Italy

The conventional smart gloves present a challenge regarding their portability as most work on gesture recognition techniques based on vision sensing and image processing. The multiple algorithms and signal filtering further make the overall process cumbersome. This work proposes a Shape Memory Alloy (SMA) integrated sensing mechanism in a smart glove for autonomous control. A novel hand gesture recognition technology is developed using kinaesthetic feedback from the finger joint movements. The paper presents a smart glove with an external SMA embedded tubing attachment for the thumb, index, and middle fingers. The motion of the SMA wires is constrained between a fixed end on the tip of the fingers, and the other end is connected to a linear position sensor with spring feedback. The SMA wires in this design exist in their Austenite phase at room temperature, thus exhibiting superelastic or pseudoelastic behavior. The tension in the SMA wire is observed and measured upon bending the fingers, corresponding to the mechanical travel in the linear position sensor. The individual and a combination of position sensor readings are then used as commands for actuating interactive toys. Using a three-finger approach, one can extract seven commands depending upon single or multiple finger movements. This data is further used to actuate the toys, and a use-case for cobotic application is proposed to help better understand interactive play, hand-eye coordination, and thus early cognitive development in children with Autism Spectrum Disorder (ASD). The discrete data output with binary data is independent of other devices or heavy data processing requirements, thus making the proposed novel SM-EXO a better alternative for non-portable and complex smart gloves.

IEEE RO-MAN 2022

The Impact of an In-Home Co-Located Robotic Coach in Helping People Make Fewer Exercise Mistakes

**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)

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[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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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.

technical paper

Human-robot interaction (HRI) requires quantitative models of proxemics and trust for robots to use in negotiating with people for space. Hall’s theory of proxemics has been used for decades to describe social interaction distances but has lacked detailed quantitative models and generative explanations to apply to these cases. In the limited case of 
autonomous vehicle interactions with pedestrians crossing a road, a recent model has explained the quantitative sizes of Hall’s distances to 4% error and their links to the concept of trust in human interactions. The present study extends this model by generalising several of its assumptions to cover further cases including human-human and human-robot interactions. It tightens the explanations of Hall zones from 4% to 1% error and fits several more recent empirical HRI results. This may help to further unify these disparate fields and quantify them to a level which enables real-world perational HRI applications.

Quantum RoboSound: Auditory Feedback of a Quantum-Driven Robotic Swarm

In music education, staying motivated has always been a crucial goal, especially for children. Earlier research has demonstrated how social robots could help improve motivation and increase the performance of children practicing music pieces. As important stakeholders, the attitudes and expectations of parents and music teachers are important factors that affect children’s and their own acceptance of applying social robots in music education. In this study, a survey was created and used to assess parents’ and music teachers’ attitudes, expectations, and intentions related to using social robots in music education. The survey results suggested that parents and music teachers expect robots to act as assistive and motivating agents, give positive feedback, and schedule and structure practice sessions. Moreover, parents expect a social robot to evaluate student performance, while music teachers focus more on positive encouragement. The biggest concerns of these stakeholders are whether the robots can convey the emotional aspects of music and demonstrate how to play the instrument. The intention of using robots is positively correlated with positive attitudes toward robots in general and the positive attitude toward robots specifically in the context of music education. The current study contributes insights into parents’ and music teachers’ attitudes and expectations as determinants of their acceptance of social robots in music education and provides a method for measuring these attitudes and expectations.

Activity Recognition in Restaurants to Address Underlying Needs: A Case Study

In Minimal Invasive Surgical procedures, flexible bevel tip needles are widely used for percutaneous intervention
due to the advantage of enhancing the target reaching accuracy. Here, the target reaching accuracy suffers due to tissue in-homogeneity, deformation in tissue domain and improper steering techniques. The main objective of the percutaneous intervention procedure is ensuring patient safety and reaching
desired target position accurately. Several researchers have already developed many approaches to control the needle steering for precise target reaching. To overcome complex
approaches in existing controllers, we have proposed a fuzzy based controller to regulate the needle in a specified plane. Our designed method involves the needle non-holonomic constraints
based kinematics inside tissue domain and Lyapunov analysis based fuzzy rule base for fuzzy inference system which ensures the closed loop stability of needling system for percutaneous
intervention procedure. We have also validated our designed control scheme through extensive simulations and experimentations in biological tissue.


