be associated with a particular safety event associated with a given task. For instance, safety events associated with working on scaffolding or otherwise working at height may include falling, vertigo, or both. Computing device  38 B may determine the severity of harm to the worker for a fall is relatively high while the severity of harm to the worker for vertigo is relatively low. Similarly, safety events associated with working with chemicals may include a chemical burn, skin or eye irritation, or both. Computing device  38 B may determine the severity of a chemical burn is relatively high and that the severity of skin or eye irritation is relatively low. As used herein, the likelihood of harm to the worker may refer to a probability of a worker experiencing a safety event. In some instances, the likelihood of harm may represent the aggregate probability of the worker experiencing any safety event. In another instance, each task and/or safety event is associated with a respective likelihood of harm. 
     In one scenario, computing device  38 B determines the risk level for worker  10 B based one or more rules. The rules may be pre-programmed or trained, for instance, via machine learning. Computing device  38 B may determine the risk level for worker  10 B by applying one or more rules to worker data associated with worker  10 B, task data associated with a task performed by worker  10 B, event data associated with PPE  13 B utilized by worker  10 B, and/or sensor data. In one example, computing device  38 B may apply the rules to a type of task performed by worker  10 B and outputs a risk level for worker  10 B. For instance, computing device  38 B may determine the risk level for worker  10 B is relatively high (e.g., 80 out of 100) when the worker is performing a welding task. In another instance, computing device  38 B may determine the risk level for worker  10 B is relatively low (e.g., 20% out of 100) when the worker is painting. As another example, computing device  38 B may apply the rules to sensor data indicative of physiological conditions of worker  10 B and output a risk level for worker  10 B. For example, computing device  38 B may determine the risk level is relatively high when the worker is breathing relatively hard (e.g., above a threshold breathing rate) or has a relatively high heart rate (e.g., above a threshold heart rate). 
     Computing device  38 B, in some examples, determines whether to output a visual representation of the message based at least in part on the risk level for the worker. For example, computing device  38 B may determine whether the risk level satisfies a threshold risk level. In such examples, computing device  38 B may determine to output the representation of the message in response to determining the risk level for the worker does not satisfy (e.g., is less than) the threshold risk level. Outputting the visual representation of the message may enable worker  10 B to receive communications from other workers  10  or remote users  24 , for example, when doing so is not likely to distract worker  10 B or otherwise increase the risk of a safety event. In another example, computing device  38 B may determine to refrain from outputting the message in response to determining the risk level satisfies (e.g., is greater than or equal to) the threshold risk level. Refraining from outputting the visual representation of the message may reduce the risk of a safety event, for example, by reducing the risk that worker  10 B will be distracted by the message when he or she should be focusing on the task he or she is performing. 
     Computing device  38 B may determine an urgency level of the message. In some instances, the data signal received from computing device  38 A includes metadata for the message. The metadata may include data indicating an urgency level of the message, a sender of the message, a location of the sender, a timestamp, among other data. In one example, a user of computing device  38 A specifies the urgency level such that computing device  38 A indicates the urgency level of the message in the metadata. In another example, computing device  38 A may determine the urgency level and may indicate the urgency level of the message in the metadata. 
     In some examples, computing device  38 A determines the urgency level of the message based on physiological conditions of the sender (e.g., worker  10 A). For example, computing device  38 A may assign the urgency level of the message based on the sender&#39;s (worker  10 A) heart rate and/or breathing rate. For example, high heart rates and/or breathing rates may indicate worker  10 A is distressed or in-danger. Similarly, low heart rates and/or breathing rates may indicate worker  10 A is distressed or in-danger. In some examples, computing device  38 A may assign higher urgency levels as worker  10 A&#39;s heart rate and/or breathing rate increase or decrease outside of a threshold range of heart rates and breathing rates, respectively. 
     Computing device  38 A or  38 B may determine the urgency level of the message based on the audio characteristics of the audio data. The audio characteristics of the audio data may include a tone, frequency, and/or decibel level of the audio data. In some examples, the audio data may be defined by one set of audio characteristics when worker  10 A is stressed or panicked and may be defined by another set of audio characteristics when worker  10 A is calm or relaxed. In one example, computing device  38 B may assign one urgency level (e.g., “urgent”, or 80 out of 100) based on the first set of audio characteristics and a different urgency level (e.g., “normal”, or 40 out of 100) based on the second set of audio characteristics. Similarly, computing device  38 A may determine the urgency level of the message based on the audio characteristics and may include an indication of the urgency level in the metadata. 
     Computing device  38 A or computing device  38 B may determine the urgency level of the message based on the content of the message. For example, computing device  38 A or computing device  38 B may perform natural language processing (e.g., speech recognition) on the audio data to determine the content of the message. The content may indicate a request for assistance, a type of assistance requested, the task being performed by the sender, the location of the sender or a location of the task to be performed, a safety hazard (e.g., fire, dangerous weather, etc.), or other a combination thereof. For instance, computing device  38 B may determine the message includes one or more keyword words indicating a request for assistance and may assign a relatively high urgency level to the message. 
     As yet another example, computing device  38 A or  38 B may determine the urgency level of the message based on user data associated with the sender (e.g., worker  10 A), such as an identity of the sender or a location of the sender. For example, computing device  38 B may determine (e.g., based on the metadata) that the sender is not located within work environment  8  and may assign a relatively low urgency level to the message. In this way, computing device  38 B may prioritize messages from workers in the same area or who are likely to be performing similar tasks. As another example, computing device  38 B may assign the urgency level based on the identity of the sender. For example, computing device  38 B may assign a relatively high urgency level to messages from certain users (e.g., a supervisor of worker  10 B, such as user  24 ) and may assign a lower urgency level to messages from worker  10 A (in comparison to messages from user  24 ). 
