DESIGNING INTELLIGENT SOCIALLY ASSISTIVE ROBOTS AS EFFECTIVE TOOLS IN COGNITIVE INTERVENTIONS

2011 ◽  
Vol 08 (01) ◽  
pp. 103-126 ◽  
Author(s):  
JEANIE CHAN ◽  
GOLDIE NEJAT ◽  
JINGCONG CHEN
Keyword(s):  
Cognitive Stimulation ◽  
Human Robot Interaction ◽  
Control Architecture ◽  
Cognitive Interventions ◽  
Training Interventions ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Assistive Robot ◽  
Term Basis ◽  
Socially Assistive Robot

Recently, there has been a growing body of research that supports the effectiveness of using non-pharmacological cognitive and social training interventions to reduce the decline of or improve brain functioning in individuals suffering from cognitive impairments. However, implementing and sustaining such interventions on a long-term basis is difficult as they require considerable resources and people, and can be very time-consuming for healthcare staff. Our research focuses on making these interventions more accessible to healthcare professionals through the aid of robotic assistants. The objective of our work is to develop an intelligent socially assistive robot with abilities to recognize and identify human affective intent to determine its own appropriate emotion-based behavior while engaging in assistive interactions with people. In this paper, we present the design of a novel human-robot interaction (HRI) control architecture that allows the robot to provide social and cognitive stimulation in person-centered cognitive interventions. Namely, the novel control architecture is designed to allow a robot to act as a social motivator by encouraging, congratulating and assisting a person during the course of a cognitively stimulating activity. Preliminary experiments validate the effectiveness of the control architecture in providing assistive interactions during a HRI-based person-directed activity.

The Design of an Intelligent Socially Assistive Robot for Person-Centered Cognitive Interventions

2010 ◽  
Author(s):  
Jeanie Chan ◽  
Goldie Nejat
Keyword(s):  
Cognitive Stimulation ◽  
Human Robot Interaction ◽  
Control Architecture ◽  
The Novel ◽  
Brain Functioning ◽  
Cognitive Interventions ◽  
Training Interventions ◽  
Assistive Robot ◽  
Term Basis ◽  
Socially Assistive Robot

Recently, there has been a growing body of research that supports the effectiveness of using non-pharmacological cognitive and social training interventions to reduce the decline of or improve brain functioning in individuals suffering from cognitive impairments. However, implementing and sustaining such interventions on a long-term basis is difficult as they require considerable resources and people, and can be very time-consuming for healthcare staff. The objectives of our research are to validate the effectiveness of these training interventions and make them more accessible to healthcare professionals through the aid of robotic assistants. Our work focuses on designing a human-like socially assistive robot, Brian 2.0, with abilities to recognize and identify human affective intent to determine its own appropriate emotion-based behavior while engaging in natural and believable social interactions with people. In this paper, we present the design of a novel human-robot interaction (HRI) control architecture for Brian 2.0 that allows the robot to provide social and cognitive stimulation in person-centered cognitive interventions. Namely, the novel control architecture is designed to allow a robot to act as a social motivator by encouraging, congratulating and assisting a person during the course of a cognitively stimulating activity. Preliminary experiments validate the robot’s ability to provide assistive interactions during a HRI-based person-directed activity.

A pilot Project Exploring the Utility and Acceptability of a Socially-assistive Robot in an Assessment Unit for People with Neuropsychiatric Symptoms

2017 ◽  
Vol 41 (S1) ◽  
pp. S104-S104
Author(s):  
S. Loi ◽  
R. Khosla ◽  
K. Nguyen ◽  
N. Lautenschlager ◽  
D. Velakoulis
Keyword(s):  
Neuropsychiatric Symptoms ◽  
Well Being ◽  
Pilot Project ◽  
Future Directions ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Assistive Robot ◽  
Potential Benefits ◽  
Competing Interest ◽  
Socially Assistive Robot

