United States

We focused on neonatal resuscitation, which is difficult to train clinically, and our long- term goal is a neonatal resuscitation training system that can simulate cases and can feedback interactively based on quantitative evaluation of the procedures. In this study, we developed the neonatal simulator that has a respiratory movement simulating mechanism and a chest compression measurement mechanism. Through an interview with neonatologists, the fidelity of respiratory movements and the possibility of evaluating chest compression with the developed simulator were assessed.

IEEE RO-MAN 2021

Robocentric Conversational Group Discovery

neonatal medicine

medical simulator

robots in education

medical and surgical applications

Dear IPSO Virtual Conference Attendees:

Welcome to the IPSO 2023 Virtual Conference. We are delighted to bring together experts from multiple disciplines and six continents to provide a diverse perspective on pediatric cerebrovascular disease.

A few quick words about what to expect. Two important goals of this conference are to (1) foster a sense of community and (2) identify global priorities for our field. We encourage you to express your thoughts live in through the chat function to provide a global perspective on all topics. Please introduce yourself in the chat, so everyone knows where you are attending from.

We are excited to announce IPSO is able to offer 9.5 CME units for the virtual conference. If you would like to claim CME credits, please email membership@internationalpediatricstroke.org. 

We want to hear from you! Planning for IPSO 2024 Congress on March 22nd-24th in Toronto, Canada, will commence as soon as the Virtual Conference ends. Let us know your priorities through the live chat function, via email, or by submitting a symposium idea. Based on your input, the Scientific Program Committee will select the topics that we will address in greater depth when we meet again in the spring.

We hope you find the Virtual Conference both interesting and educational, and hope to see you in person in Toronto, or at another IPSO meeting in the future.

Warm regards,

Heather Fullerton, IPSO President 
Maja Steinlin, IPSO Vice-President 
Lori Jordan, IPSO Secretary & Treasurer 
Noma Dlamini, IPSO Scientific Program Committee Chair 


The International Pediatric Stroke Organization (IPSO) 2023 Virtual Conference is April 28-29th, 2023. The mission of the virtual conference is to unify the global community of professionals engaged in research and clinical care for children with cerebrovascular diseases, and chart a path forward for multinational, multidisciplinary efforts. Similar to the IPSO 2021 Virtual Conference, IPSO has partnered with Underline to host this virtual conference. 

The 2023 IPSO Virtual Conference will offer:

* A highly interactive format: brief recorded talks followed by live panel discussions with audience participation through a chat function and live audience surveys
* Optional break-out groups for IPSO committees, working groups, councils
* Poster sessions available on-demand throughout the conference with a chat function for the audience to comment for ask questions of the presenters

Disclaimer: This activity has been planned and implemented in accordance with the accreditation requirements and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of UCSF and the International Pediatric Stroke Organization (IPSO) and Hospital for Sick Children, and Vanderbilt University Medical Center. UCSF is accredited by the ACCME to provide continuing medical education for physicians.

 UCSF designates this live activity for a maximum of 9.5 AMA PRA Category 1 credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Contact membership@internationalpediatricstroke.org if you would like to claim CME for the IPSO 2023 Virtual Conference. 

**About** 
Founded in February, 2019, we are an organization of health professionals, scientists and patient advocates who care about stroke and other cerebrovascular diseases in childhood. We developed out of our original collaborative network, the International Pediatric Stroke Study (IPSS), and now aim to broaden the scope of our mission beyond research.

* Our mission is to improve the lives of children worldwide with cerebrovascular disease through research, education, clinical care and advocacy.

* Our Vision is a world in which international, multidisciplinary collaborations advance the understanding, care, and outcomes of childhood cerebrovascular disease.

To access IPSO 2023 site, please register [here](https://internationalpediatricstroke.org/2023-virtual-conference/registration/). 

IPSO 2023

The mission of the International Pediatric Stroke Organization (IPSO) 2023 Virtual Conference is to unify the global community of professionals engaged in research and clinical care for children with cerebrovascular diseases, and chart a path forward for multinational, multidisciplinary efforts. 



technical paper

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

This research has developed a new method for predicting natural human arm movements. It introduces the five-parameter logistic function into this application space and uses supervised learning to predict fingertip trajectories and elbow swivel angles. The method presents small error in an experimental study, improving upon previous methods and verifying its effectiveness.

