Purdue Research Foundation

Xun Qian

Karthik Ramani

Tianyi Wang

Yuanzhi Cao

Fengming He

Gaoping Huang

Alexander J Quinn

A machine task tutorial system is disclosed that utilizes augmented reality to enable an expert user to record a tutorial for a machine task that can be learned by different trainee users in an adaptive manner. The machine task tutorial system advantageously utilizes an adaptation model that focuses on spatial and bodily visual presence for machine task tutoring. The machine task tutorial system advantageously enables adaptive tutoring in the recorded-tutorial environment based on machine state and user activity recognition. The machine task tutorial system advantageously utilizes AR to provide tutorial recording, adaptive visualization, and state recognition. In this way, the machine task tutorial system supports more effective apprenticeship and training for machine tasks in workshops or factories.

Adaptive tutoring system for machine tasks in augmented reality

An augmented reality (AR) application authoring system is disclosed. The AR application authoring system enables the real-time creation of freehand interactive AR applications with freehand inputs. The AR application authoring system enables intuitive authoring of customized freehand gesture inputs through embodied demonstration while using the surrounding environment as a contextual reference. A visual programming interface is provided with which users can define freehand interactions by matching the freehand gestures with reactions of virtual AR assets. Thus, users can create personalized freehand interactions through simple trigger-action programming logic. Further, with the support of a real-time hand gesture detection algorithm, users can seamlessly test and iterate on the authored AR experience.

System and method for authoring freehand interactive augmented reality applications

A system and method for authoring and implementing context-aware applications (CAPs) are disclosed. The system and method enables users to record their daily activities and then build and deploy customized CAPs onto augmented reality platforms in which automated actions are performed in response to user-defined human actions. The system and method utilizes an integrated augmented reality platform composed of multiple camera systems, which allows for non-intrusive recording of end-users' activities and context detection while authoring and implementing CAPs. The system and method provides an augmented reality authoring interface for browsing, selecting, and editing recorded activities, and creating flexible CAPs through spatial interaction and visual programming.

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West Lafayette, IN, United States of America

Xun Qian