Menstrual pain is an embodied, unpredictable, and diverse lived experience. However, current menstrual tracking technologies mainly adopt medicalised and quantitative approaches, reducing pain to numerical data, concealing its organic and messy nature. To uncover the felt, lived experience of pain, we explored soft robotics as a tactile, dynamic medium. Through a series of material workshops, we designed MenstaRay, a novel artefact that mimics the temporality and fluctuations of menstrual pain. Findings from sensory interactions with MenstaRay show that soft robotic materials sensitise and enhance menstruators’ bodily awareness, supporting them in contextually recalling, introspecting, and reflecting on their pain experiences, and encouraging a sense of self-care, self-acceptance, and companionship toward menstrual pain. We frame MenstaRay’s dynamic entanglements with fluid bodily experiences as a meaningful material practice through a feminist lens, highlighting the creative potential of novel programmable interactions of knitted soft robotics to express nuanced pain characteristics, extending to other somatic experience design beyond menstruation.

E-textiles are rapidly emerging as an important area of electronic circuit applications. It also facilitates many socially important applications such as personalized health, elderly care, and smart agriculture. However, the environmental impact and sustainability of e-textiles remain very problematic. STELEC, short for Sustainable Textile ELECtronics, is an interdisciplinary research project funded by the European Innovation Council (EIC) under the Pathfinder programme on the responsible elec-tronics topic seeking cutting-edge innovation. STELEC started in September 2024 and is in its initial stage. The project is a multinational collaboration of research institutes, universities and companies across Europe. It aims at developing next-generation textile-based electronics in applications from sensing, processing to AI, with a commitment to full lifecycle sustainability.

Arm gestures play a pivotal role in facilitating natural mid-air interactions. While computer vision techniques aim to detect these gestures, they encounter obstacles like obfuscation and lighting conditions. Alternatively, wearable devices have leveraged interactive textiles to recognize arm gestures. However, these methods predominantly emphasize textile deformation-based interactions, like twisting or grasping the sleeve, rather than tracking the natural body movement.This study bridges this gap by introducing an e-textile sleeve system that integrates multiple ultra-sensitive graphene e-textile strain sensors in an arrangement that captures bending and twisting along with an inertia measurement unit into a sports sleeve. This paper documents a comprehensive overview of the sensor design, fabrication process, seamless interconnection method, and detachable hardware implementation that allows for reconfiguring the processing unit to other body parts. A user study with ten participants demonstrated that the system could classify six different fundamental arm gestures with over 90% accuracy.

Gestures are composed of motion information (e.g. movements of fingers) and force information (e.g. the force exerted on fingers when interacting with other objects). Current hand gesture recognition solutions such as cameras and strain sensors primarily focus on correlating hand gestures with motion information and force information is seldom addressed. Here we propose a bio-impedance wearable that can recognize hand gestures utilizing both motion information and force information. Compared with previous impedance-based gesture recognition devices that can only recognize a few multi-degrees-of-freedom gestures, the proposed device can recognize 6 single-degree-of-freedom gestures and 20 multiple-degrees-of-freedom gestures, including 8 gestures in 2 force levels. The device uses textile electrodes, is benchmarked over a selected frequency spectrum, and uses a new drive pattern. Experimental results show that 179 kHz achieves the highest signal-to-noise ratio (SNR) and reveals the most distinct features. By analyzing the 49,920 samples from 6 participants, the device is demonstrated to have an average recognition accuracy of 98.96%. As a comparison, the medical electrodes achieved an accuracy of 98.05%.

Textile and fashion designers are increasingly interested in integrating interactive technologies into their practice. However, traditional design education typically lacks support for them to develop technical digital and electronics skills alongside their expertise in materials. Reflecting on outputs from an e-textile design workshop and 8-week design projects with four textile design students using an e-textile toolkit, and follow-up data collection with the students one year after the projects, we argue that starting technical explorations with raw materials results in a better understanding and more flexible use of technical knowledge. We also argue that this newly acquired knowledge is then more fully integrated with their pre-existing material knowledge as it is applied to physical interface design. The results contribute to the development of tools and approaches in supporting designers with material expertise to learn tangible interaction design skills.