ObjectivesSocially-assistive robots have been used with older adults with cognitive impairment in residential care, and found to improve mood and well-being. However, there is little known about the potential benefits in adults with other neuropsychiatric symptoms.AimsThe aim of this project was explore the utility and acceptability of a socially-assistive robot in engaging adults with a variety of neuropsychiatric symptoms.MethodsBetty, a socially-assistive robot was installed in a unit which specialises in the assessment and diagnosis of adults presenting with neuropsychiatric symptoms. She is 39 cm tall, has a baby-face appearance and has the ability to engage individuals through personalised services which can be programmed according to individuals’ preferences. These include singing songs and playing games. Training for the nursing staff who were responsible for incorporating Betty into the unit activities was provided. The frequency, duration and type of activity which Betty was involved in was recorded. Patients admitted who could provide informed consent were able to be included in the project. These participants completed pre- and post-questionnaires.ResultsEight patients (mean age 54.4 years, SD 13.6) who had diagnoses ranging from depression and schizophrenia participated. Types of activities included singing songs, playing Bingo and reading the news. Participants reported that they were comfortable with Betty and did not feel concerned in her presence. They enjoyed interacting with her.ConclusionsThis pilot project demonstrated that participants found Betty to be acceptable and she was useful in engaging them in activities. Future directions would involve larger sample sizes and different settings.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Social Intelligence for a Task-Driven Assistive Robot

2010 ◽  
Author(s):  
Goldie Nejat ◽  
Maurizio Ficocelli
Keyword(s):  
Social Intelligence ◽  
Effective Interaction ◽  
Well Being ◽  
Emotional Behavior ◽  
Control Architecture ◽  
Patient Centered ◽  
Assistive Robots ◽  
Assistive Robot ◽  
Emotional Model ◽  
Socially Assistive Robot

The objective of a socially assistive robot is to create a close and effective interaction with a human user for the purpose of giving assistance. In particular, the social interaction, guidance and support that a socially assistive robot can provide a person can be very beneficial to patient-centered care. However, there are a number of conundrums that must be addressed in designing such a robot. This work addresses one of the main limitations in the development of intelligent task-driven socially assistive robots: Robotic control architecture design and implementation with explicit social and assistive task functionalities. In particular, in this paper, a unique emotional behavior module is presented and implemented in a learning-based control architecture for human-robot interactions (HRI). The module is utilized to determine the appropriate emotions of the robot, as motivated by the well-being of the person, during assistive task-driven interactions. A novel online updating technique is used in order to allow the emotional model to adapt to new people and scenarios. Preliminary experiments presented show the effectiveness of utilizing robotic emotional assistive behavior during HRI in assistive scenarios.

An Human-Robot Interaction Control Architecture for an Intelligent Assistive Robot

2009 ◽  
Author(s):  
Maurizio Ficocelli ◽  
Goldie Nejat ◽  
Greg Minseok Jhin
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Human Robot Interaction ◽  
Control Architecture ◽  
Health Providers ◽  
Term Care ◽  
Research Issues ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Health Care Applications

As the first round of baby boomers turn 65 in 2011, we must be prepared for the largest demographic group in history that could need long term care from nursing homes and home health providers. The development of socially assistive robots for health care applications can provide measurable improvements in patient safety, quality of care, and operational efficiencies by playing an increasingly important role in patient care in the fast pace of crowded clinics, hospitals and nursing/veterans homes. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one of the main limitations to the development of intelligent socially assistive robots for health care applications: Robotic control architecture design and implementation with explicit social and assistive task functionalities. In particular, we present the design of a unique learning-based multi-layer decision making control architecture for utilization in determining the appropriate behavior of the robot. Herein, we explore and compare two different learning-based techniques that can be utilized as the main decision-making module of the controller. Preliminary experiments presented show the potential of the integration of the aforementioned techniques into the overall design of such robots intended for assistive scenarios.

An Intelligent Socially Assistive Robot for Health Care

2008 ◽  
Author(s):  
Junichi Terao ◽  
Lina Trejos ◽  
Zhe Zhang ◽  
Goldie Nejat
Keyword(s):  
Health Care ◽  
Sensory System ◽  
Human Robot Interaction ◽  
Research Issues ◽  
Assistive Robots ◽  
Overall Design ◽  
Socially Assistive Robots ◽  
Fast Pace ◽  
Health Care Applications ◽  
Socially Assistive Robot

The development of socially assistive robots for health care applications can provide measurable improvements in patient safety, quality of care, and operational efficiencies by playing an increasingly important role in patient care in the fast pace of crowded clinics, hospitals and nursing/veterans homes. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address two main limitations to the development of intelligent socially assistive robots: (i) identification of human body language via a non-contact sensory system and categorization of these gestures for determining the accessibility level of a person during human-robot interaction, and (ii) decision making control architecture design for determining the learning-based task-driven behavior of the robot during assistive interaction. Preliminary experiments presented show the potential of the integration of the aforementioned techniques into the overall design of such robots intended for assistive scenarios.

