A Cabinet of Affective Touches (ACAT) represents an approach to generate tacit design knowledge and build a repository for creating, experiencing and reflecting on designing affective touch with soft robotic technology. Despite being increasingly recognised as a somaesthetic material for affective touch interactions, knowledge on technical parameters and the experiential quality of touch with soft robotic technology is investigated fragmentally in separate disciplines. Sixty four haptics design students used the ACAT platform and created a repository of seventeen designs intended for all primary affective dimensions, covering both positive and negative affective experience. We illustrate how this process cultivated tacit design knowledge and fostered critical reflection on novel design space, in particular, on how to facilitate the creation and sharing the designs of uncomfortable touch.

Menstrual discomfort is a prevalent, diverse, and cyclical lived experience, impacting everyday lives. However, in HCI, it has been mostly approached as a data point, leaving much unknown on how technologies can care for these experiences. In response, we designed Touchware, a collection of on-body touch probes with pneumatic shape-change and weight components, which invite wearers to engage with and care for their menstrual discomfort. We report on the participatory soma design process of making Touchware and its two-week-long deployment study with 6 participants in a workplace setting. Our data analysis highlights diffuse and lingering qualities of menstrual discomfort, shedding light on how technologies may touch bodies in vulnerable states. We discuss the importance and challenges of designing touch technologies for and with bodies in the moments of menstrual discomfort. We conclude with a reflection on the agency of touch and its potential to support the self-care labour and nurturing the radical normalization of rest.

We present a qualitative study with five healthcare experts spe-cialised in different types of touch practice to gain insight in how caring touch can be enacted. Through our analysis we focus onhow to transfer this learning into design considerations towards enacting caring touch from technologies. Despite the rapidly growing expectation for and design interest in touch from technologies intending to enhance care and well-being, the knowledge on how to design caring touch is still fragmented. How caring touch is enacted in inter-personal touch is under-explored and such expertise from healthcare practitioners has not been engaged from the perspective of HCI design research. We propose designers to consider caring as an experiential quality instead of a division between instrumental types of touch and caring types. We recommend when designing for a caring quality in technology-initiated touch that designers create a progression of touch with dynamic sensitivity and adapt the materiality of actuating devices to the plural dimensions of the body’s textures.

Through a soma design process, we explored how to design a shape-changing car seat as a point of interaction between the car and the driver. We developed a low-fidelity prototyping tool to support this design work and describe our experiences of using this tool in a workshop with a car manufacturer. We share the co-designed patterns that we developed: re-engaging in driving; dis-engaging from driving; saying farewell; and being held while turning. Our analysis contributes design knowledge on how we should design for a car seat to ‘touch’ larger, potentially heavier parts of the body including the back, shoulders, hips, and bottom. The non-habitual experience of shape-changing elements in the driver seat helped pinpoint the link between somatic experience and intelligent rational behaviour in driving tasks. Relevant meaning-making processes arose when the two were aligned, improving on the holistic coming together of driver, car, and the road travelled.

Designing and evaluating the affectivity of the robot body has become a frontier topic in Human-Robot Interaction (HRI) , with previous studies [ 1 , 2 ] emphasizing the importance of robot embodiment for human-robot communication.In particular, there is growing interest in how the tactile, haptic materiality of the robot influences and mediates users' affective and emotional states.Indeed, the sheer physicality of robotic systems is a crucial factor in the morphology of the robotic platform, and therefore in the robot's appearance to the user.How do the tactile properties of materials subtly influence user interaction?Why do certain morphologies prompt more empathetic interactions than others?How is nonverbal communication affected through the coordination of movements of the torso, head, and appendages to provide more naturalistic-seeming interaction?What is the role of nonverbal communication in the production of artificial empathy?And how do such factors encourage trust and foster confidence for nonexpert users to interact in the first place?This recognition of machinic corporeality has been of practical interest to designers and engineers working across a range of robot forms and functions.The objective of this special issue is to further this discussion, to consider theoretical, ethical, empirical, and methodological questions related to the design of robotic bodies in the context of affective HRI, and thus foster cross currents among engineering, design, social science, and artistic communities.It originally emerged as a set of conceptual and practical questions from a workshop at the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI'20) in Cambridge, UK, co-organized by two of the editors [ 3 ].The workshop, like so many other events, was canceled because of the restrictions of the COVID-19 pandemic.Consequently, we tried to

