3 research outputs found
The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection
Deep learning technology has been widely used in edge computing. However,
pandemics like covid-19 require deep learning capabilities at mobile devices
(detect respiratory rate using mobile robotics or conduct CT scan using a
mobile scanner), which are severely constrained by the limited storage and
computation resources at the device level. To solve this problem, we propose a
three-tier architecture, including robot layers, edge layers, and cloud layers.
We adopt this architecture to design a non-contact respiratory monitoring
system to break down respiratory rate calculation tasks. Experimental results
of respiratory rate monitoring show that the proposed approach in this paper
significantly outperforms other approaches. It is supported by computation time
costs with 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution
operation, similarity calculation, processing one-minute length respiratory
signals, respectively. And the computation time costs of our three-tier
architecture are less than that of edge+cloud architecture and cloud
architecture. Moreover, we use our three-tire architecture for CT image
diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19
proves that our three-tire architecture is useful for resolving tasks on deep
learning networks by edge equipment. There are broad application scenarios in
smart hospitals in the future
Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming
The rapid advancements in artificial intelligence (AI) have led to a growing
trend of human-AI teaming (HAT) in various fields. As machines continue to
evolve from mere automation to a state of autonomy, they are increasingly
exhibiting unexpected behaviors and human-like cognitive/intelligent
capabilities, including situation awareness (SA). This shift has the potential
to enhance the performance of mixed human-AI teams over all-human teams,
underscoring the need for a better understanding of the dynamic SA interactions
between humans and machines. To this end, we provide a review of leading SA
theoretical models and a new framework for SA in the HAT context based on the
key features and processes of HAT. The Agent Teaming Situation Awareness (ATSA)
framework unifies human and AI behavior, and involves bidirectional, and
dynamic interaction. The framework is based on the individual and team SA
models and elaborates on the cognitive mechanisms for modeling HAT. Similar
perceptual cycles are adopted for the individual (including both human and AI)
and the whole team, which is tailored to the unique requirements of the HAT
context. ATSA emphasizes cohesive and effective HAT through structures and
components, including teaming understanding, teaming control, and the world, as
well as adhesive transactive part. We further propose several future research
directions to expand on the distinctive contributions of ATSA and address the
specific and pressing next steps.Comment: 52 pages,5 figures, 1 tabl