Person:

Howard, Ayanna M.

Permanent Link

https://hdl.handle.net/1853/72195

Associated Organization(s)

Organizational Unit

School of Interactive Computing

Organizational Unit

School of Electrical and Computer Engineering

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Publication Search Results

Now showing 1 - 9 of 9

Validation of Accuracy of the Super Pop VR™ Kinematic Assessment Methodology Using Markerless Versus Marker-Based Motion Capture Systems

(Georgia Institute of Technology, 2016) García-Vergara, Sergio ; Robinette, Paul ; Chen, Yu-Ping ; Howard, Ayanna M.

Therapists and clinicians have been combining virtual reality (VR) systems for rehabilitation purposes with motion capture systems to accurately keep track of the users' movements and better analyze their kinematic performance. The current state-of-the-art motion capture technology is limited to the clinical setting due to its cost, the necessity for a controlled environment, requirement of additional equipment, among others. Given the benefits of home-based rehabilitation protocols, more portable and cost-effective technology is being coupled with the VR systems. In this work, we focus on validating the accuracy of the Kinect™ camera from Microsoft. We compare its performance to a current state-of-the-art motion capture system. Namely, we 1) analyze the difference between the outcome metrics computed with data collected with the Kinect™ camera and the outcome metrics computed with data collected with the motion capture system, and 2) compare the spatial trajectories generated by both systems for the hand, elbow, and shoulder joints. Data were collected from ten able-bodied adults to quantify these comparisons. In general, results from both analyzes support the validity and feasibility of using the Kinect™ camera for home-based rehabilitation purposes.
The Effect of Robot Performance on Human-‐Robot Trust in Time-‐Critical Situations

( 2015-01) Robinette, Paul ; Wagner, Alan R. ; Howard, Ayanna M.

We vary the ability of robots to mitigate a participant’s risk in a navigation guidance task to determine the effect this has on the participant’s trust in the robot in a second round. A significant loss of trust was found after a single robot failure.
Assessment of Robot Guidance Modalities Conveying Instructions to Humans in Emergency Situations

(Georgia Institute of Technology, 2014-08) Robinette, Paul ; Wagner, Alan R. ; Howard, Ayanna M.

Motivated by the desire to mitigate human casualties in emergency situations, this paper explores various guidance modalities provided by a robotic platform for instructing humans to safely evacuate during an emergency. We focus on physical modifications of the robot, which enables visual guidance instructions, since auditory guidance instructions pose potential problems in a noisy emergency environment. Robotic platforms can convey visual guidance instructions through motion, static signs, dynamic signs, and gestures using single or multiple arms. In this paper, we discuss the different guidance modalities instantiated by different physical platform constructs and assess the abilities of the platforms to convey information related to evacuation. Human-robot interaction studies with 192 participants show that participants were able to understand the information conveyed by the various robotic constructs in 75.8% of cases when using dynamic signs with multi-arm gestures, as opposed to 18.0% when using static signs for visual guidance. Of interest to note is that dynamic signs had equivalent performance to single-arm gestures overall but drastically different performances at the two distance levels tested. Based on these studies, we conclude that dynamic signs are important for information conveyance when the robot is in close proximity to the human but multi-arm gestures are necessary when information must be conveyed across a greater distance.
Modeling Human-Robot Trust in Emergencies

(Georgia Institute of Technology, 2014-03) Robinette, Paul ; Wagner, Alan R. ; Howard, Ayanna M.

Modeling human trust decisions is a notoriously difficult problem. We focus on decisions where a victim must decide whether to trust a robot in an emergency situation and outline the necessary inputs to model this decision. These inputs can each be represented as an outcome matrix and combined using a weighted sum. Calibrating these weights can be accomplished through the use of internet surveys.
Building and Maintaining Trust Between Humans and Guidance Robots in an Emergency

(Georgia Institute of Technology, 2013-03) Robinette, Paul ; Wagner, Alan R. ; Howard, Ayanna M.