     Computing device  38 B determines whether to output a visual representation of the message based at least in part on the risk level for the worker, the urgency level of the message, or both. Computing device  38 B may determine whether the risk level for the worker satisfies a threshold risk level. In one example, computing device  38 B outputs the visual representation of the message in response to determining that the risk level for the worker does not satisfy (e.g., is less than) a threshold risk level. For instance, computing device  38 B may infer that displaying a visual representation of a message is not likely to increase the risk of worker  10 B experiencing a safety event when the risk level is less than the threshold risk level, such that the visual representation of the message (e.g., text, an icon, etc.) can safely be displayed. In another example, computing device  38 B may refrain from outputting a visual representation of the message in response to determining that the risk level for the worker satisfies (e.g., is greater than or equal to) the threshold risk level. In this way, computing device  38 B may dynamically manage the information output to worker  10 B to improve worker safety by refraining from potentially distracting the worker when the risk to the worker safety is relatively high. 
     Computing device  38 B may determine whether the urgency level for the message satisfies a threshold urgency level. In some examples, computing device  38 B outputs the visual representation of the message in response to determining that the urgency level for the message satisfies (e.g., is greater than or equal to) a threshold urgency level. In another example, computing device  38 B may refrain from outputting a visual representation of the message in response to determining that the urgency level for the message does not satisfy (e.g., is less than) the threshold urgency level. In this way, computing device  38 B may dynamically output information to worker  10 B to improve worker safety by outputting urgent messages while refraining outputting less urgent messages. 
     Computing device  38 B may determine whether to output the visual representation of the message based on the risk level for the worker and the urgency level for the message. In some examples, computing device  38 B may compare the urgency level of the message to different threshold urgency levels and/or compare the risk level to different risk levels. In one example, when computing device  38 B determines the risk level for the worker is a first risk level (e.g., “high”), computing device  38 B may compare the urgency level to a first urgency level to determine whether to output the visual representation of the message. For example, when the risk level is “high”, computing device  38 B may output a visual representation of the message when the urgency level of the message is, for example, “life threatening,” and may refrain from a visual representation of the message for all other (e.g., lower, less urgent) messages. In another example, when computing device  38 B determines the risk level for the worker is a different risk level (e.g., “medium”), computing device  38 B may compare the urgency level to a second urgency level to determine whether to output the visual representation of the message. For example, computing device  38 B may output visual representations of messages with an urgency level of, for example, “important,” “very important,” or “life threatening,” when the risk level for worker  10 B is, for example, “medium.” 
     Responsive to determining to output the visual representation of the message, computing device  38 B may cause display device  34 B to display the visual representation of the message. For instance, computing device  38 B may cause display device  34 B to output a graphical user interface that includes the visual representation of the message. The visual representation may include text, an icon, an emoji, a GIF, or other visually detectable representation of the message. 
     Computing device  38 B may determine whether to output an audible representation of the message in a manner similar to determining whether to output a visual representation of the message. In one example, audible messages may be less distracting to the worker, such that computing device  38 B may output an audible representation of a message when the risk level for the worker is relatively high while refraining from outputting a visual representation of the message at the same risk level. Responsive to determining to output the audible representation of the message, computing device may cause speaker  32 B to output the audible representation of the message. 
     Computing device  38 B may receive a message from one or more articles of equipment  30 , one or more sensing stations  21 , PPEMS  6 , or a combination thereof, and determine whether to output a representation of the message. The message may include a flag or metadata indicating an urgency of the message. 
     In one example, computing device  38 B receives a message from sensing station  21  where the message includes information indicative of one or more environmental hazards within environment  8 . Computing device  38 B may determine an urgency level of the message from sensing station  21 . For example, the message may indicate levels of environmental characteristics of the work environment, such as the temperature, harmful gas concentration levels, sound decibel levels, among others. Computing device  38 B may compare the levels of the environmental characteristics to one or more thresholds associated with the environmental characteristics to determine the urgency level of the message. For instance, computing device  38 B may determine the urgency level of the message is “high” in response to determining harmful gas levels are above a safety threshold. Computing device  38 B may compare the urgency level of the message to a threshold urgency level to determine whether to output a representation (e.g., audible, visual, tactile) of the message to worker  10 B. Additionally or alternatively, in some instances, computing device  38 B may determine whether to output a representation of the message from sensing stations  21  based on the risk level for the worker, as described above. 
     Computing device  38 B may determine an urgency level of a message received from equipment  30  to determine whether to output a representation of the message from equipment  30 . For example, the message may indicate characteristics of the article of equipment  30 , such as a health status of the equipment (e.g., “normal”, “malfunction”, “overheating”, among others), usage status (e.g., indicative of battery life, filter life, oxygen levels remaining, among others), or any other information about the operation of equipment  30 . Computing device  38 B may compare the characteristics to one or more thresholds associated with the characteristics to determine the urgency level of the message. For instance, computing device  38 B may determine the message is “urgent” in response to remaining oxygen left in an oxygen tank for a respirator is less than a safety threshold. Computing device  38 B may compare the urgency level of the message to a threshold urgency level to determine whether to output a representation (e.g., audible, visual, tactile) of the message to worker  10 B. Additionally or alternatively, in some instances, computing device  38 B may determine whether to output a representation of the message from equipment  30  based on the risk level for the worker, as described above. 
     In this way, a computing device  38  may selectively output messages to a worker  10  based on the urgency level of the message and/or a risk level for the worker. Selectively outputting messages may reduce the risk of distracting a worker (e.g., a worker performing a dangerous task). Reducing distractions to the worker may increase worker safety. 
     While computing device  38  is described as managing communications between workers  10 , in some examples, PPEMS  6  may include all or a subset of the functionality of computing device  38 . For example, PPEMS  6  may determine a risk level for the worker and/or an urgency level of the message. PPEMS  6  may determine whether to output a representation of the message to the worker based on the risk level and/or urgency level. In some examples, PPEMS  6  may cause an article of PPE  13  to output a visual representation of the message, for example, by outputting a command to the article of PPE  13  to display a GUI that includes at least a portion of the message. In one example, PPEMS  6  may determine to refrain from outputting the representation of the message. In such examples, PPEMS  6  may refrain from outputting the command to the article of PPE  13  or may output a command causing the article of PPE  13  to refrain from outputting the representation of the message. 