Using the Price Sensitivity Meter to Measure the Value of Transformative Robot Sound

Many physically demanding tasks require the help of a second person to avoid the otherwise harmful effects of musculoskeletal disorders when performing the task alone. The workforce in many domains, such as nursing care, is rapidly declining. Furthermore, complex repositioning tasks during patient care make it all the more difficult to find a simple solution. Therefore, robotic assistance could be a perfect fit to support this scenario. For this purpose, we conducted a study where 12 caregivers were given the task of repositioning a patient simulator (80 kg) within a nursing bed with the help of a robotic assistant. We measured the physical relief potential using muscle activity and force data while examining this human-robot collaboration problem with two different control approaches, namely a virtual reality-based and a rule-based robot control method. The results show that both methods effectively supported the chosen task with varied strengths and weaknesses. The main advantage of VR-based control is the ability to adapt to the situation and execute quickly (6.61 s), but this also results in higher acting robot forces (35.70 N). With the rule-based control variant, a lower speed of support can be noted on average (10.84 s), but better robot interaction forces (14.95 N) can be discovered in return.

Productive Inconvenience: Facilitating Posture Variability by Stimulating Robot-to-Human Handovers

Educational robotics is a field aiming at investigating the use of robots in schools to support teaching and learning activities. While several robotic solutions exist in support of the STEM teaching activities, in this work, we present ``Classmate Robot" as a new social robot to be used in the classrooms as a support to the learning experience through interaction.
Classmate Robot has been designed and developed to improve the effectiveness of the activities by providing a framework where the robot's behaviors can be personalized, and learning applications can be easily integrated on top of the robot interaction capabilities. This approach aims to increase the engagement of learners. 
We introduce the ROS-based architecture developed that is divided into three main layers plus an application layer. 
As a social robot, it combines several multimodal social cues to interact and communicate with students and teachers. 
Moreover, the robot is endowed with a set of behaviors designed to be compliant with its role of ``classmate" in the interaction with the students.

Towards Subjective Experience Prediction for Time-Delayed Teleoperation with Haptic Data Reduction

Describing pain intensity constitutes an essential part of
pain communication. A medical practitioner cannot depend on
pain scales because of the criteria differences between the
patient and caregiver. However, the context of online pain
communication is dependent on a patient's description and
an assessment on a pain scale. This paper proposes a
robot-assisted pain communication system with a tangible
user interface that enables non-numerical pain
communication. The SQTT interface is proposed using design
processes consisting of a novel squeezable input device and
a twisting robot. The twisting expression of the robot
represents pain intensity, which is gauged from the
squeezing power on the input device. Integrating input and
output requires defining how the twisting motion of the
robot is rendered from the squeezing input. An experiment
was conducted to evaluate four methods of rendering
methods: raw, smoothing (moving average), dynamic
smoothing, and updating peak methods. The result of a
non-parametric one-way analysis of variance indicates a
significant difference between the rendering methods. As a
result, an appropriate rendering method is proposed based
on the ranges: smoothing for mild pain, vibrating for
moderate pain, and exaggerating for severe pain. In
conclusion, The robot-assisted pain communication system
can be implemented with intuitive interaction primitives
for online context. This paper contributes a design lesson
for a robot-assisted online pain communication, which uses
a new method of measuring deformation.

Development of a Robot-assisted Online Pain Communication System using a Squeezable Tangible User Interface

Supernumerary Robotic Limbs (SRL) represent a new class of wearable robots that can augment human manipulation capabilities. SRL can be controlled through input interfaces worn on the user body and can interact with the environment. Such interaction can be measured and feedback to the human wearer through wearable haptic interfaces. However, human somatotopic arrangement on the central nervous system lacks a location for artificially added limbs. Where is the best location for feedback coming from a robot not directly associated with a part of the wearer’s body?
This paper sheds light on the problem of the best body location for the feedback coming from an SRL as well as on the relation between the position of the input interface and the haptic interface. We have tested four different body locations - shoulder, wrist, hip, and ankle - for vibrotactile feedback coming from the simulated interaction with a robotic extra limb activated using an interface consisting of an accelerometer worn on the user’s shoulder. Results from the experiment involving 14 participants demonstrated that the ankle feedback position led to significantly worse performances when having inputs from the shoulder, whereas the other three locations led to comparable results.

