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 - 10 of 124

Increasing Super Pop VR™ Users' Intrinsic Motivation by Improving the Game's Aesthetics

(Georgia Institute of Technology, 2015-08) García-Vergara, Sergio ; Li, Hongfei ; Howard, Ayanna M.

During physical therapy intervention protocols, it's important to consider the individual's intrinsic motivation to perform in-home recommended exercises. Physical therapy exercises can become tedious thus limiting the individual's progress. Not only have researchers developed serious gaming systems to increase user motivation, but they have also worked on the design aesthetics since results have shown positive effects on the users' performance for attractive models. As such, we improved the aesthetics of a previously developed serious game called Super Pop VR™. Namely, we improved the game graphics, added new game features, and allowed for more game options to provide users the opportunity to tailor their own experience. The conducted user studies show that participants rank the version of the game with the improved aesthetics higher in terms of the amount of interest/enjoyment it generates, thus allowing for an increase in intrinsic motivation when interacting with the system.
Retrieving Experience: Interactive Instance-based Learning Methods for Building Robot Companions

(Georgia Institute of Technology, 2015-05) Park, Hae Won ; Howard, Ayanna M.

A robot companion should adapt to its user’s needs by learning to perform new tasks. In this paper, we present a robot playmate that learns and adapts to tasks chosen by the child on a touchscreen tablet. We aim to solve the task learning problem using an experience-based learning framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar task in which the system generates predictions of task behaviors based on prior actions. In order to automate the processes of instance encoding, acquisition, and retrieval, we have developed a framework that gathers task knowledge through interaction with human teachers. This approach, further referred to as interactive instance-based learning (IIBL), utilizes limited information available to the robot to generate similarity metrics for retrieving instances. In this paper, we focus on introducing and evaluating a new hybrid IIBL framework using sensitivity analysis with artificial neural networks and discuss its advantage over methods using k-NNs and linear regression in retrieving instances.
An Adaptive Robotic Tablet Gaming System for Post-Stroke Hand Function Rehabilitation

(Georgia Institute of Technology, 2015-03) English, Brittney A. ; Howard, Ayanna M.

Physical therapy is a common treatment for the rehabilitation of hemiparesis, or the weakness of one side of the body. Stroke is a common cause of hemiparesis. Stroke survivors regularly struggle with motivation and engagement, especially in-between sessions when the therapist is absent from the exercising process. As a solution, we have developed a robotic tablet gaming system to facilitate post-stroke hand function rehabilitation. Healthy subject pilot studies have been completed to verify that this system increases engagement and is capable of encouraging specific therapeutic motions. In the future, a learning model algorithm will be added to the system to assess the patient’s progress and optimize the recovery time.
Increasing Motor Learning During Hand Rehabilitation Exercises Through the Use of Adaptive Games: A Pilot Study

(Georgia Institute of Technology, 2015-03) English, Brittney A. ; Howard, Ayanna M.

Physical therapy is a common treatment for the rehabilitation of hemiparesis, or the weakness of one side of the body [1]. Unfortunately, a recent study found that about one third of stroke patients who are prescribed rehabilitation in hospital settings are ranked as poor participators in physical therapy [2]. In an attempt to increase morale and participation of stroke survivors in hand function motor therapy, a robotic rehabilitation system is being designed to counteract these hindrances to hand function recovery. For this system, an adaptive game that is only controllable through hand movement has been designed to optimize the challenges and rewards presented to the user. A healthy subjects pilot study was conducted to assess the adaptive game’s ability to increase the motor learning of participants during rehabilitation exercises. During this experiment, participants were asked to wear a robotic wrist sensor that functions as a game controller and play a rehabilitative tablet game that encourages therapeutic motions. To play this game users had to reach various targets in the game scenario by moving their hand in pre-determined ranges of motion. Two game scenarios presented the participant with a constant level of challenge, one of which was an easy scenario and the other a hard scenario, while a third scenario adjusted the game difficulty in order to maintain a constant balance of challenge and reward. When participants were presented with a constant level of challenge, their performance did not increase or decrease linearly during the session. This lack of linear growth or decay suggests that the participants did not experience significant learning and their performances were not hindered by negative emotions such as frustration or boredom. Participants that played the adaptive scenario performed similarly to the fixed difficulty levels when presented with an easy scenario for the beginning portion of the gaming experience and a difficult portion at the end. However, if participants were presented with a difficult scenario at the beginning of their gaming experience and an easy scenario at the end, they performed similarly to the fixed difficulty during the hard portion yet much better than the fixed difficulty during the easy portion. The averages for the easy portion of the adaptive level and the fixed easy level were 90.33% and 82.72%, respectively, and the standard deviations were 10.25% and 17.82%, respectively.
Encouraging Specific Intervention Motions via a Robotic System for Rehabilitation of Hand Function: A Healthy Pilot Study

(Georgia Institute of Technology, 2014-12) English, Brittney A. ; Howard, Ayanna M.

