Multiscale Entropy Quantifies the Differential Effect of the Medium Embodiment on Older Adults Prefrontal Cortex during the Story Comprehension: A Comparative Analysis

Todays’ communication media virtually impact and transform every aspect of our daily communication and yet the extent of their embodiment on our brain is unexplored. The study of this topic becomes more crucial, considering the rapid advances in such fields as socially assistive robotics that envision the use of intelligent and interactive media for providing assistance through social means. In this article, we utilize the multiscale entropy (MSE) to investigate the effect of the physical embodiment on the older people’s prefrontal cortex (PFC) activity while listening to stories. We provide evidence that physical embodiment induces a significant increase in MSE of the older people’s PFC activity and that such a shift in the dynamics of their PFC activation significantly reflects their perceived feeling of fatigue. Our results benefit researchers in age-related cognitive function and rehabilitation who seek for the adaptation of these media in robot-assistive cognitive training of the older people. In addition, they offer a complementary information to the field of human-robot interaction via providing evidence that the use of MSE can enable the interactive learning algorithms to utilize the brain’s activation patterns as feedbacks for improving their level of interactivity, thereby forming a stepping stone for rich and usable human mental model.

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Robotics for Assisting Children with Physical and Cognitive Disabilities

Assistive Technologies for Physical and Cognitive Disabilities - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-4666-7373-1.ch005 ◽

2015 ◽

pp. 78-120 ◽

Cited By ~ 4

Author(s):

Mark Tee Kit Tsun ◽

Lau Bee Theng ◽

Hudyjaya Siswoyo Jo ◽

Patrick Then Hang Hui

Keyword(s):

Mental Health ◽

Cerebral Palsy ◽

Social Interaction ◽

Assistive Technologies ◽

Human Robot Interaction ◽

Assistive Robotics ◽

Active Movement ◽

Robot Interaction ◽

Passive Stretching ◽

Locomotion Training

This chapter summarizes the findings of a study on robotics research and application for assisting children with disabilities between the years 2009 and 2013. The said disabilities include impairment of motor skills, locomotion, and social interaction that is commonly attributed to children suffering from Autistic Spectrum Disorders (ASD) and Cerebral Palsy (CP). As opposed to assistive technologies for disabilities that largely account for restoration of physical capabilities, disabled children also require dedicated rehabilitation for social interaction and mental health. As such, the breadth of this study covers existing efforts in rehabilitation of both physical and socio-psychological domains, which involve Human-Robot Interaction. Overviewed topics include assisted locomotion training, passive stretching and active movement rehabilitation, upper-extremity motor function, social interactivity, therapist-mediators, active play encouragement, as well as several life-long assistive robotics in current use. This chapter concludes by drawing attention to ethical and adoption issues that may obstruct the field's effectiveness.

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Brain activity during reciprocal social interaction investigated using conversational robots as control condition

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0033 ◽

2019 ◽

Vol 374 (1771) ◽

pp. 20180033 ◽

Cited By ~ 11

Author(s):

Birgit Rauchbauer ◽

Bruno Nazarian ◽

Morgane Bourhis ◽

Magalie Ochs ◽

Laurent Prévot ◽

...

Keyword(s):

Prefrontal Cortex ◽

Social Interaction ◽

Medial Prefrontal Cortex ◽

Brain Activity ◽

Blood Oxygen Level Dependent ◽

Human Robot Interaction ◽

Human Interaction ◽

Peripheral Blood Flow ◽

Robot Interaction ◽

Temporoparietal Junction

We present a novel functional magnetic resonance imaging paradigm for second-person neuroscience. The paradigm compares a human social interaction (human–human interaction, HHI) to an interaction with a conversational robot (human–robot interaction, HRI). The social interaction consists of 1 min blocks of live bidirectional discussion between the scanned participant and the human or robot agent. A final sample of 21 participants is included in the corpus comprising physiological (blood oxygen level-dependent, respiration and peripheral blood flow) and behavioural (recorded speech from all interlocutors, eye tracking from the scanned participant, face recording of the human and robot agents) data. Here, we present the first analysis of this corpus, contrasting neural activity between HHI and HRI. We hypothesized that independently of differences in behaviour between interactions with the human and robot agent, neural markers of mentalizing (temporoparietal junction (TPJ) and medial prefrontal cortex) and social motivation (hypothalamus and amygdala) would only be active in HHI. Results confirmed significantly increased response associated with HHI in the TPJ, hypothalamus and amygdala, but not in the medial prefrontal cortex. Future analysis of this corpus will include fine-grained characterization of verbal and non-verbal behaviours recorded during the interaction to investigate their neural correlates. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction'.

