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IEEE Southeastcon

作者： T. Warren de Wit Vineetha Menon Thomas Davis Dept. of Computer Science University of Alabama Huntsville Huntsville USA Human Systems Integration Division DEVCOM Data and Analysis Center Huntsville USA

Design and deployment of brain-computer interface enabled assistive systems poses many practical questions, including the decision of whether to use a pretrained model or to customize a model to each unique end-user. In this work, we apply this question to the application domain of a passive brain- controlled drone for use in disaster relief and hostage rescue situations. A six-class intent (e.g. EEG) recognition experiment is performed with 42 subjects. This pilot study explores and evaluates the effects of subject-specific (“customized”) versus subject- independent (“uncustomized”) modeling approaches. Based on experimental validation, we present our discussions on the observed pros and cons of each training strategy. In this study, it was noted that the uncustomized training approach had the best target detection performance with a reduction in variance. Additionally, its deployment readiness attribute make it a more relevant and feasible option for our intended use case.

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EEG Context Fusion for AI-Based Object Detection and Drone Navigation in Situationally Aware Brain-Computer Interfaces

EEG Context Fusion for AI-Based Object Detection and Drone N...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： T. Warren de Wit Vineetha Menon Nathan Tenhundfeld Thomas Davis Dept. of Computer Science University of Alabama Huntsville Huntsville USA Dept. of Psychology University of Alabama Huntsville Huntsville USA Human Systems Integration Division DEVCOM Data and Analysis Center Huntsville USA

We are interested in the utility that artificially intelligent mobile systems such as drones offer to personnel in fast-paced situations such as hostage rescue and disaster relief for real-time situational awareness. Ideally, these assistive systems place no additional cognitive or physical burden on their user; rather, they should respond to the user’s intent with minimal physical and cognitive burden. To this end, brain-computer interface (BCI) and in particular electroencephalography (EEG) offers a novel way to capture intent. EEG-based drone control has been explored, but typically relies on the domain of motor imagery (MI), which still requires somewhat-manual drone piloting for micro directional movement, adding cognitive burden. We propose leveraging the robust computer-vision based AI that exists on modern drones to use objects as waypoints and fly mostly autonomously, with EEG in an object recognition paradigm for passively selecting the waypoint. While existing techniques for EEG object recognition-including steady-state visually evoked potentials (SSVEP), rapid serial visual presentation (RSVP), and eye tracking - rely on “marked” input or a controlled environment, we need one that will work on passive objects. In this work, we propose merging available scene imagery in a vision network with deep-learning based EEG processing to achieve passive intent recognition. The proposed context-scene fusion for a situational awareness paradigm presents accuracy in the same ranges as existing object recognition without requiring SSVEP, RSVP, eye tracking, or MI techniques and serves as proof-of-concept for this approach to real-world BCI applications.

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A Case Study of human-AI Interactions Using Transparent AI-Driven Autonomous systems for Improved human-AI Trust Factors 3

A Case Study of Human-AI Interactions Using Transparent AI-D...

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3rd IEEE International Conference on human-Machine systems, ICHMS 2022

作者： Pham, Daniel Menon, Vineetha Tenhundfeld, Nathan Weger, Kristin Mesmer, Bryan Gholston, Sampson Davis, Thomas The University of Alabama in Huntsville Department of Computer Science HuntsvilleAL United States The University of Alabama in Huntsville Department of Psychology HuntsvilleAL United States The University of Alabama in Huntsville Department of Iseem HuntsvilleAL United States Human Systems Integration Division Devcom Data and Analysis Center HuntsvilleAL United States