       FIG. 2  is a conceptual diagram illustrating example operations of an article of personal protective equipment, in accordance with various techniques of this disclosure. In the example of  FIG. 2 , workers  10  may communicate with one another while utilizing PPE  13 . 
     Worker  10 B (e.g., Amy) may speak a first message (e.g., “Big plans this weekend?”) to worker  10 A (e.g., Doug). Microphone  36 B may detect audio input (e.g., the words spoken by worker  10 B) and may generate audio data that includes the message. Computing device  38 B may output an indication of the audio data to computing device  38 A associated with worker  10 A. The indication of the audio data may include an analog signal that includes the audio data, a digital signal encoded with the audio data, or text indicative of the first message. 
     Computing device  38 A may determine a risk level for worker  10 A. In the example of  FIG. 2 , computing device  38 A determines the risk level for worker  10 A is “Low”. Computing device  38 A may determine whether to display a visual representation of the first message from worker  10 B based at least in part on the risk level for worker  10 A. For example, computing device  38 A may determine the risk level for worker  10 A does not satisfy (e.g., is less than) a threshold risk level. In the example of  FIG. 2 , computing device  38 A determines to output a visual representation of the first message in response to determining the risk level for worker  10 A does not satisfy the threshold risk level. For example, computing device  38 A may cause display device  34 A to display graphical user interface  202 A. Graphical user interface  202 A may include a text representation of the first message. In some examples, graphical user interface  202 A includes a visual representation of the second message. For example, graphical user interface  202  may include message grouped by the parties involved in the communication (e.g., sender, recipient), topic, etc. 
     After receiving the first message, microphone  36 A may detect a second message spoken by worker  10 A (e.g., “Sorry for the delay. No, you?”) and may generate audio data that includes the second message. Computing device  38 A may receive the audio data from microphone  36 A and output an indication of the audio data to computing device  38 B. 
     Computing device  38 B may determine whether to output a visual indication of the second message based at least in part on a risk level for worker  10 B. In the example of  FIG. 2 , computing device  38 B determines the risk level for worker  10 B is “Medium”. In some examples, computing device  38 B determines to refrain from outputting a visual representation of the second message in response to determining the risk level for worker  10 B satisfies (e.g., is greater than or equal to) the threshold risk level. 
     Computing device  38 B may receive an indication of audio data that includes a third message. For instance, computing device  38 B may receive the third message from remote user  24  of  FIG. 1  (e.g., a supervisor of worker  10 B). In some examples, computing device  38 B determines whether to output a visual representation of the third message based at least in the risk level for worker  10 B and an urgency level for the third message. In the example of  FIG. 2 , computing device  38 B may determine the urgency level for the third message is “Medium”. Computing device  38 B may determine a threshold risk level for worker  10 B based at least in part on the urgency level of the third message. For example, computing device  38 B may determine the threshold urgency level associated with worker  10 B&#39;s current risk level is a “Medium” urgency level. In such examples, computing device  38 B may compare the urgency level for the third message to the threshold urgency level. Computing device may determine to output the visual representation of the third message in response to determining the urgency level for the third message satisfies (e.g., is equal to or greater than) the threshold urgency level. For example, computing device  38 B may output the visual representation of the third message by causing display device  34 B to output a graphical user interface  202 B that includes a representation of the third message. In some instances, as shown in  FIG. 2 , graphical user interface  202 B includes a text representation of the third message. In another instance, graphical user interface  202 B may include an image representing the third message (e.g., the visual representation may include an icon such as a storm-cloud when the third message includes information about an impending thunderstorm). 
     In some examples, the third message includes an indication of a task associated with another worker (e.g., Steve). In the example of  FIG. 2 , the third message indicates that Steve is performing a task. In such examples, computing device  38 B may output, for display, data associated with the third message. In some instances, the data associated with the third image includes a map indicating a location of the task, one or more articles of PPE associated with the task, one or more articles of equipment associated with the task, or a combination thereof. In other words, in one example, graphical user interface  202 B may include a map indicating a location of the task performed by another worker, one or more articles of PPE associated with that task, and/or one or more articles of equipment associated with that task. 
       FIG. 3  is a conceptual diagram illustrating an example PPE that includes a computing device, in accordance with aspects of this disclosure. PPE  13 A includes head protection that is worn on the worker&#39;s head to protect the worker&#39;s hearing, sight, breathing, or otherwise protect the worker. In the example of  FIG. 3 , PPE  13 A includes computing device  300 . Computing device  300  may be an example of computing devices  38  of  FIG. 1 . 
     Computing device  300  includes one or more processors  302 , one or more storage devices  304 , one or more communication units  306 , one or more sensors  308 , one or more user interface (UI) devices  310 , sensor data  320 , models  322 , worker data  324 , and task data  326 . Processors  302 , in one example, are configured to implement functionality and/or process instructions for execution within computing device  300 . For example, processors  302  may be capable of processing instructions stored by storage device  304 . Processors  302  may include, for example, microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate array (FPGAs), or equivalent discrete or integrated logic circuitry. 
     Storage device  304  may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device  304  may include one or more of a short-term memory or a long-term memory. Storage device  304  may include, for example, random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), magnetic hard discs, optical discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable memories (EEPROM). 
     In some examples, storage device  304  may store an operating system or other application that controls the operation of components of computing device  300 . For example, the operating system may facilitate the communication of data from electronic sensors  308  to communication unit  306 . In some examples, storage device  304  is used to store program instructions for execution by processors  302 . Storage device  304  may also be configured to store information within computing device  300  during operation. 