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Bio-inspired assistive robotics: Service dogs as a model for human-robot interaction and mobile manipulation

2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics ◽

10.1109/biorob.2008.4762910 ◽

2008 ◽

Cited By ~ 16

Author(s):

Hai Nguyen ◽

Charles C. Kemp

Keyword(s):

Human Robot Interaction ◽

Assistive Robotics ◽

Mobile Manipulation ◽

Service Dogs ◽

Robot Interaction

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A Human-Robot Cooperative and Personalized Compliant Joint Controller for Upper-Limb Rehabilitation Robots: The Elbow Joint Validation

10.21203/rs.3.rs-595139/v1 ◽

2021 ◽

Author(s):

Stefano Dalla Gasperina ◽

Valeria Longatelli ◽

Francesco Braghin ◽

Alessandra Laura Giulia Pedrocchi ◽

Marta Gandolla

Keyword(s):

Upper Limb ◽

Human Robot Interaction ◽

Control Law ◽

Robot Interaction ◽

Activation Patterns ◽

Upper Limb Rehabilitation ◽

Control Framework ◽

High Level ◽

Limb Rehabilitation ◽

Muscular Activation

Abstract Background: Appropriate training modalities for post-stroke upper-limb rehabilitation are key features for effective recovery after the acute event. This work presents a novel human-robot cooperative control framework that promotes compliant motion and renders different high-level human-robot interaction rehabilitation modalities under a unified low-level control scheme. Methods: The presented control law is based on a loadcell-based impedance controller provided with positive-feedback compensation terms for disturbances rejection and dynamics compensation. We developed an elbow flexion-extension experimental setup, and we conducted experiments to evaluate the controller performances. Seven high-level modalities, characterized by different levels of (i) impedance-based corrective assistance, (ii) weight counterbalance assistance, and (iii) resistance, have been defined and tested with 14 healthy volunteers.Results: The unified controller demonstrated suitability to promote good transparency and render compliant and high-impedance behavior at the joint. Superficial electromyography results showed different muscular activation patterns according to the rehabilitation modalities. Results suggested to avoid weight counterbalance assistance, since it could induce different motor relearning with respect to purely impedance-based corrective strategies. Conclusion: We proved that the proposed control framework could implement different physical human-robot interaction modalities and promote the assist-as-needed paradigm, helping the user to accomplish the task, while maintaining physiological muscular activation patterns. Future insights involve the extension to multiple degrees of freedom robots and the investigation of an adaptation control law that makes the controller learn and adapt in a therapist-like manner.

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Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories

Scientific Reports ◽

10.1038/s41598-019-54280-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Soheil Keshmiri ◽

Hidenobu Sumioka ◽

Ryuji Yamazaki ◽

Masahiro Shiomi ◽

Hiroshi Ishiguro

Keyword(s):

Prefrontal Cortex ◽

Information Content ◽

Elderly Care ◽

Verbal Communication ◽

Human Robot Interaction ◽

Assistive Robotics ◽

Face To Face ◽

Novel Approach ◽

Perceived Difficulty ◽

Comparable Performance

AbstractThe ability to realize the individuals’ impressions during the verbal communication allows social robots to significantly facilitate their social interactions in such areas as child education and elderly care. However, such impressions are highly subjective and internalized and therefore cannot be easily comprehended through behavioural observations. Although brain-machine interface suggests the utility of the brain information in human-robot interaction, previous studies did not consider its potential for estimating the internal impressions during verbal communication. In this article, we introduce a novel approach to estimation of the individuals’ perceived difficulty of stories using the quantified information content of their prefrontal cortex activity. We demonstrate the robustness of our approach by showing its comparable performance in face-to-face, humanoid, speaker, and video-chat settings. Our results contribute to the field of socially assistive robotics by taking a step toward enabling robots determine their human companions’ perceived difficulty of conversations, thereby enabling these media to sustain their communication with humans by adapting to individuals’ pace and interest in response to conversational nuances and complexity.