Motion capture technology is widely used in movement-related Human-Computer Interaction, especially in digital arts such as digital dance performance. This paper presents a knit stretch sensor-based dance leotard design to evaluate the locations where the sensors best capture the movement on the body. Two studies are undertaken: (1) interviews to determine user requirements of a dance movement sensing system; (2) evaluation of sensor placement on the body. Ten interviewees including dancers, choreographers, and technologists describe their requirements and expectations for a body movement sensing system. The centre of the body (the torso) is determined to be the area of primary interest for dancers and choreographers to sense movement, and technologists find the robustness of textile sensors the most challenging for textile sensing system design. A dance leotard toile is then designed with sensor groupings on the torso along the direction of major muscles, based on the interviewees’ preferred movements to be captured. Each group of the sensors are evaluated by comparing their signal output and a Vicon motion capture system. The evaluation shows sensors which are constantly under tension perform better. For example, sensors on the upper back have a higher success rate than the sensors on the lower back. The dance leotard design was found to capture the movements of standing lean back and standing waist twists the best.

This paper explores the process by which designers come to terms with an unfamiliar and ambiguous sensor material. Drawing on craft practice and material-driven interaction design, we developed a simple yet flexible sensor technology based on the movement of conductive elements within a magnetic field. Variations in materials and structure give rise to objects which produce a complex time-varying signal in response to physical interaction. Sonifying the signal yields nuanced and intuitive action-sound correspondences which nonetheless defy easy categorisation in terms of conventional types of sensors. We reflect on a craft-based exploration of the material by one of the authors, then report on two workshops with groups of designers of varying background. Through examining the objects produced and the experience of the participants, we explore the tension between tacit and explicit understanding of unfamiliar materials and the ways that material thinking can create new design opportunities.

Historically, women have been excluded from engineering and computer science disciplines, and interactive audio is no exception. Relatively few women are involved with the designing and building of embedded audio systems with traditional tools such as microprocessors, but when embedded audio systems are built using e-textiles, much larger proportions of women become engaged with technology. In this paper we review theories for this gender disparity and the barriers women face in working with audio technology, and then present a comparison of survey data between an e-textile audio workshop and an audio platform user group. Extrapolating from the case study and the surveyed literature, we propose that flexibility in learning, communal dissemination of knowledge, and gendering of tools are prominent reasons why women engage with technology via e-textiles.

Body posture is a good indicator of, amongst other things, people's state of arousal, focus of attention and level of interest in a conversation. Posture is conventionally measured by observation and hand coding of videos or, more recently, through automated computer vision and motion capture techniques. Here we introduce a novel alternative approach exploiting a new modality: posture classification using bespoke 'smart' trousers with integrated textile pressure sensors. Changes in posture translate to changes in pressure patterns across the surface of our clothing. We describe the construction of the textile pressure sensor and, using simple machine learning techniques on data gathered from 10 participants, demonstrate its ability to discriminate between 19 different basic posture types with high accuracy. This technology has the potential to support anonymous, unintrusive sensing of interest, attention and engagement in a wide variety of settings.

This paper presents initial steps towards the design of an embedded system for body-centric sonic performance. The proposed prototyping system allows performers to manipulate sounds through gestural interactions captured by textile wearable sensors. The e-textile sensor data control, in real-time, audio synthesis algorithms working with content from Audio Commons, a novel web-based ecosystem for re-purposing crowd-sourced audio. The system enables creative embodied music interactions by combining seamless physical e-textiles with web-based digital audio technologies.

Music collections are often visualized in a two-dimensional space to show relationships between songs. Some user inter faces interacting with these two-dimensional maps of songs use spatial auditory display to allow easier access to the au dio content. A common auditory display is to have multiple songs playing simultaneously from differing spatial locations around the user. However, when using this style of interface the sonification of the collection needs to be limited to a lo cal subset of the collection, usually three to six songs. This paper presents the amblr, a spatial audio music browser that allows a user to auralize the collection. It combines effective design from previous work with new approaches to create a novel interface. This allows for a more intuitive navigation of a virtual space populated by a large collection songs without relying on textual metadata.

This paper introduces a new database of room impulse responses. This database differs greatly from previously released databases as it contains over 700 impulse responses. The impulse responses are measured in three different rooms each with a static source position and at least 130 different receiver positions. Each measurement position is recorded with both an omnidirectional microphone and a B-format microphone.