     Computing device  300  may use one or more communication units  306  to communicate with external devices via one or more wired or wireless connections. Communication units  306  may include various mixers, filters, amplifiers and other components designed for signal modulation, as well as one or more antennas and/or other components designed for transmitting and receiving data. Communication units  306  may send and receive data to other computing devices using any one or more suitable data communication techniques. Examples of such communication techniques may include TCP/IP, Ethernet, Wi-Fi®, Bluetooth®, 4G, LTE, DECT, to name only a few examples. In some instances, communication units  306  may operate in accordance with the Bluetooth Low Energy (BLU) protocol. In some examples, communication units  306  may include a short-range communication unit, such as an RFID reader. 
     Computing device  300  includes one or more sensors  308 . Examples of sensors  308  include a physiological sensor, an accelerometer, a magnetometer, an altimeter, an environmental sensor, among other examples. In some examples, physiological sensors include a heart rate sensor, breathing sensor, sweat sensor, etc. 
     UI device  310  may be configured to receive user input and/or output information, also referred to as data, to a user. One or more input components of UI device  310  may receive input. Examples of input are tactile, audio, kinetic, and optical input, to name only a few examples. For example, UI device  310  may include a mouse, keyboard, voice responsive system, video camera, buttons, control pad, microphone  316 , or any other type of device for detecting input from a human or machine. In some examples, UI device  310  may be a presence-sensitive input component, which may include a presence-sensitive screen, touch-sensitive screen, etc. 
     One or more output components of UI device  310  may generate output. Examples of output are data, tactile, audio, and video output. Output components of UI device  310 , in some examples, include a display device  312  (e.g., a presence-sensitive screen, a touch-screen, a liquid crystal display (LCD) display, a Light-Emitting Diode (LED) display, an optical head-mounted display (HMD), among others), a light-emitting diode, a speaker  314 , or any other type of device for generating output to a human or machine. UI device  310  may include a display, lights, buttons, keys (such as arrow or other indicator keys), and may be able to provide alerts or otherwise provide information to the user in a variety of ways, such as by sounding an alarm or vibrating. 
     According to aspects of this disclosure, computing device  300  may be configured to manage worker communications while a worker utilizes an article of PPE that includes computing device  300  within a work environment. For example, computing device  300  may determine whether to output a representation of one or more messages to worker  10 A. 
     Computing device  300  receives an indication of audio data from a computing device, such as computing devices  38 , PPEMS  6 , or computing devices  16 ,  18  of  FIG. 1 . Computing device  300  may determine whether to output a representation (e.g., visual, audible, or tactile representation) of the message. In some examples, computing device  300  determines whether to output a visual representation of the message based at least in part on a risk level for worker  10 A and/or an urgency level of the message. 
     Computing device  300  may determine the risk level for worker  10 A and/or the urgency level for the message based on one or more rules. In some examples, the one or more rules are stored in models  322 . Although other technologies can be used, in some examples, the one or more rules are generated using machine learning. In other words, storage device  304  may include executable code generated by application of machine learning. The executable code may take the form of software instructions or rule sets and is generally referred to as a model that can subsequently be applied to data, such as sensor data  320 , worker data  324 , and/or task data  326 . 
     Example machine learning techniques that may be employed to generate models  322  can include various learning styles, such as supervised learning, unsupervised learning, and semi-supervised learning. Example types of algorithms include Bayesian algorithms, Clustering algorithms, decision-tree algorithms, regularization algorithms, regression algorithms, instance-based algorithms, artificial neural network algorithms, deep learning algorithms, dimensionality reduction algorithms and the like. Various examples of specific algorithms include Bayesian Linear Regression, Boosted Decision Tree Regression, and Neural Network Regression, Back Propagation Neural Networks, the Apriori algorithm, K-Means Clustering, k-Nearest Neighbor (kNN), Learning Vector Quantization (LUQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL), Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and Least-Angle Regression (LARS), Principal Component Analysis (PCA) and Principal Component Regression (PCR). 
     Models  322  include, in some example, separate models for individual workers, a population of workers, a particular environment, a type of PPE, a type of task, or combinations thereof. Computing device  300  may update models  322  based on additional data. For example, computing device  300  may update models  322  for individual workers, a population of workers, a particular environment, a type of PPE, or combinations thereof based on data received from PPE  13 , sensing stations  21 , or both. 
     Computing device  300  may apply one or more models  322  to sensor data  320 , worker data  324 , and/or task data  326  to determine a risk level for worker  10 A. In one example, computing device  300  may apply models  322  to a type of task performed by worker  10 A and outputs a risk level for worker  10 A. As another example, computing device  300  may apply models  322  to sensor data  320  indicative of physiological conditions of worker  10 A and output a risk level for worker  10 A. For example, computing device  300  may apply models  322  to physiological data generated by sensors  308  to determine the risk level is relatively high when physiological data indicates the worker is breathing relatively hard or has a relatively high heart rate (e.g., above a threshold heart rate). As another example, computing device  300  may apply models  322  to worker data  324  and output a risk level for worker  10 A. For example, computing device  300  may apply models  322  to worker data  324  to determine the risk level is relatively low when worker  10 A is relatively experienced and determine the risk level is relatively high when worker  10 A is relatively inexperienced. 
     In yet another example, computing device  300  applies models  322  to sensor data  320  and task data  326  to determine the risk level for worker  10 A. For example, computing device  300  may apply models  322  to sensor data  320  indicative of environmental characteristics (e.g., decibel levels of the ambient sounds in the work environment) and task data  326  (e.g., indicating a type of task, a location of a task, a duration of a task) to determine the risk level. For instance, computing device  300  may determine the risk level for worker  10 A is relatively high when the task involves dangerous equipment (e.g., sharp blades, etc.) and the noise in the work environment is relatively loud. 
     Computing device  300  may apply one or more models  322  to determine an urgency level of the message. In one example, computing device  300  applies models  322  to the audio characteristics of the audio data to determine the urgency level of the message. For example, computing device  300  may apply models  322  to the audio characteristics to determine that the audio characteristics of the audio data indicate the sender is afraid, such that computing device  300  may determine the urgency level for the message is high. 