Emergency evacuations are dangerous situations for both evacuees and first responders. The use of automation in the form of guidance robots can reduce the danger to humans by both aiding evacuees and assisting first responders. This presents an interesting opportunity to explore the trust dynamic between frightened evacuees and automated robot guides. We present our work so far on designing robots to immediately generate trust as well as our initial concept of an algorithm for maintaining trust through interaction.
Trust in Emergency Evacuation Robots

(Georgia Institute of Technology, 2012-11) Robinette, Paul ; Howard, Ayanna M.

Would you trust a robot to lead you to safety in an emergency? What design would best attract your attention in a smoke-filled environment? How should the robot behave to best increase your trust? To answer these questions, we have created a three dimensional environment to simulate an emergency and determine to what degree an individual will follow a robot to a variety of exits. Survey feedback and quantitative scenario results were gathered on two different robot designs. Fifteen volunteers completed a total of seven scenarios each: one without a robot and one with each robot pointing to each of three exits in the environment. Robots were followed by each volunteer in at least two scenarios. One-third of all volunteers followed the robot in each robot-guided scenario.
Information Propagation Applied to Robot-Assisted Evacuation

(Georgia Institute of Technology, 2012-05) Robinette, Paul ; Vela, Patricio A. ; Howard, Ayanna M.

Inspired by large fatality rates due to fires in crowded areas and the increasing presence of robots in dangerous emergency situations, we have implemented a model of information propagation among evacuees. Information about the locations of exits and the relative confidence of the individual in the location of the exit disseminated through a simulated crowd of people during an evacuation modeled after The Station Nightclub fire of 2003. True believers were added to this system as individuals who refused to accept exit information from others, instead preferring to head to their own exit. This system was then tested to find what percentage of true believers most likely existed in the actual fire. Using this true believer percentage, robots were added to the environment to guide evacuees to the nearest exit. The number of people who believed a robot’s instructions was varied to find what percentage of people need to trust these robots in order to exploit information propagation and thus increase survivability. As a lower bound, we have found that 30% of the evacuees should believe a robot’s instructions to significantly increase survival rates.
Information Propagation Applied to Robot-Assisted Evacuation

(Georgia Institute of Technology, 2012-05) Robinette, Paul ; Vela, Patricio A. ; Howard, Ayanna M.

Inspired by large fatality rates due to fires in crowded areas and the increasing presence of robots in dangerous emergency situations, we have implemented a model of information propagation among evacuees. Information about the locations of exits and the relative confidence of the individual in the location of the exit disseminated through a simulated crowd of people during an evacuation modeled after The Station Nightclub fire of 2003. True believers were added to this system as individuals who refused to accept exit information from others, instead preferring to head to their own exit. This system was then tested to find what percentage of true believers most likely existed in the actual fire. Using this true believer percentage, robots were added to the environment to guide evacuees to the nearest exit. The number of people who believed a robot's instructions was varied to find what percentage of people need to trust these robots in order to exploit information propagation and thus increase survivability. As a lower bound, we have found that 30% of the evacuees should believe a robot's instructions to significantly increase survival rates.
Incorporating a Model of Human Panic Behavior for Robotic-Based Emergency Evacuation

(Georgia Institute of Technology, 2011-08) Robinette, Paul ; Howard, Ayanna M.

Evacuating a building in an emergency situation can be very confusing and dangerous. Exit signs are static and thus have no ability to convey information about congestion or danger between the sign and the actual exit door. Emergency personnel may arrive too late to assist in an evacuation. Robots, however, can be stored inside of buildings and can be used to guide evacuees to the best available exit. To enable this process, evacuation robots must have an understanding of how people react in emergency situations. By incorporating a model of human panic behavior, these robots can effectively guide crowds of people to zones of safety. In this paper, we discuss an initial design of these robots and their behaviors. Preliminary simulation results show that a significantly larger proportion of people are evacuated with robot assistance than without.