4 research outputs found
MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks
No full textThis paper presents MOCAS, a multimodal dataset dedicated for human cognitive
workload (CWL) assessment. In contrast to existing datasets based on virtual
game stimuli, the data in MOCAS was collected from realistic closed-circuit
television (CCTV) monitoring tasks, increasing its applicability for real-world
scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam
were utilized to collect physiological signals and behavioral features from 21
human subjects. After each task, participants reported their CWL by completing
the NASA-Task Load Index (NASA-TLX) and Instantaneous Self-Assessment (ISA).
Personal background (e.g., personality and prior experience) was surveyed using
demographic and Big Five Factor personality questionnaires, and two domains of
subjective emotion information (i.e., arousal and valence) were obtained from
the Self-Assessment Manikin (SAM), which could serve as potential indicators
for improving CWL recognition performance. Technical validation was conducted
to demonstrate that target CWL levels were elicited during simultaneous CCTV
monitoring tasks; its results support the high quality of the collected
multimodal signals.Comment: 18 pages, 10 figures, 9 tables, Accepted IEEE transaction Affective
Computing
ARTEMIS: AI-driven Robotic Triage Labeling and Emergency Medical Information System
No full textMass casualty incidents (MCIs) pose a significant challenge to emergency
medical services by overwhelming available resources and personnel. Effective
victim assessment is the key to minimizing casualties during such a crisis. We
introduce ARTEMIS, an AI-driven Robotic Triage Labeling and Emergency Medical
Information System, to aid first responders in MCI events. It leverages speech
processing, natural language processing, and deep learning to help with acuity
classification. This is deployed on a quadruped that performs victim
localization and preliminary injury severity assessment. First responders
access victim information through a Graphical User Interface that is updated in
real-time. To validate our proposed algorithmic triage protocol, we used the
Unitree Go1 quadruped. The robot identifies humans, interacts with them, gets
vitals and information, and assigns an acuity label. Simulations of an MCI in
software and a controlled environment outdoors were conducted. The system
achieved a triage-level classification precision of over 74% on average and 99%
for the most critical victims, i.e. level 1 acuity, outperforming
state-of-the-art deep learning-based triage labeling systems. In this paper, we
showcase the potential of human-robot interaction in assisting medical
personnel in MCI events
MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks
No full textThis MOCAS is a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin, which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.This material is based upon work supported by the National Science Foundation under Grant No. IIS-1846221. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation
UPPLIED: UAV Path Planning for Inspection through Demonstration
No full textIn this paper, a new demonstration-based path-planning framework for the
visual inspection of large structures using UAVs is proposed. We introduce
UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes
a demonstrated trajectory to generate a new trajectory to inspect other
structures of the same kind. The demonstrated trajectory can inspect specific
regions of the structure and the new trajectory generated by UPPLIED inspects
similar regions in the other structure. The proposed method generates
inspection points from the demonstrated trajectory and uses standardization to
translate those inspection points to inspect the new structure. Finally, the
position of these inspection points is optimized to refine their view. Numerous
experiments were conducted with various structures and the proposed framework
was able to generate inspection trajectories of various kinds for different
structures based on the demonstration. The trajectories generated match with
the demonstrated trajectory in geometry and at the same time inspect the
regions inspected by the demonstration trajectory with minimum deviation. The
experimental video of the work can be found at https://youtu.be/YqPx-cLkv04.Comment: Accepted for publication in IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2023), Detroit, Michigan, US