     Computing device  300  may determine the urgency level of the message based on the content of the message and/or metadata for the message. For example, computing device  300  may perform natural language processing (e.g., speech recognition) on the audio data to determine the content of the message. In one example, computing device  300  may perform determine the content of the message and apply one or more of models  322  to the content to determine the urgency level of the message. For example, computing device  300  may determine the content of the message includes casual conversation and may determine based on applying models  322  that the urgency level for the message is low. As another example, computing device  300  applies models  322  to data metadata for the message (e.g., data indicating the sender of the message) and determines the urgency level for the message based on the metadata. 
     Computing device  300 , in some examples, determines whether to output a visual representation of the message based at least in part on the risk level for the worker, the urgency level of the message, or both. For example, computing device  300  may determine whether the risk level satisfies a threshold risk level. In such examples, computing device  300  may determine to output the representation of the message in response to determining the risk level for the worker does not satisfy (e.g., is less than) the threshold risk level. In another example, computing device  300  may determine to refrain from outputting the representation of the message in response to determining the risk level satisfies (e.g., is greater than or equal to) the threshold risk level. 
     In some scenarios, computing device  300  determines to output the representation of the message in response to determining that the urgency level for the message satisfies (e.g., is greater than or equal to) a threshold urgency level. The representation of the message may include a visual representation of the message, an audible representation of the message, a haptic representation of the message, or a combination therein. In one instance, computing device  300  may output a visual representation of the message via display device  312 . In another instance, computing device  300  outputs an audible representation of the message via speaker  314 . In one example, computing device  300  may determine to refrain from outputting a representation of the message in response to determining that the urgency level for the message does not satisfy (e.g., is less than) the threshold urgency level. 
     In some examples, computing device outputs the representation of the message as a visual representation in response to determining to output the representation of the message. In one example, computing device  300  determines whether the representation of the message should be a visual representation, an audible representation, or a haptic representation, or a combination thereof. In other words, computing device  300  may determine a type (e.g., audible, visual, haptic) of the output that represents the message. 
     Computing device  300  may determine the type of the output based on the components of PPE  13 A. In one example, computing device  300  determines the type of output includes an audible output in response to determining that computing device  300  includes speaker  314 . Additionally or alternatively, computing device  300  may determine that the type of output includes a visual output in response to determine the computing device  300  includes display device  312 . In this way, computing device  300  may output an audible representation of the message, a visual representation of the message, or both. 
     In some scenarios, computing device  300  determines a type of output based on the risk level of worker  10 A and/or the urgency level of the message. In one scenario, computing device  300  compares the risk level to one or more threshold risk levels to determine the type of output. For example, computing device  300  may determine the type of output includes a visual output in response to determining that the risk level for worker  10 A includes a “medium” threshold risk level and determine the type of output includes an audible risk level in response to determining the risk level includes a “high” threshold risk level. In other words, in one example, computing device  300  may output a visual representation of the message when the risk level for the worker is relatively low or medium risk. In examples where the risk level is relatively high, computing device  300  may output an audible representation of the message and may refrain from outputting a visual representation of the message. 
     In some examples, computing device  300  may store one or more received messages. For example, computing device  300  may store a message in response to determining to refrain from outputting a representation of the level. As one example, computing device  300  may store the message when the risk level for the worker satisfies the threshold risk level. In some instances, computing device  300  may output a representation of the message at a later time, for example, in response to determining the risk level for the worker does not satisfy the threshold risk level. For instance, computing device  300  may enable the worker to check stored messages and may output a visual, audible, and/or haptic representation of the message in response to receiving a user input to output one or more stored messages. 
     Computing device  300  may receive a message from a sensing station  21  of  FIG. 1 , PPEMS  6  of  FIG. 1 , computing devices  16 ,  18  of  FIG. 1 , equipment  30  of  FIG. 1 , or other device. Computing device  300  may determine whether to output a representation of the message based on an urgency of the message and/or the risk level for worker  10 A. For instance, computing device  300  may determine an urgency level of the message in a manner similar to determining the urgency level for messages received from other workers  10 . As one example, computing device  300  may determine whether to output a representation of a message received from an article of equipment  30  based on the urgency level of the message. The message may include data indicating characteristics of the article of equipment  30 , such as a health status of the equipment (e.g., “normal”, “malfunction”, “overheating”, among others), usage status (e.g., indicative of battery life, filter life, oxygen levels remaining, among others), or any other information about the operation of equipment  30 . Computing device  300  may compare the characteristics to one or more thresholds associated with the characteristics to determine the urgency level of the message. Computing device  300  may output a representation of the message in response to determining the urgency level satisfies a threshold urgency. Additionally or alternatively, in some instances, computing device  300  may determine whether to output a representation of the message based on the risk level for the worker, as described above. 
       FIG. 4  is a block diagram providing an operating perspective of PPEMS  6  when hosted as cloud-based platform capable of supporting multiple, distinct environments  8  having an overall population of workers  10 , in accordance with techniques described herein. In the example of  FIG. 4 , the components of PPEMS  6  are arranged according to multiple logical layers that implement the techniques of the disclosure. Each layer may be implemented by one or more modules comprised of hardware, software, or a combination of hardware and software. 
     In  FIG. 4 , safety equipment  62  include personal protective equipment (PPE)  13 , beacons  17 , and sensing stations  21 . Equipment  30 , safety equipment  62 , and computing devices  60  operate as clients  63  that communicate with PPEMS  6  via interface layer  64 . Computing devices  60  typically execute client software applications, such as desktop applications, mobile applications, and web applications. Computing devices  60  may represent any of computing devices  16 ,  18  of  FIG. 1 . Examples of computing devices  60  may include, but are not limited to a portable or mobile computing device (e.g., smartphone, wearable computing device, tablet), laptop computers, desktop computers, smart television platforms, and servers, to name only a few examples. 