A knowledge gap exists for how to improve hand rehabilitation after stroke using robotic rehabilitation methods, and non-robotic hand rehabilitation methods show only small patient improvements. A proposed solution for this knowledge gap is to integrate the strengths of three of the most favorable rehabilitation strategies for post-stroke rehabilitation of hand function, which are constraint-induced movement therapy (CIMT), high-intensity therapy, and repetitive task training, with a robotic rehabilitation gaming system. To create a system that is composed of collaborative therapy efforts, we must first understand how to encourage rehabilitation intervention motions. An experiment was conducted in which healthy participants were asked to complete six levels of a rehabilitation game, each level designed to encourage a specific therapeutic intervention, and a control, where participants were asked to complete undefined exercise motions. The results showed that participants’ motions were significantly different than the control while playing each of the levels. Upon comparing the actual paths of participants to the paths encouraged by the levels, it was discovered that the participants followed the intended path while encouragement was being provided for them to do so. When the encouraged motions required quick, hard motions, the participants would follow an aliased version of the intended path. This study suggests that robotic rehabilitation systems can not only change how a participant moves, but also encourage specific motions designed to mimic therapeutic interventions.
Robot Learners: Interactive Instance-based Learning and Its Application to Therapeutic Tasks

(Georgia Institute of Technology, 2014-11) Park, Hae Won ; Howard, Ayanna M.

Programming a robot to perform tasks requires training that is beyond the skill level of most individuals. To address this issue, we focus on developing a method that identifies keywords used to convey task knowledge among people and a framework that uses these keywords as conditions for knowledge acquisition by the robot learner. The methodology includes generalizing task modeling and providing a robot learner the ability to learn and improve its skills through accumulated experience gained from interaction with humans. More specifically, the aim of this research addresses the issues of knowledge encoding, acquisition, and retrieval through interactive instance-based learning (IIBL). In interaction studies, the benefit of using such a robot learner is in promoting social behaviors that results from the participant taking on an active role as teacher. Our recent experiment with 33 participants, including 19 typically developing children, and a pilot study with two children with autism spectrum disorder showed that IIBL provides a framework for designing an effective robot learner, and that the robot learner successfully increases the amount of social interactions initiated by the participants.
Robotic Resource Allocation for the Observation of Ablating Target Sources

(Georgia Institute of Technology, 2014-10) Coogle, Richard A. ; Howard, Ayanna M.

Icebergs generated from ice ablation processes continue to be a threat for operations conducted in polar regions. Systems that have been developed to track and observe these threats often use either space-based radar imaging or visual observation by the crew of the ship. Both of these methods have disadvantages, mostly in terms of real-time observation or the physical abilities of the crew. We propose a robotic solution for in-situ observation of icebergs, so that countermeasures may be quickly implemented. Our focus in this work is the problem of allocating resources to observation regions: once areas of iceberg activity have been identified, how are robot observers assigned to these regions and what cost metric may be used to determine the best placement of robot observers. Our solution is currently demonstrated and evaluated in simulation.
Engagement Study of an Integrated Rehabilitation Robotic Tablet-Based Gaming System

(Georgia Institute of Technology, 2014-09) English, Brittney A. ; Howard, Ayanna M.

Physical therapy is a common treatment for the rehabilitation of hemiparesis, or the weakness of one side of the body. However, a significant issue in the therapy process is that patients may struggle with motivation to complete the required therapy sessions. For this purpose, a rehabilitation gaming system was created that combines a hand rehabilitation robot with a gaming application on a tablet to promote engagement and discourage boredom. A study was also conducted to compare engagement between the gaming system and traditional therapeutic exercises. The results of this study show that participants prefer playing rehabilitation games more than they do exercising with traditional rehabilitation methods. On average, participants spent approximately four times longer playing the rehabilitation game before becoming bored than they did with traditional exercises. From their responses to the retrospective survey, the participants experienced significantly more enjoyment and engagement when exercising with the tablet game. They also experienced significantly less boredom. The participants unanimously agreed that if they were required to exercise their wrists for an hour a day, as is a normal requirement for patients in stroke therapy, that they would prefer to do so by playing the tablet game.
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.
Developing a Baseline for Upper-Body Motor Skill Assessment Using a Robotic Kinematic Model

(Georgia Institute of Technology, 2014-08) García-Vergara, Sergio ; Serrano, Miguel M. ; Chen, Yu-Ping ; Howard, Ayanna M.

In the rehabilitation field, determining the effectiveness of an intervention protocol begins by comparing the individual’s movement characteristics against a baseline. In most settings, this baseline is determined through clinical studies involving a range of patients belonging to the same demographic group. Unfortunately, this leads to a process that is difficult to repeat for all patient demographics, or all movement characteristics, given the demands on clinicians’ and patients’ time for performing such clinical baseline measurement studies. To address this issue, we discuss a method that allows clinicians to objectively assess an individual’s movements and compare the resulting outcome kinematic metrics to a kinematic baseline. Instead of collecting human patient data, we propose a robotic kinematic model that generates a baseline for different kinematic parameters in real-time as a function of the state of a given task. We evaluate our methodology on elbow and shoulder range of motion (ROM) angles obtained from eleven typically developing children. We compare the user’s ROM angles to those generated by the proposed model, and discuss the potential of the model to be used in various intervention protocols.