Incorporating Conversational Strategies in a Social Robot to Interact with People with Dementia

Background: Socially assistive robots (SARs) have the potential to assist nonpharmacological interventions based on verbal communication to support the care of persons with dementia (PwDs). However, establishing verbal communication with a PwD is challenging. Thus, several authors have proposed strategies to converse with PwDs. While these strategies have proved effective at enhancing communication between PwDs and their caregivers, they have not been used or tested in the domain of human-robot interaction. Objectives: This study aimed to assess the effectiveness of incorporating conversational strategies proposed in the literature for caregivers, during PwD-robot interactions. Methods: We conducted a total of 23 group sessions based on music and conversation therapy, where a SAR interacted with 12 PwDs (mean = 80.25 years) diagnosed with mild to moderate-stage dementia. Using a single subject research approach, we designed an AB study to assess the effectiveness of the conversational strategies in the PwD-robot interaction. Our analysis focuses on the direct communication between the PwDs and the robot, and the perceived enjoyment of PwDs. Results: The number of utterances made from a PwD to the robot increased significantly when the conversational strategies were included in the robot. In addition, PwDs engaged in more sustained conversations. Additionally, PwDs enjoyed conversing with the robot Eva, as much as listening to music. These results indicate that the use of these conversational strategies is effective at increasing the interaction between PwD and a SAR. Conclusions: PwDs who participated in the study engaged and enjoyed the interaction with the SAR. The results provide evidence of the importance of incorporating appropriate conversational strategies in SARs that support interventions for the care and social stimulation of PwDs.

Download Full-text

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

Multimodal Technologies and Interaction ◽

10.3390/mti5110071 ◽

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).

Download Full-text

Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials

Sensors ◽

10.3390/s20113033 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3033

Author(s):

Soheil Keshmiri ◽

Masahiro Shiomi ◽

Hidenobu Sumioka ◽

Takashi Minato ◽

Hiroshi Ishiguro

Keyword(s):

Preliminary Investigation ◽

Real Life ◽

Human Robot Interaction ◽

Machine Learning Algorithms ◽

Robot Interaction ◽

Affective Communication ◽

Assistive Robots ◽

Socially Assistive Robots ◽

Average Accuracy ◽

Different Types

Touch plays a crucial role in humans’ nonverbal social and affective communication. It then comes as no surprise to observe a considerable effort that has been placed on devising methodologies for automated touch classification. For instance, such an ability allows for the use of smart touch sensors in such real-life application domains as socially-assistive robots and embodied telecommunication. In fact, touch classification literature represents an undeniably progressive result. However, these results are limited in two important ways. First, they are mostly based on overall (i.e., average) accuracy of different classifiers. As a result, they fall short in providing an insight on performance of these approaches as per different types of touch. Second, they do not consider the same type of touch with different level of strength (e.g., gentle versus strong touch). This is certainly an important factor that deserves investigating since the intensity of a touch can utterly transform its meaning (e.g., from an affectionate gesture to a sign of punishment). The current study provides a preliminary investigation of these shortcomings by considering the accuracy of a number of classifiers for both, within- (i.e., same type of touch with differing strengths) and between-touch (i.e., different types of touch) classifications. Our results help verify the strength and shortcoming of different machine learning algorithms for touch classification. They also highlight some of the challenges whose solution concepts can pave the path for integration of touch sensors in such application domains as human–robot interaction (HRI).

Download Full-text

Promoting Critical Thinking Dispositions in Children and Adolescents Through Human-Robot Interaction with Socially Assistive Robots

Communications in Computer and Information Science - Technology and Innovation in Learning, Teaching and Education ◽

10.1007/978-3-030-20954-4_11 ◽

2019 ◽

pp. 153-165

Author(s):

Dimitrios Pnevmatikos ◽

Panagiota Christodoulou ◽

Nikolaos Fachantidis

Keyword(s):

Critical Thinking ◽

Children And Adolescents ◽

Human Robot Interaction ◽

Robot Interaction ◽

Thinking Dispositions ◽

Assistive Robots ◽

Socially Assistive Robots ◽

Critical Thinking Dispositions

Download Full-text

Human Affective State Recognition and Classification During Human-Robot Interaction Scenarios

Volume 3: ASME/IEEE 2009 International Conference on Mechatronic and Embedded Systems and Applications; 20th Reliability, Stress Analysis, and Failure Prevention Conference ◽

10.1115/detc2009-87647 ◽

2009 ◽

Cited By ~ 3

Author(s):

Zhe Zhang ◽

Goldie Nejat

Keyword(s):

Affective State ◽

Human Robot Interaction ◽

Affective States ◽

Robot Interaction ◽

Research Issues ◽

Assistive Robots ◽

Socially Assistive Robots ◽

Main Challenge ◽

Fast Pace ◽

Health Care Applications

A new novel breed of robots known as socially assistive robots is emerging. These robots are capable of providing assistance to individuals through social and cognitive interaction. 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 main challenge in the development of intelligent socially assistive robots: The robot’s ability to identify, understand and react to human intent and human affective states during assistive interaction. In particular, we present a unique non-contact and non-restricting sensory-based approach for identification and categorization of human body language in determining the affective state of a person during natural real-time human-robot interaction. This classification allows the robot to effectively determine its taskdriven behavior during assistive interaction. Preliminary experiments show the potential of integrating the proposed gesture recognition and classification technique into intelligent socially assistive robotic systems for autonomous interactions with people.

Download Full-text

Towards a communication model for a sociable companion robot

10.32920/ryerson.14668440.v1 ◽

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.

Download Full-text