IEEE 1589:2020 pdf free download – IEEE Standard for Augmented Reality Learning Experience Model
activity: The execution of a planned workflow following a specific process，leading typically frombeginning to end, regardless of whether the anticipated learning outcome is achieved.
augmentation:The digital representation of effector outputs that serve to stimulate the sensory experienceof the user, including output to visual, audio, haptic, and other modalities.
augmentation primitive: The types of annotations available are defined in the augmented reality (AR)training system. They include，but are not limited to， audio，video，images，animations，labels，andvibrotactile patterns.
augmented reality (AR): Human perception is enhanced with additional computer-generated sensorialinput to create a new user experience，including，but not restricted to，enhancing human vision bycombining natural with digital offers.
augmented reality (AR) tracking subsystem: A component of the augmented reality (AR) trainingsystem that detects position markers and anchors， typically (but not limited to) using computer vision todetermine the location of markers, image targets, or spatial anchors in a room scan.
augmented reality (AR) training system: An augmented reality (AR) training system consists of a singlesoftware application or a set of software applications, and connected delivery hardware (such as a head-mounted display，HMD), that allow trainees to practice and perform predefined learning activities in agiven workplace. It may also include authoring functionality to facilitate the creation of new learningactivities.
detectable: Entities that link to fiducial markers，target feature models,or other sensor state propertiesproviding input to computer vision and other sensor processing systems. They have a unique identity andlink to data enabling their tracking with the help of the sensor processing system referenced.
experience API (xAPI): A learning analytics application programming interface(APl)，standardizingcommunication with a learning record store for logging learner performance.Any augmented reality (AR)training system may optionally use the xAPl to keep andor retrieve records of the users’ learning activities.IfxAPl logging is supported, xAPl statement querics may be used in the if-rules to control the activity.
learning activity: An activity that motivates the development of competence (i.e., knowledge,skills,abilities,and other characteristics).
learning experience: The cognitive and sensory-motoric effect on the user from performing a learningactivity in a particular workplace.
predicate: Reusable instructional augmentations, configuring a specific augmentation primitive for its usein activities. The set of predicates defined in a workplace model is a domain-specific language forinstruction in the workplace under consideration, typically including all required verbs of handling andmovement and their visual overlay animations signifying them.
sensor: A device that detects or measures physical characteristics and communicates the data generateddigitally, such as a heart rate sensor, a gyroscope, or a nondescript Internet-of-Things sensor that signals,for example,whether a specific button has been pressed during operation of a manufacturing machine.
trigger: A mechanism firing an event on completion of an action step that allows the statements in the exitsection of the action step to be rcleased.
4. Learning in activities and workplaces
Descriptions of learning experiences consist of two types of data.Learning activities contain task-dependent descriptions of how to interact with real and virtual elements in the sequence of actions.Workplace environment and context descriptions consist of data concerning which real and virtual elementsexist and are featured within the experience.
The definition of an activity mixture for learning by experience includes orchestration of user interactionacross multiple devices. Furthermore, it integrates the tracking of and responses to user interaction acrossdevices and sensors. Optionally, if-rules may be defined and evaluated as part of the activity to test for userachievements or action outcomes using data from one or more software andor hardware sensors.
The activity modeling language (activityML) in this standard is a domain-specific language used to describethe activity mixture. The workplace modeling language (workplaceML) in this standard complements theactivity representation language to realize interoperability of applications interpreting activityML. It is adomain-specific language for describing the tangibles, configurables, and basic triggers of a workplace (seeFigure l).