     Client applications executing on computing devices  60  may communicate with PPEMS  6  to send and receive data that is retrieved, stored, generated, and/or otherwise processed by services  68 . The client applications executing on computing devices  60  may be implemented for different platforms but include similar or the same functionality. For instance, a client application may be a desktop application compiled to run on a desktop operating system or a mobile application compiled to run on a mobile operating system. As another example, a client application may be a web application such as a web browser that displays web pages received from PPEMS  6 . In the example of a web application, PPEMS  6  may receive requests from the web application (e.g., the web browser), process the requests, and send one or more responses back to the web application. In this way, the collection of web pages, the client-side processing web application, and the server-side processing performed by PPEMS  6  collectively provides the functionality to perform techniques of this disclosure. In this way, client applications use various services of PPEMS  6  in accordance with techniques of this disclosure, and the applications may operate within various different computing environment (e.g., embedded circuitry or processor of a PPE, a desktop operating system, mobile operating system, or web browser, to name only a few examples). 
     In some examples, the client applications executing at computing devices  60  may request and edit event data including analytical data stored at and/or managed by PPEMS  6 . In some examples, the client applications may request and display aggregate event data that summarizes or otherwise aggregates numerous individual instances of safety events and corresponding data obtained from safety equipment  62  and/or generated by PPEMS  6 . The client applications may interact with PPEMS  6  to query for analytics data about past and predicted safety events, behavior trends of workers  10 , to name only a few examples. In some examples, the client applications may output, for display, data received from PPEMS  6  to visualize such data for users of computing devices  60 . As further illustrated and described in below, PPEMS  6  may provide data to the client applications, which the client applications output for display in user interfaces. 
     As shown in  FIG. 4 , PPEMS  6  includes an interface layer  64  that represents a set of application programming interfaces (API) or protocol interface presented and supported by PPEMS  6 . Interface layer  64  initially receives messages from any of computing devices  60  for further processing at PPEMS  6 . Interface layer  64  may therefore provide one or more interfaces that are available to client applications executing on computing devices  60 . In some examples, the interfaces may be application programming interfaces (APIs) that are accessible over a network. Interface layer  64  may be implemented with one or more web servers. The one or more web servers may receive incoming requests, process and/or forward data from the requests to services  68 , and provide one or more responses, based on data received from services  68 , to the client application that initially sent the request. In some examples, the one or more web servers that implement interface layer  64  may include a runtime environment to deploy program logic that provides the one or more interfaces. As further described below, each service may provide a group of one or more interfaces that are accessible via interface layer  64 . 
     In some examples, interface layer  64  may provide Representational State Transfer (RESTful) interfaces that use HTTP methods to interact with services and manipulate resources of PPEMS  6 . In such examples, services  68  may generate JavaScript Object Notation (JSON) messages that interface layer  64  sends back to the computing devices  60  that submitted the initial request. In some examples, interface layer  64  provides web services using Simple Object Access Protocol (SOAP) to process requests from computing devices  60 . In still other examples, interface layer  64  may use Remote Procedure Calls (RPC) to process requests from computing devices  60 . Upon receiving a request from a client application to use one or more services  68 , interface layer  64  sends the data to application layer  66 , which includes services  68 . 
     As shown in  FIG. 4 , PPEMS  6  also includes an application layer  66  that represents a collection of services for implementing much of the underlying operations of PPEMS  6 . Application layer  66  receives data included in requests received from clients  63  and further processes the data according to one or more of services  68  invoked by the requests. Application layer  66  may be implemented as one or more discrete software services executing on one or more application servers, e.g., physical or virtual machines. That is, the application servers provide runtime environments for execution of services  68 . In some examples, the functionality interface layer  64  as described above and the functionality of application layer  66  may be implemented at the same server. 
     Application layer  66  may include one or more separate software services  68 , e.g., processes that communicate, e.g., via a logical service bus  70  as one example. Service bus  70  generally represents logical interconnections or set of interfaces that allows different services to send messages to other services, such as by a publish/subscription communication model. For instance, each of services  68  may subscribe to specific types of messages based on criteria set for the respective service. When a service publishes a message of a particular type on service bus  70 , other services that subscribe to messages of that type will receive the message. In this way, each of services  68  may communicate data to one another. As another example, services  68  may communicate in point-to-point fashion using sockets or other communication mechanisms. Before describing the functionality of each of services  68 , the layers are briefly described herein. 
     Data layer  72  of PPEMS  6  represents a data repository that provides persistence for data in PPEMS  6  using one or more data repositories  74 . A data repository, generally, may be any data structure or software that stores and/or manages data. Examples of data repositories include but are not limited to relational databases, multi-dimensional databases, maps, and hash tables, to name only a few examples. Data layer  72  may be implemented using Relational Database Management System (RDBMS) software to manage data in data repositories  74 . The RDBMS software may manage one or more data repositories  74 , which may be accessed using Structured Query Language (SQL). Data in the one or more databases may be stored, retrieved, and modified using the RDBMS software. In some examples, data layer  72  may be implemented using an Object Database Management System (ODBMS), Online Analytical Processing (OLAP) database or other suitable data management system. 
     As shown in  FIG. 4 , each of services  68 A- 68 C (collectively, services  68 ) is implemented in a modular form within PPEMS  6 . Although shown as separate modules for each service, in some examples the functionality of two or more services may be combined into a single module or component. Each of services  68  may be implemented in software, hardware, or a combination of hardware and software. Moreover, services  68  may be implemented as standalone devices, separate virtual machines or containers, processes, threads or software instructions generally for execution on one or more physical processors. In some examples, one or more of services  68  may each provide one or more interfaces that are exposed through interface layer  64 . Accordingly, client applications of computing devices  60  may call one or more interfaces of one or more of services  68  to perform techniques of this disclosure. 