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Benchmarks for evaluating socially assistive robotics

Interaction Studies ◽

10.1075/is.8.3.07fei ◽

2007 ◽

Vol 8 (3) ◽

pp. 423-439 ◽

Cited By ~ 63

Author(s):

David Feil-Seifer ◽

Kristine Skinner ◽

Maja J. Matarić

Keyword(s):

Human Robot Interaction ◽

Assistive Robotics ◽

Application Domain ◽

Robot Interaction ◽

Socially Assistive Robotics ◽

Ethical Implications ◽

The Many

Socially assistive robotics (SAR) is a growing area of research. Evaluating SAR systems presents novel challenges. Using a robot for a socially assistive task can have various benefits and ethical implications. Many questions are important to understanding whether a robot is effective for a given application domain. This paper describes several benchmarks for evaluating SAR systems. There exist numerous methods for evaluating the many factors involved in a robot’s design. Benchmarks from psychology, anthropology, medicine, and human–robot interaction are proposed as measures of success in evaluating a given SAR system and its impact on the user and broader population.

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Designing Acceptable Robots for Assisting Older Adults: A Pilot Study on the Willingness to Interact

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182010686 ◽

2021 ◽

Vol 18 (20) ◽

pp. 10686

Author(s):

Roberta Bevilacqua ◽

Elisa Felici ◽

Filippo Cavallo ◽

Giulio Amabili ◽

Elvira Maranesi

Keyword(s):

Older Adults ◽

Mixed Method ◽

Interaction Analysis ◽

Human Robot Interaction ◽

Assistive Robotics ◽

Service Robot ◽

Emotional Expressions ◽

Nonverbal Behaviors ◽

Robot Interaction ◽

Method Approach

The aim of this paper was to explore the psychosocial determinants that lead to acceptability and willingness to interact with a service robot, starting with an analysis of older users’ behaviors toward the Robot-Era platform, in order to provide strategies for the promotion of social assistive robotics. A mixed-method approach was used to collect information on acceptability, usability, and human–robot interaction, by analyzing nonverbal behaviors, emotional expressions, and verbal communication. The study involved 35 older adults. Twenty-two were women and thirteen were men, aged 73.8 (±6) years old. Video interaction analysis was conducted to capture the users’ gestures, statements, and expressions. A coded scheme was designed on the basis of the literature in the field. Percentages of time and frequency of the selected events are reported. The statements of the users were collected and analyzed. The results of the behavioral analysis reveal a largely positive attitude, inferred from nonverbal clues and nonverbal emotional expressions. The results highlight the need to provide robotic solutions that respect the tasks they offer to the users It is necessary to give older consumers dedicated training in technological literacy to guarantee proper, long-lasting, and successful use.

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Escaping Oz: Autonomy in Socially Assistive Robotics

Annual Review of Control Robotics and Autonomous Systems ◽

10.1146/annurev-control-060117-104911 ◽

2019 ◽

Vol 2 (1) ◽

pp. 33-61 ◽

Cited By ~ 7

Author(s):

Caitlyn Clabaugh ◽

Maja Matarić

Keyword(s):

Design Patterns ◽

Human Robot Interaction ◽

Assistive Robotics ◽

Robot Interaction ◽

Wizard Of Oz ◽

Socially Assistive Robotics ◽

Special Needs Education ◽

Engineering Practices ◽

And Robotics

The field of socially assistive robotics (SAR) aims to supplement the efforts of clinicians, therapists, educators, and caregivers through individualized, socially mediated interventions with robots. SAR is faced with the interdisciplinary challenge to balance sensitive domain needs with current technical limitations. Many researchers in SAR and the broader human–robot interaction community overcome technical barriers by using a Wizard of Oz approach, or teleoperation of the robot or aspects of the interaction. Although Wizard of Oz is a well-established practice, it becomes intractable in critical SAR domains that require long-term, situated support, such as aging in place and special needs education. In this article, we define a set of autonomy-centric design properties for SAR interventions based on concepts from artificial intelligence and robotics. These properties structure a systematic review of the last decade of autonomous SAR research. From the review, we draw and discuss common computational methods, engineering practices, and design patterns that enable autonomy in SAR.

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