Patients with neurological gait impairments fear to
fall while performing locomotor tasks like sit to stand, stand to
sit, and level-ground walking without assistance. This prevents
them from participating in daily life. Multi-directional BodyWeight Support (BWS) systems aim to assist training in a safe
environment to overcome this limitation. To ensure the safety
and comfort during training, BWS systems should effectively
and automatically assist patients with forces that support the
desired locomotor task.
Instead of manual switching of controllers through the therapist, we propose a machine learning-based motion onset
recognition model that aims at automatic controller switching
(4 gait related task classes in this paper). In addition to the data
provided by the gait rehabilitation robot FLOAT, data of three
Inertial Measurement Units (IMUs) attached to the sternum
and middle of the outer thighs on 6 healthy participants were
used to predict motion onset of these 4 gait-related tasks.
Four train data sets have been built up from the synchronously
obtained data from the IMUs and the BWS system by dividing
them into observation windows with sizes of 100 ms and 200 ms
and overlap factors of 50% and 90%. From each training
dataset, 108 time-domain features have been extracted, ranked,
and reduced using the Minimum Redundancy Maximum Relevance (MRMR) method from each train set before applying it to
the classifier. The dominant features were applied for training
and comparing four different classifiers. The performance of
the classifiers has been evaluated based on leave one participant
out cross-validation. The ensemble classifier obtained from the
train data set with a window size of 100 ms and 90% overlap
achieved the best performance with an F1 score of 83.7%.

THE LMA12-O Framework for Emotional Robot Eye Gestures

Existing approaches to direct robot-to-human handovers are typically implemented on fixed-base robot arms, or on mobile manipulators that come to a full stop before performing the handover. We propose "on-the-go" handovers which permit a moving mobile manipulator to hand over an object to a human without stopping. The on-the-go handover motion is generated with a reactive controller that allows simultaneous control of the base and the arm. In a user study, human receivers subjectively assessed on-the-go handovers to be more efficient, predictable, natural, better timed and safer than handovers that implemented a "stop-and-deliver" behavior.

I Can’t Believe That Happened! Exploring Expressivity in Collaborative Storytelling with the Tabletop Robot Ha

We present a visual perception system for social robots
that is able to detect a person’s engagement in an interaction
from nonverbal cues independently of principal user activity.
This was achieved by the introduction of a set of proxemics,
body posture and attention features extracted with a Kinect sensor.
The system simultaneously classifies if a person is attempting to interact with the camera and regresses the user's level of availability. Finally, a prototype was implemented which is able to run both previous estimates in real-time using a shared feature vector.

PredGaze:A Incongruity Prediction Model for User’s Gaze Movement

Storytelling with an embodied agent through empathetic interaction by leveraging creative contents with affective robotic agency

Design of Dynamic Tangible Workspaces for Games:Application on Robot-Assisted Upper Limb Rehabilitation

**Session Chairs: Giulio Sandini, Koen Hindriks**

Session V: Non-verbal Cues and Expressiveness

technical paper

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

Registration Is Required

IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

**Session Chairs: Kerstin Sophie Haring, Carolin Straßmann**

Session I: Storytelling in HRI

Interactive 3B - Decision-Making, Cognition, and Robot Architectures

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/)

IEEE RO-MAN 2021

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. 

Randy Gomez

4

SHORT BIO

Presentations

THE LMA12-O Framework for Emotional Robot Eye Gestures

I Can’t Believe That Happened! Exploring Expressivity in Collaborative Storytelling with the Tabletop Robot Ha

PredGaze:A Incongruity Prediction Model for User’s Gaze Movement

Design of Dynamic Tangible Workspaces for Games:Application on Robot-Assisted Upper Limb Rehabilitation

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