Human Arm Motion Prediction in Reaching Movements

We present a 3D human activity simulator, ``HOIsim", focusing on Human-Object Interactions (HOIs). Using HOIsim we model two complex activities for ``lunch" and ``breakfast". Furthermore, we introduce methods to meaningfully randomize activity flows and the environment topology, and make a dataset of the 3D pose maps publicly available.

HOIsim: Synthesizing Realistic 3D Human-Object Interaction Data for Human Activity Recognition

This work presents a method for recognition of walking activities and prediction of gait periods using wearable sensors. First, a Convolutional Neural Network (CNN) is used to recognise the walking activity and gait period. Second, the output of the CNN is used by a Predicted Information Gain (PIG) method to predict the next most probable gait period while walking. The output of these two processes are combined to adapt the recognition accuracy of the system. This adaptive combination allows us to achieve an optimal recognition accuracy over time.

Parents’ Views on Using Social Robots for Language Learning

Gesture are a natural modality of human interaction. Smart technologies are seamlessly part of our daily lives, becoming essential to endow them with gesture recognition capbilities to ensure a natural interaction. This work leverages the power of convolutional neural networks proposinga novel spatiotemporal visually interppretable image encoding representation of skeleton data for CNN-based gesture recognition.

Robotic Object Sorting via Deep Reinforcement Learning: a Generalized Approach

In a world where applications of human-robot interaction are beneficial for our society in many aspects, robots sometimes need to mimic our behavior and body expressions. To achieve this, we present a novel framework for motion retargeting (i.e., transferring human motion) to humanoid robots by implementing pose estimation, an efficient and practical method of motion capture from just videos of the human based on computer vision and deep learning approaches.

A Bistable Soft Gripper with Mechanically Embedded Sensing and Actuation for Fast Grasping

This paper argues for the possibility of e-friendship, a form of friendship between humans and silicon-based electronic agents, with a focus on sociable robots. Section I introduces the topic. Section II defines "e-friendship." Sections III and IV identify qualities humans and silicon-based systems require for e-friendship. Section V defends the proposal against objections.

Exercising with an “Iron Man”: Design for a Robot Exercise Coach for Persons with Dementia

In this paper, we conduct a full meta-analysis of the existing literature on personality similarity versus dissimilarity (matching/mismatching) and attempt to come to a consensus on if robots with matching personalities are accepted more readily than those with mismatching personalities. In addition, we provide several gaps in this literature worth pursuing.

Social Robot Navigation adapted to Time-dependent Affordance Spaces: a Use Case for Caregiving Centers

A critical issue during HRI is eliciting an intentional stance in the human interactor, who then perceive the agent as an "intelligent" being with full agency towards their own intentions and desires. Eliciting such a stance, however, has proven elusive. We argue for a paradigm shift in our approach to this problem, based on recent evidence from social robotics and digital avatars. To trigger an intentional stance in humans, perhaps our artificial agents need to adopt one about themselves.

What if it speaks like it was from the village? Effects of a Robot speaking in Regional Language Variations on Users’ Evaluations

We aim to improve the believability of virtual players by incorporating personalities, moods, and emotions for them. We propose a novel framework for a multi-player VR Volleyball game by integrating various behavioral changes during gameplay. This is achieved by simulating player reactions and rendering changes in the player's body postures, which are dependent on game environment parameters and scenarios. We showcase visual analysis of individual players' mood changes during gameplay to further corroborate the presented VR framework. Incorporating the proposed model, we exhibit the progression and variations in moods for players with different personalities when presented with varied game situations.

Rate My Robot: The effect of Word-of-Mouth (WOM) on perceptions of a social robot’s teaching performance

We tested a new method for presenting natural materials using an electrostatic tactile texture display. In this method, we combined Coulomb friction as physical model for low frequency friction and texture-induced vibration model for high frequency friction. In a condition where only the low-frequency friction force components were rendered, the materials were correctly recognized at 70%. In contrast, when the high-frequency components were superposed, this rate increased to 80%. Therefore, Coulomb friction model plus fine texture improves material discrimination accuracy.

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Human Arm Motion Prediction in Reaching Movements

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