Presentation
HDFusion: Hierarchical Data Fusion for Robust Deep Tissue Sensing
DescriptionNon-invasive sensing of deep tissue, a key task in many medical cyber-physical systems, is inherently challenged by low signal-to-noise ratio (SNR), and unpredictable anatomical and physiological tissue dynamics, which render a particular sensor design sub-optimal. The use of multiple sensors can conceptually enable the system to operate more robustly under such dynamics, assuming that the data acquired by different sensors can be adaptively integrated to form a coherent view of the tissue.
In this paper, we present an algorithm for data fusion at several levels of information abstraction, raw data, feature and decision levels, to meet this need. We validate the proposed technique via non-invasive fetal heart rate tracking using in-vivo data collected in gold-standard pregnant ewe experiments. The root-mean-squared error of our three-level hierarchical data fusion compared to a single-level and two-level fusion improved by over 31% and 19%, respectively. This underscores the robustness of our approach in overcoming deep tissue sensing challenges.
In this paper, we present an algorithm for data fusion at several levels of information abstraction, raw data, feature and decision levels, to meet this need. We validate the proposed technique via non-invasive fetal heart rate tracking using in-vivo data collected in gold-standard pregnant ewe experiments. The root-mean-squared error of our three-level hierarchical data fusion compared to a single-level and two-level fusion improved by over 31% and 19%, respectively. This underscores the robustness of our approach in overcoming deep tissue sensing challenges.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security