A Socially Assistive Robot to Help With Getting Dressed

2017 ◽  
Author(s):  
Leo Woiceshyn ◽  
Yuchi Wang ◽  
Goldie Nejat ◽  
Beno Benhabib
Keyword(s):  
Impression Formation ◽  
Recommendation System ◽  
Well Being ◽  
Autism Spectrum ◽  
Substantial Impact ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Assistive Robot ◽  
Socially Assistive Robot ◽  
Clothing Choices

Getting dressed is a universally performed daily activity, and has a substantial impact on a person’s well-being. Choosing appropriate outfits to wear is important, as clothes protect a person from elements in the environment, and act as a barrier against harsh surfaces [1]. Studies have shown strong correlation between clothing choices and perceptions of sociability, emotional stability, and impression formation (e.g., [2]). This activity, however, can be difficult for some individuals, as they may lack the required reasoning and judgement required [3]. They include children with intellectual and learning disabilities [4] (e.g., Down syndrome [5], dyspraxia [6], autism spectrum disorder [7]), and older adults suffering from dementia including Alzheimer’s disease [8,9], or HIV-associated neurocognitive disorders [10]. In this paper, we present the development of a novel autonomous robotic clothing recommendation system to provide appropriate clothing options, which are personalized to a user’s wardrobe. This research expands on our previous work on socially assistive robots providing assistance with other daily activities, including meal eating [11] and playing Bingo games [12]. Currently, a few smartphone applications exist for providing outfit choices (e.g., [13,14]); however, unlike our proposed system, they are fashion-focused and not able to adapt online to a user’s preferences. Furthermore, by utilizing a socially assistive robot, we provide a more engaging interaction. We utilize the small Nao social robot, Leia, to guide and interact with a user in order to obtain information regarding his/her preferences, the activity for which the clothing will be worn, as well as the environment in which the activity will take place in order to make outfit recommendations, Fig. 1.

scholarly journals Towards a communication model for a sociable companion robot

2021 ◽  
Author(s):  
Lauren Dwyer
Keyword(s):  
Human Robot Interaction ◽  
Communication Model ◽  
Robot Interaction ◽  
The Public ◽  
Assistive Robots ◽  
Technical Requirements ◽  
Long Term Study ◽  
Robot Evaluation ◽  
Socially Assistive Robot

Anxiety has a lifetime prevalence of 31% of Canadians (Katzman et al. 2014). In Canada, psychological services are only covered by provincial health insurance if the psychologist is employed in the public sector; this means long wait times in the public system or expensive private coverage (Canadian Psychological Association). Currently, social robots and Socially Assistive Robots (SAR) are used in the treatment of elderly individuals in nursing homes, as well as children with autism (Feil-Seifer & Matarić, 2011; Tapus et al., 2012). The following MRP is the first step in a long-term project that will contend with the issues faced by individuals with anxiety using a combined communications, social robotics, and mental health approach to develop an anxiety specific socially assistive robot companion. The focus of this MRP is the development of a communication model that includes three core aspects of a social robot companion: Human-Robot Interaction (HRI), anxiety disorders, and technical design. The model I am developing will consist of a series of suggestions for the robot that could be implemented in a long-term study. The model will include suggestions towards the design, communication means, and technical requirements, as well as a model for evaluating the robot from a Human-Robot- Interaction perspective. This will be done through an evaluation of three robots, Sphero’s BB-8 App Enabled Droid, Aldebaran’s Nao, and the Spin Master Zoomer robot. Evaluation measures include modified versions of Shneiderman’s (1992) evaluation of human-factors goals, Feil-Seifer et al.’s (2007) SAR evaluative questions, prompts for the description of both the communication methods and the physical characteristics, and a record of the emotional response of the user when interacting with the robot.

scholarly journals Towards a communication model for a sociable companion robot

2021 ◽  
Author(s):  
Lauren Dwyer
Keyword(s):  
Human Robot Interaction ◽  
Communication Model ◽  
Robot Interaction ◽  
The Public ◽  
Assistive Robots ◽  
Technical Requirements ◽  
Long Term Study ◽  
Robot Evaluation ◽  
Socially Assistive Robot