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

human interactions with Artificial Intelligence (AI) have become a ubiquitous phenomenon in our society. It plays a major role in defense and espionage in the context of AI-based autonomous systems such as drones and ground robots that are sent to the front lines to navigate terrains, map the surroundings, and identify adversaries in the environment. In these types of scenarios, hostages may be spread out over a larger geographical area. Reliance on ground vision alone may not only be obscured and insufficient but also can expose soldiers or human rescuers to danger. Since time is of great essence, such an intense situation calls for decisions to be made swiftly and efficiently as the lives of hostages are at stake. This makes such a scenario ideal to evaluate the effectiveness of an AI-driven drone autonomous system for combat search and rescue (CSAR) missions. Here the focus is to gather information about the operational environment (detect hostages and other targets-of-interest) in a short amount of time that spans a varying geographic terrain. This allows for targeted rescue operations with AI-informed decision-making outcomes to ensure that the rescuers at ground level can be directed along the most optimal path, and casualties will be either minimized or entirely averted. In this work, we study the feasibility of our drone-based AI target detection autonomous system to evaluate the classification accuracy of multi-view targets-of-interest in the CSAR environment. The overarching goal of this study is to understand the role of AI-driven autonomous systems as an assistive support tool for efficient, swift and accurate decision making, navigating a complex and ever-changing environment, and identification of targets. © 2022 IEEE.

关键词： Object detection

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Lower-Extremity Kinematics and Spatiotemporal Differences Between Normal Walking and Walking While Maintaining Weapon Aim

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Augmented human Research 2024年第1期9卷 1-10页

作者： Talarico, Maria K. Sperlein, Jennifer N. Yang, Jingzhen Chaudhari, Ajit Oñate, James A. Weapons Branch Human Systems Integration Division (HSID) Combat Capabilities Development Command (DEVCOM) - Analysis Center (DAC) Aberdeen Proving Ground USA Center for Injury Research & Policy Nationwide Children’s Hospital Columbus USA College of Public Health The Ohio State University Columbus USA Sports Medicine Research Institute The Ohio State University Columbus USA School of Health and Rehabilitation Sciences The Ohio State University Columbus USA Division of Athletic Training The Ohio State University Columbus USA

Although exoskeletons for tactical athletes are potentially beneficial tools to increase survivability and enhance operational capabilities, they often do not account for variations from basic movements. Tactical movement patterns must be investigated to best guide device design and mitigate restriction of the user’s abilities to execute duties. The purpose of this study was to identify if biomechanical patterns are difference between walking while maintaining weapon aim (i.e., ‘shoot on the move’, SM) and normal walking. Twenty-two male active-duty Army Soldiers performed normal walking and SM under three speed conditions: (1) self-selected slow, (2) standard (1.12 m/s), and (3) self-selected fast. Lower extremity kinematics and spatiotemporal parameters were collected. Unadjusted and adjusted models were performed to determine the effects of walking and speed conditions on dependent variables. Individuals exhibited larger lower extremity flexion angles during SM than during normal walking (p < 0.01). Smaller strides were observed during SM than during normal walking (p < 0.01). No difference in time spent in double limb support were observed between walking tasks (p = 0.97). Tactical athletes exhibit different movement patterns during SM compared to normal walking. Lower extremities are more flexed, and strides are shorter during SM to minimize vertical movement and increase stability, suggesting that occupationally relevant movements may not directly translate to basic movements. Information on occupationally relevant movements should be accessible to exoskeleton designers to build optimum control systems that will aid and not hinder tactical athletes in their roles to serve and protect.

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human Factors in Future Vertical Lift, the U.S. Army’s Next Generation Rotorcraft

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Proceedings of the human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 149 - 153页

作者： Katie Ernst Michael Wilson David Durbin Roth Roth Laura Militello Robert Copeland Applied Decision Science LLC USA United States Army Aeromedical Research Laboratory USA United States Army Data and Analysis Center Human Systems Integration Division USA Emilie Cognitive Engineering USA Applied Decision Science LLC USA Kevin Oden Lockheed Martin Rotary and Mission Systems USA United States Army Futures Command Aviation and Missile Center USA

The United States Army is developing and procuring their next generation of rotorcraft, called the Future Vertical Lift (FVL) program. FVL rotorcraft will fly faster and incorporate new work requirements such as command of unmanned systems and optionally piloted variants. Successful design and implementation of human automation teaming will be paramount to the mission success and safety of FVL systems, presenting both challenges and opportunities for human factors personnel supporting FVL development. The purpose of this panel is to highlight the range of human factors challenges within the FVL program. Panelists are drawn from both government and industry, and represent a diverse set of perspectives including user, researcher, and practitioner. The session will include time for panelists to introduce their work and challenges that they are addressing within the FVL program as well as interact with audience members.