     Event endpoint frontend  68 A operates as a frontend interface for exchanging communications with equipment  30  and safety equipment  62 . In other words, event endpoint frontend  68 A operates to as a frontline interface to equipment deployed within environments  8  and utilized by workers  10 . In some instances, event endpoint frontend  68 A may be implemented as a plurality of tasks or jobs spawned to receive individual inbound communications of event streams  69  that include data sensed and captured by equipment  30  and safety equipment  62 . For instance, event streams  69  may include message from workers  10  and/or from equipment  30 . Event streams  69  may include sensor data, such as PPE sensor data from one or more PPE  13  and environmental data from one or more sensing stations  21 . When receiving event streams  69 , for example, event endpoint frontend  68 A may spawn tasks to quickly enqueue an inbound communication, referred to as an event, and close the communication session, thereby providing high-speed processing and scalability. Each incoming communication may, for example, carry messages from workers  10 , remote users  24  of computing devices  60 , or captured data (e.g., sensor data) representing sensed conditions, motions, temperatures, actions or other data, generally referred to as events. Communications exchanged between the event endpoint frontend  68 A and safety equipment  62 , equipment  30 , and/or computing devices  60  may be real-time or pseudo real-time depending on communication delays and continuity. 
     In general, event processor  68 B operates on the incoming streams of events to update event data  74 A within data repositories  74 . In general, event data  74 A may include all or a subset of data generated by safety equipment  62  or equipment  30 . For example, in some instances, event data  74 A may include entire streams of data obtained from PPE  13 , sensing stations  21 , or equipment  30 . In other instances, event data  74 A may include a subset of such data, e.g., associated with a particular time period. Event processor  68 B may create, read, update, and delete event data stored in event data  74 A. 
     In accordance with techniques of this disclosure, in some examples, analytics service  68 C is configured to manage messages presented to workers in a work environment while the workers are utilizing PPE  13 . Analytics service  68 C may include all or a portion of the functionality of PPEMS  6  of  FIG. 1 , computing devices  38  of  FIG. 1 , and/or computing device  300  of  FIG. 3 . Analytics service  68 C may determine whether to cause an article of PPE  13  utilized by a first worker to output a representation of audio data received from a second worker. For example, PPEMS  6  may receive an indication of audio data that includes a message from worker  10 A of  FIG. 1 . In some instances, the indication of the audio data includes an analog signal that includes the audio data. In another instance, the indication of the audio data includes a digital signal encoded with the audio data. In yet another instance, the indication of the audio data includes text indicative of the message. 
     Analytics service  68 C may determine whether to output a representation of the message included in the audio data based on one or more rules. The rules may be pre-programmed or generated using machine learning. In the example of  FIG. 4 , the rules are stored in models  74 B. Models  74 B include, in some example, separate models for individual workers, a population of workers, a particular environment, a type of PPE, a type task, or combinations thereof. Analytics service  68 C may update models  74 B as PPEMS  6  receives additional data, such as data received from safety equipment  62 , equipment  30 , or both. 
     In some examples, analytics service  68 C determines a risk level for the worker based on one or more models  74 B. For example, analytics service  68 C may apply one or more models  74 B to event data  74 A (e.g., sensor data), worker data  74 C, task data  74 D, or a combination thereof to determine a risk level for worker  10 A. 
     Analytics service  68 C may determine an urgency level for the message based on one or more models  74 B. For example, analytics service  68 C may apply one or more models  74 B to audio characteristics for the audio data, content of the message, metadata for the message, or a combination thereof. 
     In some scenarios, analytics service  68 C determines whether to output a representation of the message based at least in part on the risk level for worker  10 A, an urgency level of the received message, or both. For example, analytics service  68 C may determine whether to output a visual representation of the message based on the risk level and/or urgency level. In another example, analytics service  68 C determines whether to output an audible representation of the message based on the risk level and/or urgency level. In some instances, analytics service  68 C determines whether to output a visual representation of the message, an audible representation of the message, both an audible representation and a visual representation of the message, or none at all. 
     Responsive to determining to output a visual representation of the message, analytics service  68 C may output data causing display device  34 A of PPE  13 A to output the visual representation of the message by outputting a GUI. The GUI may include that text or an image (e.g., icon, emoji, GIF, etc.) indicative of the message. Similarly, analytics service  68 C may output data causing speakers  32 A of PPE  13 A to output an audible representation of the message. 
       FIG. 5  is a flowchart illustrating example operations of an example computing system, in accordance with various techniques of this disclosure.  FIG. 5  is described below in the context of computing device  38 B of PPE  13 B worn by worker  10 B of  FIG. 1 . While described in the context of computing device  38 B of PPE  13 B, other computing devices (e.g., computing device  38 A of  FIG. 1 ; PPEMS  6  of  FIGS. 1, 4 ; computing devices  16 ,  18  of  FIG. 1 ; computing device  300  of  FIG. 3 ) may perform all or a subset of the functionality described. 
     Computing device  38 B receives an indication of audio data that includes a message ( 502 ). Computing device  38 B may receive the indication of the audio data from another computing device, such as a computing device  38 A associated with another worker  10 A, PPEMS  6 , computing devices  16 ,  18 , or any other computing device. The indication of the audio data may include an analog signal that includes the audio data. The indication of the audio data may include a digital signal encoded with the audio data. In some instances, the indication of the audio data includes text indicative of the message. 
     In some examples, computing device  38 B determines a risk level for worker  10 B ( 504 ). In some examples, computing device  38 B determines the risk level based on task data associated with a task performed by worker  10 B, worker data associated with worker  10 B, sensor data (e.g., environmental data generated by one or more environmental sensors and/or physiological data generated by one or more physiological sensors associated with worker  10 B), or a combination thereof. In some examples, computing device  38 B determines the risk level by applying one or more models (e.g., generated by machine learning) to the task data, worker data, and/or sensor data. 
     Computing device  38 B may determine whether to output a visual representation of the message ( 506 ) based at least in part on the risk level for worker  10 B. For example, computing device  38 B may compare the risk level to a threshold risk level. In some instances, computing device  38 B determines whether to output the visual representation of the message based on the risk level for worker  10 B and an urgency level of the message. 