Anxiety has a lifetime prevalence of 31% of Canadians (Katzman et al. 2014). In Canada, psychological services are only covered by provincial health insurance if the psychologist is employed in the public sector; this means long wait times in the public system or expensive private coverage (Canadian Psychological Association). Currently, social robots and Socially Assistive Robots (SAR) are used in the treatment of elderly individuals in nursing homes, as well as children with autism (Feil-Seifer & Matarić, 2011; Tapus et al., 2012). The following MRP is the first step in a long-term project that will contend with the issues faced by individuals with anxiety using a combined communications, social robotics, and mental health approach to develop an anxiety specific socially assistive robot companion. The focus of this MRP is the development of a communication model that includes three core aspects of a social robot companion: Human-Robot Interaction (HRI), anxiety disorders, and technical design. The model I am developing will consist of a series of suggestions for the robot that could be implemented in a long-term study. The model will include suggestions towards the design, communication means, and technical requirements, as well as a model for evaluating the robot from a Human-Robot- Interaction perspective. This will be done through an evaluation of three robots, Sphero’s BB-8 App Enabled Droid, Aldebaran’s Nao, and the Spin Master Zoomer robot. Evaluation measures include modified versions of Shneiderman’s (1992) evaluation of human-factors goals, Feil-Seifer et al.’s (2007) SAR evaluative questions, prompts for the description of both the communication methods and the physical characteristics, and a record of the emotional response of the user when interacting with the robot.

scholarly journals Application of Reinforcement Learning to a Robotic Drinking Assistant

Robotics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Tejas Kumar Shastha ◽  
Maria Kyrarini ◽  
Axel Gräser
Keyword(s):  
Reinforcement Learning ◽  
Tactile Feedback ◽  
Human Robot Interaction ◽  
Assistive Robotics ◽  
Training Procedure ◽  
Assistive Robots ◽  
Assistive Robot ◽  
Markov Decision ◽  
Degree Of Acceptance ◽  
High Degree

Meal assistant robots form a very important part of the assistive robotics sector since self-feeding is a priority activity of daily living (ADL) for people suffering from physical disabilities like tetraplegia. A quick survey of the current trends in this domain reveals that, while tremendous progress has been made in the development of assistive robots for the feeding of solid foods, the task of feeding liquids from a cup remains largely underdeveloped. Therefore, this paper describes an assistive robot that focuses specifically on the feeding of liquids from a cup using tactile feedback through force sensors with direct human–robot interaction (HRI). The main focus of this paper is the application of reinforcement learning (RL) to learn what the best robotic actions are, based on the force applied by the user. A model of the application environment is developed based on the Markov decision process and a software training procedure is designed for quick development and testing. Five of the commonly used RL algorithms are investigated, with the intention of finding the best fit for training, and the system is tested in an experimental study. The preliminary results show a high degree of acceptance by the participants. Feedback from the users indicates that the assistive robot functions intuitively and effectively.

scholarly journals Comply with Me: Using Design Manipulations to Affect Human–Robot Interaction in a COVID-19 Officer Robot Use Case

2021 ◽  
Vol 5 (11) ◽  
pp. 71
Author(s):  
Ela Liberman-Pincu ◽  
Amit David ◽  
Vardit Sarne-Fleischmann ◽  
Yael Edan ◽  
Tal Oron-Gilad
Keyword(s):  
Thematic Analysis ◽  
Face Mask ◽  
Human Robot Interaction ◽  
Use Case ◽  
Robot Interaction ◽  
Public Building ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Robotic Application

This study examines the effect of a COVID-19 Officer Robot (COR) on passersby compliance and the effects of its minor design manipulations on human–robot interaction. A robotic application was developed to ensure participants entering a public building comply with COVID restrictions of a green pass and wearing a face mask. The participants’ attitudes toward the robot and their perception of its authoritativeness were explored with video and questionnaires data. Thematic analysis was used to define unique behaviors related to human–COR interaction. Direct and extended interactions with minor design manipulation of the COR were evaluated in a public scenario setting. The results demonstrate that even minor design manipulations may influence users’ attitudes toward officer robots. The outcomes of this research can support manufacturers in rapidly adjusting their robots to new domains and tasks and guide future designs of authoritative socially assistive robots (SARs).

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