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human FACTORS IN FUTURE VERTICAL LIFT, THE U.S. ARMY’S NEXT GENERATION ROTORCRAFT 64th

HUMAN FACTORS IN FUTURE VERTICAL LIFT, THE U.S. ARMY’S NEXT...

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64th International Annual Meeting of the human Factors and Ergonomics Society, HFES 2020

作者： Ernst, Katie Wilson, Michael Durbin, David Roth, Emilie Militello, Laura Oden, Kevin Copeland, Robert Applied Decision Science LLC United States United States Army Aeromedical Research Laboratory United States United States Army Data and Analysis Center Human Systems Integration Division United States Roth Cognitive Engineering United States Applied Decision Science LLC United States Lockheed Martin Rotary and Mission Systems United States United States Army Futures Command Aviation and Missile Center United States

关键词： Ergonomics

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Influence of human engineering on manning levels and human performance on ships

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NAVAL ENGINEERS JOURNAL 1997年第5期109卷 67-76页

作者： Anderson, DE Oberman, FR Malone, TB Baker, CC David E. Anderson:has a bachelor of science degree in environmental engineering from Florida Technological University and a master's degree in environmental engineering from the University of Central Florida. He is a graduate of the Naval Sea Systems Command's Engineer-In-Training (EIT) Program. Mr. Anderson was instrumental in introducing the collective protection system (CPS) in the U.S. Navy developing the initial forward-fit package for the USS Gunston Hall and the engineering change proposal (ECP) for the USS Wasp. In 1990 he joined the Human Systems Integration (HSI) Division (SEA 55W5) where he was task leader for auxiliary ships. He is currently the HSI manager for the future technology variant of the SeaLift ship and the future carrier. Association of Scientists and Engineers 33rdAnnual Technical Symposium 26 April 1996. Fred R. Oberman:has B.A. and M.A. degrees from the University of Chicago and Loyola University (experimental psychology) and an M.S. degree in industrial engineering and operations research from Virginia Polytechnic Institute (VPI). He has more than 30 years experience in HSI management planning research analysis design and testing in government and private sector positions. He is currently responsible for NavSea HSI generic research and tool development efforts. He is responsible for Human Engineering Specifications and Standards (Commercial Hypertext) and is the NavSea 03D7 representative on HSI in Performance Specifications and for integration of HSI within Integrated Logistic Support (ILS). He has served as DoD HSI SubTAG chair and member of the Simulation and Modeling Test and Evaluation Display and Control Systems Human Computer Interaction Specifications and Standards and Systems Design Sub Tags as a member of the Society of Naval Architects and Marine Engineers (SNAME) Systems Safety Panel and a member of NATO RSG 14 on man-machine analysis. Thomas B. Malone: CHFEP received a Ph.D. in experimental psychology from Fordham University in 1964. He is president of Carlow

The objectives of human Engineering (HE) are generally viewed as increasing human performance, reducing human error, enhancing personnel and equipment safety, and reducing training and related personnel costs. There are other benefits that are thoroughly consistent with the direction of the Navy of the future, chief among these is reduction of required numbers of personnel to operate and maintain Navy ships. The Naval Research Advisory Committee (NRAC) report on Man-Machine Technology in the Navy estimated that one of the benefits from increased application of man-machine technology to Navy ship design is personnel reduction as well as improving system availability, effectiveness, and safety The objective of this paper is to discuss aspects of the human engineering design of ships and systems that affect manning requirements, and impact human-performance and safety The paper will also discuss how the application of human engineering leads to improved performance, and crew safety, and reduced workload, all of which influence manning levels. Finally, the paper presents a discussion of tools and case studies of good human engineering design practices which reduce manning.

关键词： human engineering

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