     Responsive to determining to output the visual representation of the message (“YES” branch of  506 ), in some examples, computing device  38 B outputs a visual representation of the message ( 508 ). For example, computing device  38 B may output the visual representation of the message by outputting a GUI via a display device of PPE  13 B. The visual representation of the message may include text, an image (e.g., an icon, emoji, map, GIF, etc.), or both. 
     In some examples, computing device  38 B refrains from outputting a visual representation of the message ( 510 ) in response to determining not to output the visual representation of the message (“NO” branch of  510 ). In some examples, computing device  38 B may output an audible representation of the message rather than a visual representation of the message. As another example, computing device  38 B may refrain from outputting a visual or audible representation of the message. 
     The following numbered examples may illustrate one or more aspects of the disclosure: 
     Example 1. A method comprising: receiving, by a computing device, an indication of audio data from a second worker, the audio data including a message; determining, by the computing device, a risk level for a first worker utilizing an article of personal protective equipment; determining, by the computing device, based at least in part on the risk level, whether to display a visual representation of the message; and responsive to determining to display the visual representation of the message, outputting, by the computing device, for display by a display device of the article of PPE, the visual representation of the message. 
     Example 2: The method of example 1, wherein determining the risk level is based at least in part on one or more physiological conditions of the worker. 
     Example 3: The method of any one of examples 1-2, wherein determining the risk level is further based at least in part on task data for a task associated with the first worker, wherein the task data includes at least one of: a location of the task, a complexity of the task, a severity of harm to the first worker, a likelihood of harm to the first worker, a type of the task, or a duration of the task. 
     Example 4: The method of any one of examples 1-3, wherein the visual representation comprises one or more of text or an image. 
     Example 5: The method of any one of examples 1-4, further comprising: determining, by the computing device, an urgency level of the message; and determining, by the computing device, whether to display the visual representation of the message further based on an urgency level of the message. 
     Example 6: The method of example 5, wherein determining the urgency level is based on one or more audio characteristics of the audio data. 
     Example 7: The method of any one of examples 5-6, wherein determining the urgency level is based on content of the message. 
     Example 8. The method of any one of examples 5-7, wherein determining the urgency level is based on metadata for the message. 
     Example 9. The method of any one of examples 1-8, further comprising: determining, by the computing device, whether to output an audible representation of the message. 
     Example 10. The method of any one of examples 1-9, wherein the message indicates a task associated with another worker, the method further comprising: outputting, by the computing device, for display by the display device, data associated with the message, wherein the data associated with the message includes one or more of: a map indicating a location of the task; one or more articles of PPE associated with the task; or one or more articles of equipment associated with the task. 
     Example 11. The method of any one of examples 1-10, wherein the message is a first message, the method further comprising: receiving, by the computing device, a second message from an article of equipment within a work environment that includes the first worker; and determining, by the computing device whether to output a representation of the second message. 
     Although the methods and systems of the present disclosure have been described with reference to specific exemplary embodiments, those of ordinary skill in the art will readily appreciate that changes and modifications may be made thereto without departing from the spirit and scope of the present disclosure. 
     In the present detailed description of the preferred embodiments, reference is made to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced. The illustrated embodiments are not intended to be exhaustive of all embodiments according to the invention. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims. 
     Unless otherwise indicated, all numbers expressing feature sizes, amounts, and physical properties used in the specification and claims are to be understood as being modified in all instances by the term “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the foregoing specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings disclosed herein. 
     As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” encompass embodiments having plural referents, unless the content clearly dictates otherwise. As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise. 
     Spatially related terms, including but not limited to, “proximate,” “distal,” “lower,” “upper,” “beneath,” “below,” “above,” and “on top,” if used herein, are utilized for ease of description to describe spatial relationships of an element(s) to another. Such spatially related terms encompass different orientations of the device in use or operation in addition to the particular orientations depicted in the figures and described herein. For example, if an object depicted in the figures is turned over or flipped over, portions previously described as below or beneath other elements would then be above or on top of those other elements. 
     As used herein, when an element, component, or layer for example is described as forming a “coincident interface” with, or being “on,” “connected to,” “coupled with,” “stacked on” or “in contact with” another element, component, or layer, it can be directly on, directly connected to, directly coupled with, directly stacked on, in direct contact with, or intervening elements, components or layers may be on, connected, coupled or in contact with the particular element, component, or layer, for example. When an element, component, or layer for example is referred to as being “directly on,” “directly connected to,” “directly coupled with,” or “directly in contact with” another element, there are no intervening elements, components or layers for example. The techniques of this disclosure may be implemented in a wide variety of computer devices, such as servers, laptop computers, desktop computers, notebook computers, tablet computers, hand-held computers, smart phones, and the like. Any components, modules or units have been described to emphasize functional aspects and do not necessarily require realization by different hardware units. The techniques described herein may also be implemented in hardware, software, firmware, or any combination thereof. Any features described as modules, units or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. In some cases, various features may be implemented as an integrated circuit device, such as an integrated circuit chip or chipset. Additionally, although a number of distinct modules have been described throughout this description, many of which perform unique functions, all the functions of all of the modules may be combined into a single module, or even split into further additional modules. The modules described herein are only exemplary and have been described as such for better ease of understanding. 
     If implemented in software, the techniques may be realized at least in part by a computer-readable medium comprising instructions that, when executed in a processor, performs one or more of the methods described above. The computer-readable medium may comprise a tangible computer-readable storage medium and may form part of a computer program product, which may include packaging materials. The computer-readable storage medium may comprise random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The computer-readable storage medium may also comprise a non-volatile storage device, such as a hard-disk, magnetic tape, a compact disk (CD), digital versatile disk (DVD), Blu-ray disk, holographic data storage media, or other non-volatile storage device. 
     The term “processor,” as used herein may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated software modules or hardware modules configured for performing the techniques of this disclosure. Even if implemented in software, the techniques may use hardware such as a processor to execute the software, and a memory to store the software. In any such cases, the computers described herein may define a specific machine that is capable of executing the specific functions described herein. Also, the techniques could be fully implemented in one or more circuits or logic elements, which could also be considered a processor.