For thousands of years in the history of our human society, people are inevitably segregated by long-distances. No matter whatever the reasons are, due to working, studying, visiting, traveling, or even the self-isolations as a result enforced by pandemic diseases, we are always separated with our closest friends, families and/or loved ones all the times in our life. There are no effective ways to bond us all together while we are away from each other. REmotion is an ergonomic, sleek, and human-centered non-invasive neurohaptic interface that turns human emotion brain signals into physical touch stimulations and synthetic images. It allows "speech-free", "typing-free" remote communications with your friends by direct brain-to-brain telepathy. While wearing our earbud-like simple brain-computer interface and the robotic haptic suit/armband, the developed BCI biosensing algorithms can interpret EEG signals, and the deep Generative Adversarial Network (GAN) will translate the "feelings of missing someone" into perceivable images and haptic sensations conveyed remotely to your friends. This method provides on-the-fly telepathy and peaceful feelings when people are segregated from their families, close friends, or loved ones. The only thing they have to do is by just thinking "I miss you", without additional fumbling of speaking on the cellphones, using hands to open the Apps or typing text messages in order to communicate with each other.

In the future, Human-Robot Interaction should be enabled by a compact, human-centered and ergonomic wearable device that can merge human and machine altogether seamlessly by constantly identifying each other's intentions. In this paper, we will showcase the use of an ergonomic and lightweight wearable device that can identify human's eye/facial gestures with physiological signal measurements. Since human's intentions are usually coupled with eye movements and facial expressions, through proper design of interactions using these gestures, we can let people interact with the robots or smart home objects naturally. Combined with Computer Vision object recognition algorithms, we can allow people use very simple and straightforward communication strategies to operate telepresence robot and control smart home objects remotely, totally “Hands-Free”. People can wear a VR head-mounted display and see through the robot's eyes (the remote camera attached on the robot) and interact with the smart home devices intuitively by simple facial gestures or blink of the eyes. It is tremendous beneficial for the people with motor impairment as an assistive tool. For the normal people without disabilities, they can also free their hands to do other tasks and operate the smart home devices at the same time as multimodal control strategies.

Wearable robot that constantly monitors, adapts and reacts to human's need is a promising potential for technology to facilitate stress alleviation and contribute to mental health. Current means to help with mental health include counseling, drug medications, and relaxation techniques such as meditation or breathing exercises to improve mental status. The theory of human touch that causes the body to release hormone oxytocin to effectively alleviate anxiety shed light on a potential alternative to assist existing methods. Wearable robots that generate affective touch have the potential to improve social bonds and regulate emotion and cognitive functions. In this study, we used a wearable robotic tactile stimulation device, AffectNodes2, to mimic human affective touch. The touch-stimulated brain waves were captured from 4 EEG electrodes placed on the parietal, prefrontal and left and right temporal lobe regions of the brain. The novel Deep MSCNN with emotion polling structure had been developed to extract Affective touch, Non-affective touch and Relaxation stimuli with over 95% accuracy, which allows the robot to grasp the current human affective status. This sensing and decoding structure is our first step towards developing a self-adaptive robot to adjust its touch stimulation patterns to help regulate affective status.

Current assistive technologies are complicated, cumbersome, not portable, and users still need to apply extensive fine motor control to operate the device. Brain-Computer Interfaces (BCIs) could provide an alternative approach to solve these problems. However, the current BCIs have low classification accuracy and require tedious human-learning procedures. The use of complicated Electroencephalogram (EEG) caps, where many electrodes must be attached on the user's head to identify imaginary motor commands, brings a lot of inconvenience. In this demonstration, we will showcase EXGbuds, a compact, non-obtrusive, and comfortable wearable device with non-invasive biosensing technology. People can comfortably wear it for long hours without tiring. Under our developed machine learning algorithms, we can identify various eye movements and facial expressions with over 95% accuracy, such that people with motor disabilities could have a fun time to play VR games totally "Hands-free".