Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks
This addresses the problem of limited AR acceptance in industry due to lack of established methods, offering a practical solution for workers in complex manual tasks, though it appears incremental as it combines existing technologies like AR and neural networks.
The paper tackled the challenge of integrating Augmented Reality (AR) into industrial processes by proposing an AR-based human assistance system that uses deep neural networks for object and action detection to assist workers in complex manual tasks. The result showed a significant reduction in time to task completion and a decrease in error rate in user studies.
As a result of an increasingly automatized and digitized industry, processes are becoming more complex. Augmented Reality has shown considerable potential in assisting workers with complex tasks by enhancing user understanding and experience with spatial information. However, the acceptance and integration of AR into industrial processes is still limited due to the lack of established methods and tedious integration efforts. Meanwhile, deep neural networks have achieved remarkable results in computer vision tasks and bear great prospects to enrich Augmented Reality applications . In this paper, we propose an Augmented-Reality-based human assistance system to assist workers in complex manual tasks where we incorporate deep neural networks for computer vision tasks. More specifically, we combine Augmented Reality with object and action detectors to make workflows more intuitive and flexible. To evaluate our system in terms of user acceptance and efficiency, we conducted several user studies. We found a significant reduction in time to task completion in untrained workers and a decrease in error rate. Furthermore, we investigated the users learning curve with our assistance system.