“Spatial Sensing: Augmenting Human Understanding in Data-Driven Exploration” by Humml
Conference:
Type(s):
Title:
- Spatial Sensing: Augmenting Human Understanding in Data-Driven Exploration
Session/Category Title:
- Seeing Space and Time
Presenter(s)/Author(s):
Moderator(s):
Abstract:
Machine-Guided Spatial Sensing is a novel measurement technique that combines augmented reality (AR), active learning, and human-in-the-loop interaction to measure environmental fields with high accuracy and efficiency. This system employs a head-mounted display (HMD) and a handheld sensor to capture various physical quantities, such as flow fields and gas concentrations, in real-time. A central data model processes the collected measurements continuously and updates predictions of the environmental field. Using active learning, the system identifies regions of high uncertainty and guides the operator to optimal sampling locations through intuitive AR visualizations. This closed-loop framework effectively transfers the sampling expertise from the operator to the machine learning algorithm, enabling efficient and accurate field estimation. Experimental evaluations demonstrate that the proposed method achieves high accuracy and reduces measurement times significantly compared to traditional sampling techniques. The system’s flexibility allows for integration with various environmental sensors, making it suitable for applications in engineering, scientific research, and environmental protection. By leveraging real-time data analysis and human-machine collaboration, Machine-Guided Spatial Sensing provides a robust, user-friendly solution for complex spatial measurement challenges. Future research will focus on enhancing sensor fusion and adapting the system to dynamic environmental conditions. These promising results indicate that the approach reduces setup complexity, lowers costs, and enhances data reliability across diverse environments.
References:
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