On the Somatotopic Mapping of Haptic Feedback from Robotic Supernumerary Limbs

Visual user interface paradigms are one of the key modules for brain-actuated wheelchairs, which are a class of promising assistive devices that can increase the autonomy and mobility of people suffering from severe motor impairments. This work proposes a Dynamic Environment-based Visual Interface System (DEVIS) for intuitive navigation target selection for brain-actuated wheelchairs. It is composed of a novel Dynamic Visual Interface (DVI), an RGB image-based perception module (indoor scene classification, object detection and classification, and object tracking), and a P300-based BCI. The DVI displays potential navigation goals in three forms of visual cues: an RGB camera image streaming with object bounding boxes overlaid on objects detected and tracked, three global points of interest (indoor places), and two static commands. Hence, the DVI allows a user to select, through the P300-based BCI, navigation targets/commands that are flashing independently and randomly to create an oddball paradigm. 
Experimental evaluations were carried out in a dynamic setting with 5 participants, who were asked to select targets displayed in the DVI. The dynamic setting was obtained using RGB image sequences of the ISR-RGB-D Dataset, which represents a mission performed by a mobile robotic platform. The target selection was performed through a P300-based BCI, using non-self-paced and self-paced modalities. The obtained results show that the proposed DEVIS can run in real-time and that users are able to correctly select targets with high BCI accuracy rates.

Dynamic Environment-based Visual User Interface for Intuitive Navigation Target Selection for Brain-actuated Wheelchairs

Social isolation is an issue that effects many people, especially those from ethic minorities, LGBTQIA+ communities, the elderly, those in long-term healthcare, and those living with life-limiting illnesses. It has become increasingly evident during the pandemic, when mental health issues have soared, and the importance of interacting with loved ones has been highlighted. While telecommunication software helped a great deal in these unprecedented circumstances, it does not allow for navigation in remote environments, and lacks high level interactions found in face-to-face communication. Therefore, this system has been developed to address these issues, and this study was being conducted to test the technical usability of the system when being used by healthy participants. It was found that 720p is the highest resolution that can be applied before the camera delay becomes unusable; though participants suggested that they would like the option to switch to 2k resolution should they be looking close up without moving. In addition, it became apparent that overall the participants were positive about the system, but would prefer a less bulky head-mounted display, and that the choice of which robot to use with the system (Nao or Pepper) was entirely down to individual preference based on the task being completed.

Effects of a Co-Located Robot and Anthropomorphism on Human Motivation and Emotion across Personality and Gender

Study reproducibility and generalizability of results to broadly inclusive populations is crucial in any research. Previous meta-analyses in HRI have focused on the consistency of reported information from papers in various categories. However, members of the HRI community have noted that much of the information needed for reproducible and generalizable studies is not found in published papers. We address this issue by surveying the reported study metadata over the main proceedings of the 2021 IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) and the past three years (2019 through 2021) of the main proceedings of the International Conference on Human-Robot Interaction (HRI) and alt.HRI. Based on the analysis results, we propose a set of recommendations for the HRI community that follow the longer-standing reporting guidelines from human-computer interaction (HCI), psychology, and other fields most related to HRI. Finally, we examine two key areas for user study reproducibility: recruitment details and participant compensation. We find a lack of reporting of both of these study metadata categories: of the 416 studies across both conferences and all years, 258 studies failed to report recruitment method and 255 studies failed to report compensation. This work provides guidance about specific types of needed reporting improvements for the field of HRI.

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The Impact of an In-Home Co-Located Robotic Coach in Helping People Make Fewer Exercise Mistakes

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Quantum RoboSound: Auditory Feedback of a Quantum-Driven Robotic Swarm

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